Sensor system and calibration device for calibrating analyte sensor

Abstract

To provide systems and methods for processing sensor data and self-calibration.SOLUTION: Systems and methods are provided which are capable of calibrating a continuous analyte sensor based on an initial sensitivity, and then continuously performing self-calibration without using, or with reduced use of, reference measurements. In certain embodiments, a sensitivity (110) of the analyte sensor is determined by applying an estimative algorithm (120) that is a function of certain parameters. A sensor property can be used to compensate sensor data for sensitivity drift, or determine another property associated with the sensor, such as temperature, sensor membrane damage, moisture ingress in sensor electronics, and scaling factors.SELECTED DRAWING: Figure 1A

Description

Technical Field

[0001] Cross-reference to Related Applications This application claims the benefit of U.S. Provisional Patent Application No. 61 / 476,145, filed on Apr. 15, 2011. The above application is hereby incorporated by reference in its entirety and is clearly a part of this specification.

[0002] Embodiments described herein generally relate to systems and methods for processing sensor data from continuous body sensors and for automatic calibration.

Background Art

[0003] Diabetes mellitus is a chronic disease that occurs when the pancreas does not produce enough insulin (Type I), or when the body cannot effectively use the insulin produced by the pancreas (Type II). This condition typically leads to an increase in blood glucose concentration (hyperglycemia), which can cause a number of physiological disorders associated with the deterioration of small blood vessels (e.g., kidney failure, skin ulcers, or bleeding within the vitreous of the eye). Sometimes, hypoglycemic reactions (hypoglycemia) are induced by the inadvertent over-administration of insulin, or after the normal administration of insulin or glucose-lowering agents associated with strenuous exercise or inadequate food intake.

[0004] Various sensor devices have been developed to continuously measure blood glucose levels. Conventionally, people with diabetes carry self-monitoring blood glucose (SMBG) monitors, which typically involve an uncomfortable finger prick method. Due to the lack of comfort and convenience, diabetic patients often measure their glucose levels 2 to 4 times a day. Unfortunately, these measurements can be too widely spaced, causing diabetic patients to sometimes learn about hypoglycemic or hyperglycemic episodes too late, potentially leading to dangerous side effects. In fact, not only are diabetic patients less likely to perform SMBG measurements in a timely manner, but even if a diabetic patient can obtain SMBG values in a timely manner, based solely on SMBG, the diabetic patient cannot know whether their blood glucose level is increasing or decreasing.

Prior Art Documents

Patent Documents

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Patent Document 1

Patent Document 2

Patent Document 3

Patent Document 4

Patent Document 5

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Patent Document 7

Patent Document 8

Patent Document 9

Summary of the Invention

Problems to be Solved by the Invention

[0006] So far, various glucose sensors have been developed to continuously measure glucose levels. Many implantable glucose sensors suffer from complications inside the body and provide short-term and inaccurate blood glucose measurements. Similarly, transdermal sensors have suffered from problems of accurate detection and continuous reporting of glucose levels over long periods. Some efforts have been made to obtain blood glucose data from implantable devices and determine the trend of blood glucose retroactively for analysis, but these efforts do not help diabetic patients in determining blood glucose information in real time. Some efforts have also been made to obtain blood glucose data from transdermal devices for future data analysis, but similar problems have occurred.

Means for Solving the Problems

[0007] In a first aspect, a method for calibrating sensor data generated by a continuous specimen sensor, the method comprising: generating sensor data using a continuous specimen sensor; repeatedly determining, by an electronic device, a sensitivity value of the continuous specimen sensor as a function of time by applying empirical information including sensor sensitivity information; and calibrating the sensor data based at least in part on the determined sensitivity value.

[0008] In one embodiment or other embodiments of the first aspect, calibrating the sensor data is performed repeatedly over substantially the entire sensor session.

[0009] In one embodiment or other embodiments of the first aspect, repeatedly determining the sensitivity value is performed periodically or at irregular intervals as determined by the empirical information.

[0010] In one embodiment or other embodiments of the first aspect, repeatedly determining the sensitivity value is performed substantially over the entire sensor session.

[0011] In one embodiment or other embodiments of the first aspect, determining the sensitivity value is performed substantially in real time.

[0012] In one embodiment or other embodiments of the first aspect, the prior information is related to at least one predetermined sensitivity value related to a predetermined time after the sensor session starts.

[0013] In one embodiment or other embodiments of the first aspect, at least one predetermined sensitivity value is related to the correlation between the sensitivity determined from the in vitro analyte concentration measurement and the sensitivity determined from the in vivo analyte concentration measurement at a predetermined time.

[0014] In one embodiment or other embodiments of the first aspect, the prior information is related to a predetermined sensitivity function that uses time as an input.

[0015] In one embodiment or other embodiments of the first aspect, time corresponds to the time after the sensor session starts.

[0016] In one embodiment or other embodiments of the first aspect, time corresponds to at least one of the time of manufacture or the time since manufacture.

[0017] In one embodiment or other embodiments of the first aspect, the sensitivity value of the continuous analyte sensor is also a function of at least one other parameter.

[0018] In one embodiment or other embodiments of the first aspect, at least one other parameter is selected from the group consisting of temperature, pH, level or duration of hydration, curing conditions, analyte concentration of the fluid surrounding the continuous analyte sensor during start of operation of the sensor, and combinations thereof.

[0019] In one embodiment or other embodiments of the first aspect, calibrating the sensor data is performed without using reference blood glucose data.

[0020] In one embodiment or other embodiments of the first aspect, the electronic device is configured to provide an accuracy level corresponding to an average absolute relative difference of no more than about 10% over at least about three days of a sensor session, and the reference measurement associated with the calculation of the average absolute relative difference is determined by analysis of blood.

[0021] In one embodiment or other embodiments of the first aspect, the sensor session is at least about four days.

[0022] In one embodiment or other embodiments of the first aspect, the sensor session is at least about five days.

[0023] In one embodiment or other embodiments of the first aspect, the sensor session is at least about six days.

[0024] In one embodiment or other embodiments of the first aspect, the sensor session is at least about seven days.

[0025] In one embodiment or other embodiments of the first aspect, the sensor session is at least about ten days.

[0026] In one embodiment or other embodiments of the first aspect, the average absolute relative difference does not exceed about 7% over the sensor session.

[0027] In one embodiment or other embodiments of the first aspect, the average absolute relative difference does not exceed about 5% throughout the sensor session.

[0028] In one embodiment or other embodiments of the first aspect, the average absolute relative difference does not exceed about 3% throughout the sensor session.

[0029] In one embodiment or other embodiments of the first aspect, the prior information is related to the calibration code.

[0030] In one embodiment or other embodiments of the first aspect, the prior sensitivity information is stored in the sensor electronics before using the sensor.

[0031] In the second aspect, a system for implementing the method of the first aspect or any of its embodiments is provided.

[0032] In the third aspect, a method for calibrating sensor data generated by a continuous sample sensor is provided, including generating sensor data using a continuous sample sensor, determining, by an electronic device, a plurality of different sensitivity values of the continuous sample sensor as a function of time and a function of sensitivity information related to prior information, and calibrating the sensor data based at least in part on at least one of the plurality of different sensitivity values.

[0033] In one embodiment or other embodiments of the third aspect, calibrating the continuous sample sensor is performed iteratively substantially throughout the entire sensor session.

[0034] In one embodiment or other embodiments of the third aspect, the plurality of different sensitivity values are stored in a look-up table in a computer memory.

[0035] In one embodiment or other embodiments of the third aspect, determining the plurality of different sensitivity values is performed only once throughout the entire sensor session.

[0036] In one embodiment or other embodiments of the third aspect, the prior information is related to at least one predetermined sensitivity value related to a predetermined time after the sensor session has started.

[0037] In one embodiment or other embodiments of the third aspect, the at least one predetermined sensitivity value is related to the correlation between the sensitivity determined from in vitro analyte concentration measurements and the sensitivity determined from in vivo analyte concentration measurements at a predetermined time.

[0038] In one embodiment or other embodiments of the third aspect, the prior information is related to a predetermined sensitivity function that uses time as an input.

[0039] In one embodiment or other embodiments of the third aspect, time corresponds to the time after the sensor session has started.

[0040] In one embodiment or other embodiments of the third aspect, time corresponds to the time of manufacture or the time since manufacture.

[0041] In one embodiment or other embodiments of the third aspect, the plurality of sensitivity values are also a function of at least one parameter other than time.

[0042] In one embodiment or other embodiments of the third aspect, the at least one other parameter is selected from the group consisting of temperature, pH, level or duration of hydration, curing conditions, analyte concentration of the fluid surrounding the continuous analyte sensor when starting the operation of the sensor, and combinations thereof.

[0043] In one embodiment or other embodiments of the third aspect, calibrating the continuous analyte sensor is performed without using reference blood glucose data.

[0044] In one embodiment or other embodiments of the third aspect, the electronic device is configured to provide an accuracy level corresponding to an average absolute relative difference of no more than about 10% over a sensor session of at least about three days, and the reference measurement associated with the calculation of the average absolute relative difference is determined by blood analysis.

[0045] In one embodiment or other embodiments of the third aspect, the sensor session is at least about four days.

[0046] In one embodiment or other embodiments of the third aspect, the sensor session is at least about five days.

[0047] In one embodiment or other embodiments of the third aspect, the sensor session is at least about six days.

[0048] In one embodiment or other embodiments of the third aspect, the sensor session is at least about seven days.

[0049] In one embodiment or other embodiments of the third aspect, the sensor session is at least about ten days.

[0050] In one embodiment or other embodiments of the third aspect, the average absolute relative difference does not exceed about 7% over the sensor session.

[0051] In one embodiment or other embodiments of the third aspect, the average absolute relative difference does not exceed about 5% over the sensor session.

[0052] In one embodiment or other embodiments of the third aspect, the average absolute relative difference does not exceed about 3% throughout the sensor session.

[0053] In one embodiment or other embodiments of the third aspect, the prior information is related to a calibration code.

[0054] In the fourth aspect, a system for implementing the method of the third aspect or any of its embodiments is provided.

[0055] In the fifth aspect, a method for processing data from a continuous sample sensor is provided. The method includes receiving, by an electronic device, sensor data from a continuous sample sensor including at least one sensor data point; iteratively determining a sensitivity value of the continuous sample sensor as a function of time and as a function of at least one predetermined sensitivity value associated with a predetermined time after starting a sensor session; forming a conversion function based at least in part on the sensitivity value; and determining a sample output value by applying the conversion function to the at least one sensor data point.

[0056] In one embodiment or other embodiments of the fifth aspect, iteratively determining the sensitivity of the continuous sample sensor is performed continuously.

[0057] In one embodiment or other embodiments of the fifth aspect, iteratively determining the sensitivity is performed substantially in real time.

[0058] In one embodiment or other embodiments of the fifth aspect, the method further includes determining a baseline of the continuous sample sensor and the conversion function being based at least in part on the baseline.

[0059] In one embodiment or other embodiments of the fifth aspect, determining the baseline of the continuous sample sensor is performed continuously.

[0060] In one embodiment or other embodiments of the fifth aspect, determining the sensitivity of the continuous analyte sensor and determining the baseline of the analyte sensor are performed substantially simultaneously.

[0061] In embodiments of the fifth aspect or any other embodiments thereof, the at least one predetermined sensitivity value is set at the manufacturing facility of the continuous analyte sensor.

[0062] In one embodiment or other embodiments of the fifth aspect, the method further includes receiving at least one calibration code and applying the at least one calibration code to the electronic device at a predetermined time after the sensor session has started.

[0063] In one embodiment or other embodiments of the fifth aspect, determining the sensitivity iteratively is performed periodically or at irregular intervals determined by the at least one calibration code.

[0064] In one embodiment or other embodiments of the fifth aspect, the at least one calibration code is related to at least one predetermined sensitivity.

[0065] In one embodiment or other embodiments of the fifth aspect, the at least one calibration code is related to a predetermined sensitivity function that uses the time of a time function as an input.

[0066] In one embodiment or other embodiments of the fifth aspect, the time corresponds to the time after the sensor session has started.

[0067] In one embodiment or other embodiments of the fifth aspect, the time corresponds to the time of manufacture or the time since manufacture.

[0068] In one embodiment or other embodiments of the fifth aspect, the sensitivity value of the continuous analyte sensor is also a function of at least one other parameter.

[0069] In one embodiment or other embodiments of the fifth aspect, the at least one other parameter is selected from the group consisting of temperature, pH, level or duration of hydration, curing conditions, analyte concentration of the fluid surrounding the continuous analyte sensor during start-up of the sensor, and combinations thereof.

[0070] In the sixth aspect, there is provided a system for implementing the method of the fifth aspect or any of its embodiments.

[0071] In the seventh aspect, there is provided a method for calibrating a continuous analyte sensor, comprising receiving sensor data from the continuous analyte sensor, forming or receiving a predetermined sensitivity profile corresponding to a change in sensor sensitivity to an analyte over substantially the entire sensor session, which is a function of at least one predetermined sensitivity value associated with a predetermined time after the start of the sensor session, and applying the sensitivity profile to an electronic device in real-time calibration.

[0072] In one embodiment or other embodiments of the seventh aspect, at least one predetermined sensitivity value, the predetermined sensitivity profile, or both are set at the manufacturing facility of the continuous analyte sensor.

[0073] In one embodiment or other embodiments of the seventh aspect, the method includes receiving at least one calibration code and applying the at least one calibration code to an electronic device at a predetermined time after the start of the sensor session.

[0074] In one embodiment or other embodiments of the seventh aspect, the at least one calibration code is related to at least one predetermined sensitivity.

[0075] In one embodiment or other embodiments of the seventh aspect, the at least one calibration code is related to a predetermined sensitivity function that uses time as an input.

[0076] In one embodiment or other embodiments of the seventh aspect, the sensitivity profile is a function of time.

[0077] In one embodiment or other embodiments of the seventh aspect, time corresponds to the time after the sensor session is started.

[0078] In one embodiment or other embodiments of the seventh aspect, time corresponds to the time of manufacture or the time since manufacture.

[0079] In one embodiment or other embodiments of the seventh aspect, the sensitivity value is a function of at least one parameter selected from the group consisting of a function of time, a predetermined sensitivity value, and temperature, pH, level or duration of hydration, curing conditions, analyte concentration of the fluid surrounding the continuous analyte sensor during activation of the sensor, and combinations thereof.

[0080] In the eighth aspect, a system for implementing the method of the seventh aspect or any of its embodiments is provided.

[0081] In the ninth aspect, a method of processing data from a continuous analyte sensor is provided, the method including receiving, by an electronic device, sensor data from a continuous analyte sensor including at least one sensor data point, receiving or forming a predetermined sensitivity profile corresponding to a change in sensor sensitivity over substantially the entire sensor session, forming a conversion function based at least in part on the sensitivity profile, and determining an analyte output value by applying the conversion function to the at least one sensor data point.

[0082] In one embodiment or other embodiments of the ninth aspect, the sensitivity profile is set at the manufacturing facility of the continuous analyte sensor.

[0083] In one embodiment or other embodiments of the ninth aspect, the method includes receiving at least one calibration code and applying at least one calibration code to the electronic device at a predetermined time after the sensor session has started.

[0084] In one embodiment or other embodiments of the ninth aspect, at least one calibration code is associated with at least one predetermined sensitivity.

[0085] In one embodiment or other embodiments of the ninth aspect, the at least one calibration code is associated with a sensitivity profile.

[0086] In one embodiment or other embodiments of the ninth aspect, the sensitivity profile is a function of time.

[0087] In one embodiment or other embodiments of the ninth aspect, time corresponds to the time after the sensor session has started.

[0088] In one embodiment or other embodiments of the ninth aspect, time corresponds to the time of manufacture or the time since manufacture.

[0089] In one embodiment or other embodiments of the ninth aspect, the sensitivity is a function of time and at least one parameter is selected from the group consisting of temperature, pH, level or duration of hydration, curing conditions, analyte concentration of the fluid surrounding the continuous analyte sensor during start of operation of the sensor, and combinations thereof.

[0090] In the tenth aspect, a system for implementing the method of the ninth aspect or any of its embodiments is provided.

[0091] In an eleventh aspect, there is provided a system for monitoring a recipient's analyte concentration, the system comprising a continuous analyte sensor configured to measure the recipient's analyte concentration and provide factory-calibrated sensor data, the continuous analyte sensor being calibrated without using reference blood glucose data, the system being configured to provide an accuracy level corresponding to an average absolute relative difference of no more than about 10% over at least about three days of sensor session, and the reference measurement associated with the calculation of the average absolute relative difference being determined by analysis of blood.

[0092] In one embodiment or other embodiments of the eleventh aspect, the sensor session is at least about four days.

[0093] In one embodiment or other embodiments of the eleventh aspect, the sensor session is at least about five days.

[0094] In one embodiment or other embodiments of the eleventh aspect, the sensor session is at least about six days.

[0095] In one embodiment or other embodiments of the eleventh aspect, the sensor session is at least about seven days.

[0096] In one embodiment or other embodiments of the eleventh aspect, the sensor session is at least about ten days.

[0097] In one embodiment or other embodiments of the eleventh aspect, the average absolute relative difference does not exceed about 7% over the sensor session.

[0098] In one embodiment or other embodiments of the eleventh aspect, the average absolute relative difference does not exceed about 5% over the sensor session.

[0099] In one embodiment or other embodiments of the eleventh aspect, the average absolute relative difference does not exceed about 3% throughout the sensor session.

[0100] In the twelfth aspect, there is provided a method for determining the characteristics of a continuous specimen sensor, the method including applying a bias voltage to the specimen sensor, applying a voltage step greater than the bias voltage to the specimen sensor, using sensor electronics to measure the signal response to the voltage step, using sensor electronics to determine the peak current of the signal response, and determining the characteristics of the sensor by associating the peak current with the predetermined relationship.

[0101] In one embodiment or other embodiments of the twelfth aspect, associating the peak current with the predetermined relationship includes calculating the impedance of the sensor based on the peak current and associating the sensor impedance with the predetermined relationship.

[0102] In one embodiment or other embodiments of the twelfth aspect, the characteristics of the sensor are the sensitivity of the sensor or the temperature of the sensor.

[0103] In one embodiment or other embodiments of the twelfth aspect, the peak current is the difference between the magnitude of the response before the voltage step and the magnitude of the most greatly measured response resulting from the voltage step.

[0104] In one embodiment or other embodiments of the twelfth aspect, the predetermined relationship is the relationship between impedance and sensor sensitivity, and the characteristic of the sensor is the sensitivity of the sensor.

[0105] In one embodiment or other embodiments of the twelfth aspect, the method further includes compensating sensor data using the determined characteristics of the sensor.

[0106] In one embodiment or other embodiments of the twelfth aspect, said compensating includes associating a predetermined relationship of said peak current with sensor sensitivity or a change in sensor sensitivity, and modifying the value of sensor data according to the associated sensor sensitivity or change in sensor sensitivity.

[0107] In one embodiment or other embodiments of the twelfth aspect, said predetermined relationship is a linear relationship over the time of using said analyte sensor.

[0108] In one embodiment or other embodiments of the twelfth aspect, said predetermined relationship is a non-linear relationship over the time of using said analyte sensor.

[0109] In one embodiment or other embodiments of the twelfth aspect, said predetermined relationship is determined by pre-testing a sensor similar to the analyte sensor.

[0110] In one embodiment or other embodiments of the thirteenth aspect, said sensor system comprises instructions stored in a computer memory, which, when executed by one or more processors of said sensor system, cause said sensor system to implement the method of the twelfth aspect or any of its embodiments.

[0111] In the fourteenth aspect, there is provided a method for calibrating an analyte sensor, which includes applying a time-varying signal to said analyte sensor, measuring a signal response to the applied signal, using sensor electronics to determine the sensitivity of the analyte sensor, said determining including associating at least one characteristic of said signal response with a predetermined sensor sensitivity profile, and using the determined sensitivity and an analyte concentration value estimated using sensor data generated by said analyte sensor to generate, using sensor electronics.

[0112] In one embodiment or other embodiments of the 14th aspect, the sensitivity profile comprises sensitivity values that change over the period since the sensor was implanted.

[0113] In one embodiment or other embodiments of the 14th aspect, the predetermined sensitivity profile includes a plurality of sensitivity values.

[0114] In one embodiment or other embodiments of the 14th aspect, the predetermined sensitivity profile is based on sensor sensitivity data generated from investigating the sensitivity change of a specimen sensor similar to the specimen sensor.

[0115] In one embodiment or other embodiments of the 14th aspect, the method includes applying a bias voltage to the sensor, and the time-varying signal includes a step voltage exceeding the bias voltage or a sine wave voltage overlapping the bias voltage.

[0116] In one embodiment or other embodiments of the 14th aspect, the determining further includes calculating an impedance value based on the measured signal response and associating the impedance value with the sensitivity value of a predetermined sensitivity profile.

[0117] In one embodiment or other embodiments of the 14th aspect, the method further includes applying a DC bias voltage to the sensor to generate sensor data, and estimating the specimen concentration value includes generating corrected sensor data using the determined sensitivity.

[0118] In one embodiment or other embodiments of the 14th aspect, the method further includes applying a conversion function to the corrected sensor data to generate the estimated specimen concentration value.

[0119] In one embodiment or other embodiments of the 14th aspect, the method further includes forming a conversion function based on at least a portion of the determined sensitivity, and the conversion function is applied to sensor data to generate an estimated analyte concentration value.

[0120] In one embodiment or other embodiments of the 14th aspect, the characteristic is the peak current value of the signal response.

[0121] In one embodiment or other embodiments of the 14th aspect, the determining further includes at least one use of performing a fast Fourier transform on the signal response data, incorporating at least a portion of the curve of the signal response, and determining the peak current of the signal response.

[0122] In one embodiment or other embodiments of the 14th aspect, the determining further includes selecting a predetermined sensitivity profile based on sensor characteristics determined from a plurality of different predetermined sensitivity profiles.

[0123] In one embodiment or other embodiments of the 14th aspect, the selection includes performing a data correlation analysis to determine a correlation between the determined sensor characteristics and each of the plurality of different predetermined sensitivity profiles, and the selected predetermined sensitivity profile has the highest correlation.

[0124] In one embodiment or other embodiments of the 14th aspect, the method further includes generating an estimated analyte concentration value using the selected sensitivity profile.

[0125] In one embodiment or other embodiments of the 14th aspect, the method further includes determining a second sensitivity value using the selected sensitivity profile, wherein a first set of estimated analyte concentration values is generated using the determined sensitivity value and sensor data associated with a first time period, and a second set of concentration values is generated using the second sensitivity value and sensor data associated with a second time period.

[0126] In the 15th aspect, there is provided a sensor system for implementing the method of the 14th aspect or any of its embodiments.

[0127] In one embodiment or other embodiments of the 15th aspect, the sensor system comprises instructions stored in a computer memory, which when executed by one or more processors of the sensor system, cause the sensor system to perform the method of the 14th aspect or any of its embodiments.

[0128] In the 16th aspect, there is provided a method for determining whether the analyte sensor system is functioning properly, the method including applying a stimulus signal to the analyte sensor, measuring a response to the stimulus signal, estimating a value of a sensor characteristic based on the signal response, correlating the sensor characteristic value with a predetermined relationship of the sensor characteristic and a predetermined sensor sensitivity profile, and initiating an error routine if the correlation does not exceed a predetermined correlation threshold.

[0129] In one embodiment or other embodiments of the 16th aspect, the correlating includes performing a data correlation analysis.

[0130] In one embodiment or other embodiments of the 16th aspect, the error routine includes displaying a message to the user indicating that the analyte sensor is not functioning properly.

[0131] In one embodiment or other embodiments of the 16th aspect, the sensor characteristic is the impedance of the sensor film.

[0132] In the 17th aspect, there is provided a sensor system configured to implement the method of the 16th aspect or any of its embodiments.

[0133] In one embodiment or other embodiments of the 17th aspect, the sensor system comprises instructions stored in a computer memory, which, when executed by one or more processors of the sensor system, cause the sensor system to implement the method of the 16th aspect or any of its embodiments.

[0134] In the 18th aspect, there is provided a method for determining the temperature associated with a continuous analyte sensor, the method comprising applying a stimulation signal to the analyte sensor, measuring a signal response of the signal, and determining the temperature associated with the analyte sensor, wherein the determining comprises correlating at least one characteristic of the signal response to a predetermined relationship of the sensor characteristic to temperature.

[0135] In one embodiment or other embodiments of the 18th aspect, the method further comprises generating an analyte concentration value estimated using the determined temperature and sensor data generated from the analyte sensor.

[0136] In one embodiment or other embodiments of the 18th aspect, the generating comprises compensating the sensor data using the determined temperature and converting the compensated sensor data to an estimated analyte value to be generated using a conversion function.

[0137] In one embodiment or other embodiments of the 18th aspect, said generating includes forming or modifying a conversion function using a determined temperature, and converting said sensor data into an estimated analyte value to be generated using said formed or modified conversion function.

[0138] In one embodiment or other embodiments of the 18th aspect, said method includes measuring temperature using a second sensor, and said determining further includes determining the temperature associated with said analyte sensor using the measured temperature.

[0139] In one embodiment or other embodiments of the 18th aspect, said second sensor is a thermistor.

[0140] In the 19th aspect, there is provided a system configured to implement the method of the 18th aspect or any of its embodiments.

[0141] In one embodiment or other embodiments of the 19th aspect, said sensor system comprises instructions stored in a computer memory, which when executed by one or more processors of said sensor system, cause said sensor system to implement the method of the 18th aspect or any of its embodiments.

[0142] In the 20th aspect, there is provided a method for determining moisture ingress into an electronic sensor system, including applying a stimulus signal having a specific frequency or a signal including a frequency spectrum to an analyte sensor, measuring a response to said stimulus signal, using sensor electronics to calculate an impedance based on said measured signal response, using sensor electronics to determine whether said impedance falls within a predefined level corresponding to moisture ingress, and using sensor electronics to initiate an error routine when said impedance exceeds one or both of the predefined levels.

[0143] In one embodiment or other embodiments of the 20th aspect, the method further includes an error routine that activates one or more audible alarms and visual alarms on a display screen to warn the user that the sensor system may not be functioning properly.

[0144] In one embodiment or other embodiments of the 20th aspect, the stimulation signal has a predetermined frequency.

[0145] In one embodiment or other embodiments of the 20th aspect, the stimulation signal has a frequency spectrum.

[0146] In one embodiment or other embodiments of the 20th aspect, the calculated impedance has a magnitude value and a phase value, and the determination includes comparing the magnitude value of the impedance with a predefined impedance magnitude threshold and the phase value with a predefined phase threshold.

[0147] In one embodiment or other embodiments of the 20th aspect, the calculated impedance is a complex impedance value.

[0148] In the 21st aspect, a sensor system for implementing the method of the 20th aspect or any of its embodiments is provided.

[0149] In one embodiment or other embodiments of the 21st aspect, the sensor system comprises instructions stored in a computer memory, which, when executed by one or more processors of the sensor system, cause the sensor system to implement the method of the 20th aspect or any of its embodiments.

[0150] In a 22nd aspect, a method for determining membrane damage of an analyte sensor using a sensor system, the method comprising: applying a stimulation signal to the analyte sensor; measuring a response to the stimulation signal; calculating an impedance based on the signal response using sensor electronics; determining, using the sensor electronics, whether the impedance falls within a predefined level corresponding to membrane damage; and starting an error routine using the sensor electronics if the impedance exceeds the predefined level.

[0151] In one or other embodiments of the 22nd aspect, the error routine includes activating one or more audible alarms and visual alarms on a display screen.

[0152] In one or other embodiments of the 22nd aspect, the stimulation signal has a predefined frequency.

[0153] In one or other embodiments of the 22nd aspect, the stimulation signal has a frequency spectrum.

[0154] In one or other embodiments of the 22nd aspect, the calculated impedance includes a magnitude value and a phase value, and the determination includes comparing the magnitude value of the impedance with a predefined impedance magnitude threshold and the phase value with a predefined phase threshold.

[0155] In one or other embodiments of the 22nd aspect, the calculated impedance is a complex impedance value.

[0156] In a 23rd aspect, a sensor system for implementing the method of the 22nd aspect or any of its embodiments is provided.

[0157] In one embodiment or other embodiments of the 23rd aspect, the sensor system comprises instructions stored in a computer memory, which, when executed by one or more processors of the sensor system, cause the sensor system to implement the method of the 22nd aspect or any of its embodiments.

[0158] In the 24th aspect, there is provided a method for determining the reusability of a sample sensor, the method including applying a stimulation signal to the sample sensor, measuring a response to the stimulation signal, calculating an impedance response based on the response, comparing the calculated impedance with a predetermined threshold value, and initiating a sensor reusability routine if it is determined that the impedance exceeds the threshold value.

[0159] In one embodiment or other embodiments of the 24th aspect, the sensor reusability routine includes activating an audible and / or visual alarm to inform the user of inappropriate reuse of the sensor.

[0160] In one embodiment or other embodiments of the 24th aspect, the routine for reusing the sensor includes shutting down the sensor system completely or partially and / or stopping the display of sensor data on the user interface of the sensor system.

[0161] In the 25th aspect, there is provided a sensor system configured to implement the method of the 24th aspect or any of its embodiments.

[0162] In one embodiment or other embodiments of the 25th aspect, the sensor system comprises instructions stored in a computer memory, which, when executed by one or more processors of the sensor system, cause the sensor system to implement the method of the 24th aspect or any of its embodiments.

[0163] In a 26th aspect, there is provided a system for determining the reusability of a sample sensor, the system including applying a stimulation signal to the sample sensor, measuring a response to the stimulation signal, calculating an impedance based on the response, determining a correlation between the impedance calculated using a data-related function and one or more recorded impedance values, and initiating a sensor reusability routine when it is determined that the correlation exceeds a predetermined threshold value.

[0164] In one embodiment or other embodiments of the 26th aspect, the sensor reusability routine includes activating an audible and / or visual alarm to inform the user of an inappropriate reuse of the sensor.

[0165] In one embodiment or other embodiments of the 26th aspect, the routine for reusing the sensor includes completely or partially shutting down the sensor system and / or stopping the display of sensor data on the user interface of the sensor system.

[0166] In one embodiment or other embodiments of the 26th aspect, the sensor system comprises instructions stored in a computer memory, which when executed by one or more processors of the sensor system, cause the sensor system to perform the method of the 25th aspect.

[0167] In a 27th aspect, there is provided a method of applying an overpotential to a sample sensor, the method including applying a stimulation signal to the sample sensor, measuring a response to the stimulation signal, determining a sensitivity of the sensor or a change in the sensitivity of the sensor based on the response, and applying an overpotential to the sensor based on the determined sensitivity or change in sensitivity.

[0168] In one embodiment or other embodiments of the 27th aspect, said determining further includes calculating an impedance based on a response and determining a sensitivity or a change in sensitivity based on said impedance.

[0169] In one embodiment or other embodiments of the 27th aspect, said determining a sensitivity or a change in sensitivity further includes associating an impedance with respect to a predetermined impedance with a sensitivity relationship.

[0170] In one embodiment or other embodiments of the 27th aspect, said applying includes determining or correcting a total time during which an overpotential is applied to the sensor.

[0171] In one embodiment or other embodiments of the 27th aspect, said applying includes determining or correcting a magnitude of the overpotential applied to the sensor.

[0172] In the 28th aspect, there is provided a sensor system configured to implement the method of the 27th aspect or any of its embodiments.

[0173] In one embodiment or other embodiments of the 28th aspect, said sensor system comprises instructions stored in a computer memory, which, when executed by one or more processors of said sensor system, cause said sensor system to implement the method of the 27th aspect or any of its embodiments.

[0174] In the 29th aspect, there is provided a method for determining characteristics of a continuous specimen sensor, including applying a stimulation signal to a first specimen sensor having a first working electrode and a first reference electrode, measuring a signal response of said stimulation signal using a second specimen sensor having a second working electrode and a second reference electrode, and determining characteristics of said first sensor by associating said response with a predetermined relationship.

[0175] In one embodiment or other embodiments of the 29th aspect, the method further includes applying a bias voltage to a first working electrode to generate sensor data and measuring a response to the bias voltage.

[0176] In one embodiment or other embodiments of the 29th aspect, the method further includes calibrating the sensor data using the determined characteristic.

[0177] In one embodiment or other embodiments of the 29th aspect, the determined characteristic is one of impedance and temperature.

[0178] In one embodiment or other embodiments of the 29th aspect, the method further includes determining damage to the sensor film using the determined characteristic.

[0179] In one embodiment or other embodiments of the 29th aspect, the method further includes determining moisture intrusion into a sensor system surrounding first and second analyte sensors using the determined characteristic.

[0180] In the 30th aspect, a sensor system configured to implement a method of one of the 29th aspect or any of its embodiments is provided.

[0181] In one embodiment or other embodiments of the 30th aspect, the sensor system comprises instructions stored in a computer memory, which when executed by one or more processors of the sensor system, cause the sensor system to implement the method of the 29th aspect or any of its embodiments.

[0182] In a 31st aspect, there is provided a method for determining scaling used in a continuous analyte sensor system, the method including applying a first stimulation signal to a first working electrode of an analyte sensor, measuring a response to the first stimulation signal, applying a second stimulation signal to a second working electrode of the analyte sensor, measuring a response to the second stimulation signal, using sensor electronics to determine a scaling factor based on the measured responses to the first and second stimulation signals, and using the scaling factor to generate an analyte value estimated based on sensor data generated by the analyte sensor.

[0183] In one or other embodiments of the 31st aspect, the method further includes the method being performed periodically.

[0184] In one or other embodiments of the 31st aspect, the determining includes calculating a first impedance using the response to the first stimulation signal and calculating a second impedance using the response to the second stimulation signal, the scaling factor being the ratio of the first impedance to the second impedance.

[0185] In one or other embodiments of the 31st aspect, the first working electrode has a thin film including an enzyme configured to react with the analyte and the second working electrode has a thin film not including the enzyme.

[0186] In one or other embodiments of the 31st aspect, determining the scaling factor includes updating a previous scaling factor based on the measured responses to the first and second stimulation signals.

[0187] In one embodiment or other embodiments of the 31st aspect, the scaling factor is the scaling factor of acetaminophen, and the method includes updating a further scaling factor based on the scaling factor of the acetaminophen, and the further scaling factor is applied to sensor data to generate an estimated analyte value.

[0188] In the 32nd aspect, a sensor system configured to implement the method of the 31st aspect or any of its embodiments is provided.

[0189] In one embodiment or other embodiments of the 32nd aspect, the sensor system includes instructions stored in a computer memory, and when the instructions are executed by one or more processors of the sensor system, the method of the 31st aspect or any of its embodiments is implemented by the sensor system.

[0190] In the 33rd aspect, a method for calibrating an analyte sensor is provided, including applying a predetermined signal to the analyte sensor, measuring a response to the applied signal, using sensor electronics to determine a change in impedance associated with a thin film of the analyte sensor based on the measured response, calculating a change in sensitivity of the analyte sensor based on the determined impedance, calculating a corrected sensitivity based on the calculated change in sensitivity and a previously used sensitivity of the analyte sensor, and generating an estimated analyte value using the corrected sensitivity.

[0191] In one embodiment or other embodiments of the 33rd aspect, calculating the change in sensitivity includes applying a non-linear compensation function.

[0192] In one embodiment or other embodiments of the 33rd aspect, the non-linear compensation function is represented by the following equation. △S=(a*log(t)+b)*△I Here, △S is the change in sensitivity, t is the time since the analyte sensor was calibrated, △I is the determined change in impedance, and a and b are predetermined coefficients.

[0193] In one embodiment or other embodiments of the 33rd aspect, a and b are determined by previously testing similar analyte sensors.

[0194] In the 34th aspect, a sensor system configured to implement the method of the 33rd aspect or any of its embodiments is provided.

[0195] In one embodiment or other embodiments of the 34th aspect, the sensor system comprises instructions stored in a computer memory, which, when executed by one or more processors of the sensor system, cause the sensor system to implement the method of the 33rd aspect or any of its embodiments.

[0196] In the 35th aspect, a method for calibrating an analyte sensor is provided, including generating sensor data using a subcutaneous analyte sensor, forming or modifying a conversion function by using pre-implantation information, internal diagnostic information, and / or external reference information as inputs, and calibrating the sensor data using the conversion function.

[0197] In one embodiment or other embodiments of the 35th aspect, the information before implantation includes information selected from the group consisting of a predetermined sensitivity profile associated with the analyte sensor, a predetermined relationship between the measured sensor attributes and sensor sensitivity, one or more predetermined relationships between the measured sensor attributes and sensor temperature, sensor data obtained from a previously used analyte sensor, a calibration code associated with the analyte sensor, a patient-specific relationship between the analyte sensor and one or more of sensitivity, baseline, drift, and impedance, information representing the implantation site of the sensor, the time since the manufacture of the analyte sensor, and information representing the analyte exposed to temperature or humidity.

[0198] In one embodiment or other embodiments of the 35th aspect, the internal diagnostic information includes information selected from the group consisting of the output of a stimulation signal, sensor data representing the analyte concentration measured by the sensor, temperature measurement using the sensor or a separate sensor, sensor data generated by a redundant sensor designed substantially the same as the analyte sensor, sensor data generated by an auxiliary sensor having a different mode from the analyte sensor, the time since the sensor was implanted or since it was connected to the sensor electronics connected to the sensor, data generated by a pressure sensor representing the pressure on the sensor or the sensor system, data generated by an accelerometer, criteria for moisture ingress, and noise criteria for the analyte concentration signal.

[0199] In one embodiment or other embodiments of the 35th aspect, the reference information includes information selected from the group consisting of real-time and / or previous analyte concentration information obtained from a reference monitor, information regarding the type / brand of the reference monitor used to provide the reference data, information regarding the amount of carbohydrates consumed by the user, information received from a drug delivery device, glucagon sensitivity information, and information collected from population-based data.

[0200] In a 36th aspect, there is provided a sensor system configured to carry out the method of the 35th aspect or any of its embodiments.

[0201] In one embodiment or other embodiments of the 36th aspect, the sensor system comprises instructions stored in a computer memory, which when executed by one or more processors of the sensor system, cause the sensor system to carry out the method of the 35th aspect or any of its embodiments.

[0202] These and other features and advantages will be better understood by reference to the following detailed description when considered in conjunction with the accompanying drawings.

Brief Description of the Drawings

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DETAILED DESCRIPTION OF THE INVENTION

[0204] Definitions For ease of understanding the embodiments described in this specification, many terms are defined as follows.

[0205] As used herein, the term "analyte" is a broad term and is given its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, substances or chemical components in biological fluids that can be analyzed (e.g., blood, interstitial fluid, cerebrospinal fluid, lymph fluid, or urine). Analytes can include naturally occurring substances, artificial substances, metabolites, and / or reaction products. In some embodiments, the analyte for measurement by the sensor heads, devices, and methods disclosed herein is glucose. However, other analytes are also contemplated, including carboxyprothrombin; acylcarnitine; adenine phosphoribosyltransferase; adenosine deaminase; albumin; alpha-fetoprotein; amino acid profile (arginine (Krebs cycle), histidine / urocanic acid, homocysteine, phenylalanine / tyrosine, tryptophan); androstenedione; antipyrine; arabinitol enantiomer; arginase; benzoylecgonine (cocaine); biotinidase; biopterin; C-reactive protein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1β-hydroxycholic acid; cortisol; creatine kinase; creatine kinase MM isoenzyme; cyclosporin A; d-penicillamine; deethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylation polymorphism, alcohol dehydrogenase, alpha1-antitrypsin, cystic fibrosis, Duchenne / Becker muscular dystrophy, analyte-6-phosphate dehydrogenase, hemoglobinopathy, hemoglobin A disease, hemoglobin S disease, hemoglobin C disease, hemoglobin E disease, D Punjab type, beta thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1, Leber hereditary optic neuropathy, medium-chain acyl-CoA dehydrogenase (MCAD), ribonucleic acid (RNA), phenylketonuria (PKU), Plasmodium malariae, sex differentiation, 21-deoxycortisol); desbutylhalofantrine; dihydropteridine reductase; diphtheria / tetanus antitoxin;Erythrocyte arginase; Erythrocyte protoporphyrin; Esterase D; Fatty acid / acylglycine; Free β-human chorionic gonadotropin; Free erythrocyte porphyrin; Free thyroxine (FT4); Free triiodothyronine (FT3); Fumarylacetoacetase; Galactose / gal-1-phosphate; Galactose-1-phosphate uridyltransferase; Gentamicin; Specimen-6-phosphate dehydrogenase; Glutathione; Glutathione peroxidase; Glycocholic acid; Glycosylated hemoglobin; Halofantrine; Hemoglobin variant; Hexosaminidase A; Human erythrocyte carbonic anhydrase I; 17α-hydroxyprogesterone; Hypoxanthine phosphoribosyltransferase; Immunoreactive trypsin; Lactic acid; Lead; Lipoproteins ((a), B / A-1, β); Lysozyme; Mefloquine; Netilmicin; Phenobarbital; Phenytoin; Phytanic acid / pristanic acid; Progesterone; Luteinizing hormone; Prolidase; Purine nucleoside phosphorylase inhibitor; Quinene; Reverse triiodothyronine (rT3); Selenium; Serum pancreatic lipase; Sisomicin; Somatomedin C; Specific antibodies (adenovirus, antinuclear antibody, anti-Zeta antibody, arbovirus, Aujeszky's disease virus, dengue fever virus, Medina worm, Echinococcus granulosus, Entamoeba histolytica, Enterovirus genus, Giardia lamblia, Helicobacter pylori, Hepatitis B virus, Herpes virus, HIV-1, IgE (atopic disease), Influenza virus, Donovan Leishmania, Leptospira, Mumps / epidemic parotitis / rubella, Mycoplasma pneumoniae, Myoglobin, Spirometra mansoni, Parainfluenza virus, Plasmodium falciparum, Poliovirus, Pseudomonas aeruginosa, RS virus, Rickettsia (scrub typhus), Schistosoma mansoni, Toxoplasma, Treponema pallidum, Trypanosoma cruzi / Langerhansia, Vesicular stomatitis virus, Wuchereria bancrofti, Yellow fever virus); Specific antigens (Hepatitis B virus, HIV-1); Succinylacetone; Sulfadoxine; Theophylline; Thyroid-stimulating hormone (TSH); Thyroxine (T4); Thyroxine-binding globulin; Trace elements; Transferrin; UDP-galactose-4-epimerase; Urea; Uroporphyrinogen I synthase; Vitamin A; Leukocytes;and include, but are not limited to, zinc protoporphyrin. Salts, carbohydrates, proteins, fats, vitamins and hormones that occur naturally in the blood, or interstitial fluid may also, in certain embodiments, constitute the sample. The sample can be, for example, metabolites, hormones, antigens, antibodies, etc., that occur naturally in biological fluids. Alternatively, the sample can be introduced into the body, for example, contrast agents for imaging, radioisotopes, chemical agents, fluorocarbon-based synthetic blood, or the following drugs or pharmaceutical compositions, but are not limited to: insulin; ethanol; cannabis (marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorinated hydrocarbons, hydrocarbons); cocaine (crack cocaine); stimulants (amphetamine, methamphetamine, Ritalin, Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine); depressants (barbiturate hypnotics, methaqualone, tranquilizers (barium, Librium, Miltown, Serax, meprobamate, Tranxene); hallucinogens (fenciclovir, lysergic acid, mescaline, peyote, psilocybin); narcotics (heroin, codeine, morphine, opium, meperidine, Percocet, Percodan, Tussionex, fentanyl, Darvon, Talwin, Lomotil); synthetic antibiotics (fentanyl, meperidine, amphetamine, methamphetamine, and fenciclovir, for example, ecstasy analogs); muscle enhancers; and nicotine. Also, metabolites of drugs and pharmaceutical compositions are expected samples. Neurochemicals and other chemicals produced in the body, such as ascorbic acid, uric acid, dopamine, norepinephrine, 3-methoxythyramine (3MT), 3,4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), 5-hydroxytryptamine (5HT), and 5-hydroxyindoleacetic acid (FHIAA) and other samples are also analyzed.;

[0206] The terms "continuous analyte sensor" and "continuous glucose sensor" as used herein are broad terms and are given their ordinary and customary meaning to those of ordinary skill in the art (and are not limited to a special or customized meaning), and refer to, but are not limited to, devices that continuously or continuously measure analyte / glucose concentration and / or calibrate, for example, from a number to the right of the decimal point of seconds up to a maximum of, for example, 1, 2 or 5 minutes, or at longer time intervals (e.g., by continuously or continuously adjusting or determining the sensitivity and background of the sensor).

[0207] The term "biological sample" as used herein is a broad term and is given its ordinary and customary meaning to those of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, samples derived from tissues of the body or recipient, such as, for example, blood, interstitial fluid, cerebrospinal fluid, saliva, urine, tears, sweat or other similar fluids.

[0208] The term "host" as used herein is a broad term and is given its ordinary and customary meaning to those of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, animals including humans.

[0209] As used herein, the term "membrane system" is a broad term and is given its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning), and can be composed of two or more regions and is typically composed of a material several microns thick or thicker, and refers to a permeable or semi-permeable membrane that is permeable to oxygen and selectively permeable to glucose, but is not limited thereto. In one embodiment, this membrane system includes an immobilized glucose oxidase enzyme that enables the occurrence of an electrochemical reaction for measuring glucose concentration.

[0210] As used herein, the term "domain" is a broad term and is given its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning), and refers to a region of a membrane that can be a layer of a functional aspect of a substance with a uniform or non-uniform gradient (e.g., anisotropic), or a region given as a part of the membrane, but is not limited thereto.

[0211] As used herein, the term "sensing region" is a broad term and is given its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning), and refers to a region of a monitoring device that is responsible for detecting a specific analyte, but is not limited thereto. In one embodiment, this sensing region generally comprises a non-conductive body, at least one electrode, a reference electrode and a selective counter electrode that penetrate the body and are fixed within the body to form an electroactive surface at one position of the body and an electrical connection at another position of the body, and a membrane system fixed to the body to cover the electroactive surface.

[0212] The term "electroactive surface" as used in this specification is a broad term and is given its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, the electrode surface where an electrochemical reaction occurs. In one embodiment, the working electrode measures hydrogen peroxide (H2O2) that generates a measurable current.

[0213] The term "baseline" as used in this specification is a broad term and is given its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, the component of the analyte sensor signal that is not related to the analyte concentration. In one example of a glucose sensor, this baseline consists of the contribution of signals from factors other than substantially glucose (e.g., interfering chemical species, non-reaction-related hydrogen peroxide, or other electroactive chemical species having an oxidation potential overlapping with that of hydrogen peroxide). In some embodiments where calibration is determined by solving the equation y = mx + b, the value of b represents the baseline of the signal. In certain embodiments, the value of this b (i.e., the baseline) can be 0 or approximately 0. This can be the result of, for example, an electrode with baseline subtraction or a low bias potential setting. As a result, in these embodiments, calibration can be determined by solving the equation y = mx.

[0214] As used herein, the term "inactive enzyme" is a broad term and is given its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, an enzyme (e.g., glucose oxidase, GOx) that has been inactivated (e.g., by denaturation of the enzyme) and substantially has no enzyme activity. Enzymes can be inactivated using various techniques known in the prior art, such as heating, freeze-thaw, organic solvents, denaturation in acids or bases, crosslinking, genetic modification of amino acids important for the enzyme, etc., but are not limited thereto. In some embodiments, after adding a solution containing an active enzyme to the sensor, the added enzyme can be substantially inactivated by heating or treatment with an inactive solvent.

[0215] As used herein, the term "non-enzymatic" is a broad term and is given its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, the absence of enzyme activity. In some embodiments, the "non-enzymatic" membrane portion does not contain an enzyme, while in other embodiments, the "non-enzymatic" membrane portion contains an inactive enzyme. In some embodiments, an enzyme solution containing an inactive enzyme or no enzyme is added.

[0216] As used herein, the term "substantially" is a broad term and is given its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, being most but not necessarily all of what is specified.

[0217] As used herein, the term "about" is a broad term and is given its ordinary and customary meaning to those of ordinary skill in the art (and is not limited to a special or customized meaning), and when related to any numerical value or range, refers to, but is not limited to, the understanding that the amount or condition by which the term varies can vary somewhat beyond a given amount as long as the disclosed function is achieved.

[0218] As used herein, the term "ROM" is a broad term and is given its ordinary and customary meaning to those of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, a read-only memory which is a type of data storage device manufactured with a fixed capacity. ROM includes, for example, electrically erasable programmable read-only memory (ROM), i.e., EEPROM.

[0219] As used herein, the term "RAM" is a broad term and is given its ordinary and customary meaning to those of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, a data storage device in which access commands to different locations do not affect the access speed. RAM is broad enough to include, for example, static random access memory (SRAM) which holds data bits in its memory as long as power is supplied.

[0220] As used herein, the term "A / D converter" is a broad term and is given its ordinary and customary meaning to those of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, hardware and / or software that converts an analog electrical signal into a corresponding digital signal.

[0221] As used herein, the terms "raw data stream" and "data stream" are broad terms and are given their ordinary and customary meaning to those skilled in the art (and are not limited to a special or customized meaning), and refer to, but are not limited to, analog or digital signals directly related to the analyte concentration measured by an analyte sensor. In one example, the raw data stream is digital data of counts converted by an A / D converter from an analog signal (e.g., voltage or amperage) representing the analyte concentration. The term broadly encompasses multiple time-spaced data points, including single measurements obtained from a substantially continuous analyte sensor at sub-second fractions up to, for example, 1, 2, or 5 minutes, or longer time intervals.

[0222] As used herein, the term "counts" is a broad term and is given its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, the unit of measurement of a digital signal. In one example, the raw data stream measured in counts is directly related to the voltage (e.g., converted by an A / D converter) directly related to the current from the working electrode.

[0223] As used herein, the term "sensor electronics" is a broad term and is given its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, elements of a device configured to process data (e.g., hardware or software). In the case of an analyte sensor, the data includes biological information regarding the analyte concentration in the biological fluid obtained by the sensor. Patent Document 1, Patent Document 2, and Patent Document 3 describe suitable electronic circuits available for certain embodiments of the device.

[0224] As used herein, the term "potentiostat" is a broad term and is given its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, an electrical system that applies a set amount of potential between the working electrode and the reference electrode of a two- or three-electrode cell and measures the current flowing through the working electrode. The potentiostat forces the current required to flow between the working electrode and the counter electrode to maintain the desired potential, provided that the required cell voltage and current do not exceed the corresponding range of the potentiostat.

[0225] As used herein, the term "operably connected" is a broad term and is given its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, one or more components that are connected to other components so as to enable the transmission of signals therebetween. For example, one or more electrodes can be used to detect the amount of glucose in a sample and convert that information into a signal, which can then be transmitted to an electronic circuit. In this case, the electrode is "operably connected" to the electronic circuit. These terms are broad enough to include both wired and wireless connections.

[0226] As used herein, the term "filtering" is a broad term and is given its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, modifying a set of data to make it smoother and more continuous and removing or reducing outlying points, for example, by performing a moving average of a raw data stream.

[0227] As used herein, the term "algorithm" is a broad term and is given its ordinary and customary meaning to those of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, a computational process (e.g., a program) that uses computer processing and is required to convert information from one state to another.

[0228] As used herein, the term "calibration" is a broad term and is given its ordinary and customary meaning to those of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, a process for determining the scale of a sensor that results in a quantitative measurement (e.g., analyte concentration). As an example, calibration can be updated or readjusted over time to compensate for changes associated with the sensor, such as changes in the sensitivity of the sensor and the background of the sensor. In addition, calibration of the sensor can include automatic and self-calibration without using, for example, a reference analyte value after the time of use.

[0229] As used herein, the term "sensor data" is a broad term and is given its ordinary and customary meaning to those of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, data received from a continuous analyte sensor that includes sensor data points separated by one or more time intervals.

[0230] The terms "reference analyte values" and "reference data" as used herein are broad terms and are given their ordinary and customary meaning to those of ordinary skill in the art (and are not limited to special or customized meanings), and refer to reference data from a reference analyte monitor such as a blood glucose meter or the like, including one or more reference data points, but are not limited thereto. In some embodiments, the reference glucose values are obtained from self-monitored blood glucose (SMBG) tests (e.g., finger or forearm blood tests), or from, for example, YSI (Yellow Springs Instrument) tests.

[0231] The terms "interferents" and "interfering species" as used herein are broad terms and are given their ordinary and customary meaning to those of ordinary skill in the art (and are not limited to special or customized meanings), and refer to effects or species that interfere with the measurement of the analyte of interest in the sensor and produce signals that do not accurately represent the analyte measurement value, but are not limited thereto. In one example of an electrochemical sensor, an interfering species is a compound that has an oxidation potential that overlaps with the analyte being measured and produces a false positive signal.

[0232] The term "sensor session" as used herein is a broad term and is given its ordinary and customary meaning to those of ordinary skill in the art (and is not limited to special or customized meanings), and refers to the period during which the sensor is applied (e.g., implanted) to a recipient or is used to obtain sensor values, but is not limited thereto. For example, in some embodiments, the sensor session extends from when the sensor is implanted (including, for example, inserting the sensor into the subcutaneous tissue and positioning the sensor to be in fluid communication with the recipient's circulatory system) until the sensor is removed.

[0233] The terms "sensitivity" or "sensor sensitivity" as used in this specification are broad terms and are given their ordinary and customary meanings to those skilled in the art (and are not limited to special or customized meanings), and refer to, but are not limited to, the amount of signal generated by a chemical species (e.g., H2O2) measured in relation to a certain concentration of the analyte being measured, or the analyte being measured (e.g., glucose). For example, in one embodiment, the sensor has a sensitivity of about 1 to about 300 picoamperes of current per 1 mg / dL of glucose analyte.

[0234] The terms "sensitivity profile" or "sensitivity curve" as used in this specification are broad terms and are given their ordinary and customary meanings to those skilled in the art (and are not limited to special or customized meanings), and refer to, but are not limited to, the representation of the change in sensitivity over a period of time.

[0235] Summary Conventional in vivo continuous analyte sensing techniques typically rely on reference measurements performed during a sensor session to calibrate the continuous analyte sensor. This reference measurement is combined with sensor data that substantially corresponds to time, resulting in a combined data pair. Subsequently, regression is performed (e.g., using least squares regression) on the combined data pair to generate a conversion function that defines the relationship between the sensor signal and the estimated glucose concentration.

[0236] In a critical care setting, calibration of a continuous analyte sensor is often performed by using a calibration solution of known analyte concentration as a reference. This calibration technique can be cumbersome. This is because a calibration bag is typically used that is separated from (and added to) the drip (venous) bag. In an ambulatory setting, calibration of a continuous analyte sensor has traditionally been performed by capillary blood glucose measurement (e.g., finger stick glucose test), whereby reference data is obtained and input into the continuous analyte sensor system. This calibration technique typically involves frequent finger stick measurements, which are inconvenient and can be painful.

[0237] Heretofore, systems and methods for in vitro calibration of continuous analyte sensors by manufacturers (e.g., factory calibration) have not relied on periodic recalibration and have been inadequate in most cases with respect to high levels of sensor accuracy. Changes in sensor characteristics (e.g., sensor sensitivity) that occur during use of the sensor can be thought to be in this part. As a result, calibration of a continuous analyte sensor typically requires periodic input of reference data, regardless of whether it is related to a calibration solution or a finger stick measurement. This can be a significant burden for patients in an ambulatory setting or for hospital staff in a critical care setting.

[0238] The continuous analyte sensors described herein can achieve continuous automatic self-calibration and high levels of accuracy during a sensor session without (or while reducing) reliance on reference data from a reference analyte monitor (e.g., a blood glucose meter). In some embodiments, the continuous analyte sensor is an invasive, minimally invasive, or non-invasive device. The continuous analyte sensor can be a subcutaneous, transdermal, or intravascular device. In one embodiment, one or more of these devices can form a continuous analyte sensor system. For example, the continuous analyte sensor system can include a combination of a subcutaneous device and a transdermal device, a combination of a subcutaneous device and an intravascular device, a combination of a transdermal device and an intravascular device, or a combination of a subcutaneous device, a transdermal device, and an intravascular device. In some embodiments, the continuous analyte sensor can analyze multiple intermittent biological samples (e.g., blood samples). The continuous analyte sensor can use any glucose measurement method, including methods involving enzymatic, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, electrophoretic, and radioanalytical mechanisms, among others.

[0239] In one embodiment, the continuous analyte sensor includes one or more working electrodes and one or more reference electrodes that operate together to measure a signal related to the analyte concentration of the recipient. The output signal from the working electrode is typically a raw data stream that is used to generate a calibrated, processed, and estimated analyte (e.g., glucose) concentration. In one embodiment, the continuous analyte sensor can measure additional signals related to the baseline and / or sensitivity of the sensor, thereby enabling additional monitoring of the baseline and / or changes in sensitivity or drift that occur in the continuous analyte sensor over time.

[0240] In some embodiments, the sensor extends through the housing, which maintains the sensor on the skin and provides an electrical connection to the sensor electronics. In one embodiment, the sensor is formed from a wire. For example, the sensor can include an elongated conductive body, such as a bare elongated conductive core (e.g., a metal wire), or an elongated conductive core coated with one, two, three, four, five or more material layers, each of which may or may not be conductive. This elongated sensor can be long and thin, yet flexible and strong. For example, in some embodiments, the minimum dimension of the elongated conductive body is less than about 0.1 inches, 0.075 inches, 0.05 inches, 0.025 inches, 0.01 inches, 0.004 inches or 0.002 inches. Other embodiments of the elongated conductive body are disclosed in Patent Document 4, which is hereby incorporated by reference in its entirety. Preferably, the membrane system adheres to at least a portion of the electroactive surface of the sensor 102 (including the working electrode and optionally the reference electrode) to protect the exposed electrode surface from the biological environment, provide diffusion resistance (limitation) of the analyte as needed, enable the reaction of the enzyme, limit or block the interferent, and / or provide hydrophilicity to the electrochemical reaction surface of the sensor interface. The disclosure regarding different membrane systems that can be used in the embodiments described herein is described in Patent Document 5, which is hereby incorporated by reference in its entirety.

[0241] Calibration of sensor data from a continuous analyte sensor generally involves determining the relationship between the measurements made by the sensor (e.g., in units of nA or digital counts after A / D conversion) and one or more reference measurements (e.g., in units of mg / dL or mmol / L). In certain embodiments, one or more reference measurements taken immediately after the analyte sensor is manufactured and before the sensor is used are used for calibration. These reference measurements can be obtained in a number of forms. For example, in certain embodiments, the reference measurement can be determined from the ratio or correlation between the sensitivity of the sensor for in vivo analyte concentration measurements over a period of time (e.g., from a certain sensor lot) and the sensitivity of other sensors (e.g., from the same lot manufactured in substantially the same manner under substantially the same conditions) for in vitro analyte concentration measurements. By providing a continuous analyte sensor with a predetermined in vivo to in vitro ratio and a predetermined sensitivity profile (as described in more detail elsewhere in this specification), self-calibration of the sensor can be achieved in relation to a high level of sensor accuracy.

[0242] Self-calibration can eliminate or at least reduce the need for recalibration using reference data during a sensor session and can require recalibration only in certain limited situations, such as when a sensor failure is detected. In addition or alternatively, in some embodiments, a continuous analyte sensor can be configured to request and accept one or more reference measurements (e.g., from a fingerstick glucose measurement or a calibration solution) at the start of a sensor session. In some embodiments, using a reference measurement value related to a predetermined sensor sensitivity profile at the start of a sensor session can eliminate or substantially reduce the need for further reference measurement values.

[0243] For certain implantable enzyme-based electrochemical glucose sensors, the detection mechanism depends on certain phenomena that are generally linearly related to glucose concentration. For example, (1) the diffusion of the analyte through a membrane system located between the implantation site (e.g., subcutaneous space) and the electroactive surface, (2) the rate of the enzymatic reaction of the analyte that generates the chemical species to be measured inside the membrane system (e.g., the rate of the glucose oxidase-catalyzed reaction of glucose with O2 to produce gluconic acid and H2O2), and (3) the diffusion of the chemical species to be measured (e.g., H2O2) to the electroactive surface. Due to this generally linear relationship, the calibration of the sensor is obtained by solving the equation: y = mx + b where y represents the sensor signal (count), x represents the estimated glucose concentration (mg / dL), m represents the sensor sensitivity to the analyte concentration (count / mg / dL), and b represents the baseline signal (count). As described in other parts of this specification, in certain embodiments, the value of b (i.e., the baseline) can be set to 0 or approximately 0. As a result, for these embodiments, the calibration can be determined by solving the equation y = mx.

[0244] In some embodiments, the continuous analyte sensor system is configured to estimate changes or drifts in sensor sensitivity over the course of a sensor session as a function of time (e.g., elapsed time since the start of the sensor session). As described in other parts of this specification, this sensitivity function plotted against time can resemble a curve. Additionally or alternatively, the system can be configured to determine changes or drifts in sensor sensitivity as a function of time and one or more other parameters that can affect sensor sensitivity, or to provide additional information regarding sensor sensitivity. These parameters can affect sensor sensitivity or provide additional information regarding sensor sensitivity, such as parameters related to the manufacture of the sensor (e.g., materials used to manufacture the sensor membrane, thickness of the sensor membrane, temperature at which the sensor membrane was cured, total time the sensor was immersed in a particular coating solution, etc.). In one embodiment, some of the parameters include information that can cause a calibration code associated with a particular sensor lot obtained prior to the sensor session. Other parameters can be related to the conditions surrounding the sensor after manufacture but prior to the sensor session, such as, for example, the level of exposure of the sensor to a certain level of humidity, or the temperature while the sensor was in a package during transit from the manufacturing facility to the patient. Still other parameters (e.g., permeability of the sensor membrane, temperature at the location of the sample, pH at the location of the sample, oxygen level at the location of the sample, etc.) can also affect sensor sensitivity or provide additional information regarding sensor sensitivity during the sensor session.

[0245] Determination of sensor sensitivity at different points in time of a sensor session based on a predefined sensor sensitivity profile can be performed before or when starting the sensor session. Additionally, in some embodiments, the determination of sensor sensitivity based on the sensor sensitivity profile can be continuously adjusted to compensate for parameters that affect sensor sensitivity or provide additional information regarding sensor sensitivity during the sensor session. These determinations of changes or drifts in sensor sensitivity can be used to provide self-calibration, update calibration, supplement calibration based on measurements (e.g., from a reference analyte monitor), and / or confirm or reject reference analyte measurements from a reference analyte monitor. In some embodiments, the confirmation or rejection of reference analyte measurements can be based on whether the reference analyte measurement is within a range of values associated with a predefined sensor sensitivity profile.

[0246] Some of the continuous analyte sensors described herein can be configured to measure signals related to the non-analyte constants of a recipient. Preferably, the non-analyte constant signal is measured under a membrane system on the sensor. In one example of a continuous glucose sensor, a measurable non-glucose constant is oxygen. In some embodiments, changes in oxygen transport, which represent changes or drifts in the sensitivity of the glucose signal, can be measured by switching the working electrode, an auxiliary electrode that measures oxygen, an oxygen sensor, or other bias potential.

[0247] In addition, some of the continuous analyte sensors described herein can be configured to measure changes in the amount of background noise in the signal. Detection of a change exceeding a certain threshold can provide a basis for initiating calibration, updating calibration, and / or verifying or rejecting inaccurate reference analyte values from a reference analyte monitor. In one example of a continuous glucose sensor, background noise includes signal contributions from factors other than substantially glucose (e.g., interfering species, non-reaction-related hydrogen peroxide, or other electroactive chemical species having an oxidation potential overlapping with hydrogen peroxide). That is, the continuous glucose sensor is configured to measure a signal related to a baseline (including substantially all of the non-glucose related current generated) measured by the sensor in the recipient. In some embodiments, an auxiliary electrode located under the non-enzymatic portion of the membrane system is used to measure the baseline signal. To obtain a signal that is completely or substantially completely related to the glucose concentration, the baseline signal can be subtracted from the glucose + baseline signal. This subtraction can be achieved electronically in the sensor using a differential amplifier, digitally in the receiver, and / or in the hardware or software of the sensor or receiver as described in more detail elsewhere herein.

[0248] Together, by determining sensor sensitivity based on a sensitivity profile and by measuring a baseline signal, the continuous analyte sensor can continuously self-calibrate during a sensor session, eliminating (or reducing) dependence on reference measurements from a reference analyte monitor or calibration solution.

[0249] Determination of Sensor Sensitivity As described elsewhere herein, in certain embodiments, self-calibration of the analyte sensor system can be performed by determining sensor sensitivity based on a sensitivity profile (and a measured or estimated baseline) such that the following equation can be solved. y = mx + b Here, y represents the sensor signal (count), x represents the estimated glucose concentration (mg / dL), m represents the sensor sensitivity to the sample (count / mg / dL), and b represents the baseline signal (count). From this equation, a conversion function can be formed, whereby the sensor signal is converted into the estimated glucose concentration.

[0250] What has been found here is that the sensitivity of the sensor to the sample concentration during a sensor session often changes or drifts as a function of time. FIG. 1A illustrates this phenomenon and gives a chart of the sensor sensitivity 110 as a function of time during the sensor sessions of a group of continuous glucose sensors. FIG. 1B gives a chart of the conversion function at three different periods of the sensor session. As shown in FIG. 1B, the three conversion functions have different slopes, each corresponding to a different sensor sensitivity. Thus, the difference in slope over time indicates that a change or drift in sensor sensitivity occurs over the sensor session.

[0251] Referring once again to the study related to FIG. 1A, the sensors were manufactured in substantially the same way under substantially the same conditions. The sensor sensitivity related to the y-axis of the chart is represented as a percentage of the substantially stable state sensitivity reached at about 3 days after the start of the sensor session. In addition, these sensor sensitivities correspond to the measurements obtained from the YSI test. As shown in the chart, the sensitivity of each measured sensor (represented as a percentage of the steady-state sensitivity) is very close to the sensitivity of the other sensors in the group at any given point in the sensor session. Without wishing to be limited by theory, the upward trend observed for the sensitivity, which is particularly evident in the early part of the sensor session, can be considered to be due to the conditioning and hydration of the detection area of the working electrode. The glucose concentration of the fluid surrounding the continuous glucose sensor during the start of the sensor operation is also thought to affect the drift of the sensitivity.

[0252] For the sensors tested in this study, the change in the sensor's sensitivity (expressed as a percentage relative to the sensitivity in a substantially stable state) over the course of time defined by a sensor session resembles a logarithmic growth curve. It should be understood here that other continuous specimen sensors manufactured with different technologies, different specifications (e.g., different film thicknesses or compositions), or under different manufacturing conditions may exhibit different sensor sensitivity profiles (e.g., one is related to a linear function). Nevertheless, a high level of reproducibility has been achieved through improved control of the operating conditions of the sensor manufacturing process such that the sensitivity profiles exhibited by the individual sensors in a sensor population (e.g., a sensor lot) are substantially similar and sometimes nearly identical.

[0253] What has been discovered here is that not only is the change or drift in sensor sensitivity over a sensor session substantially consistent among sensors manufactured in substantially the same way under substantially the same conditions, but it is also possible to perform modeling with a mathematical function that can accurately estimate this change or drift. As shown in FIG. 1A, an evaluation algorithm function 120 can be used to define the relationship between time and sensor sensitivity during a sensor session. This evaluation algorithm function can be generated by testing a sample set (including one or more sensors) from a sensor lot under in-vivo and / or in-vitro conditions. Alternatively, this evaluation algorithm function can be generated by testing each sensor under in-vivo and / or in-vitro conditions.

[0254] In some embodiments, the sensor can undergo an in vitro sensor sensitivity drift test. In this case, the sensor is exposed to a change in state (e.g., a step change in glucose concentration in a solution), and a sensitivity profile of the sensor over a period of time in vitro is generated. The duration of the test can be matched to the entire sensor session of the corresponding in vivo sensor, or can include a portion of the sensor session (e.g., the first day, the first two days, or the first three days of the sensor session, etc.). What can be considered here is to perform the above test on individual sensors or on one or more sample sensors of a sensor lot. From this test, an in vitro sensitivity profile can be generated, and then an in vivo sensitivity profile can be modeled and / or formed.

[0255] From this in vivo or in vitro test, one or more data sets each containing data points related to sensitivity over time can be generated and plotted. The sensitivity profile or curve can then be fitted to the data points. If it is determined that the curve fit is satisfactory (e.g., the standard deviation of the generated data points is less than a certain threshold), it can be determined that the sensor sensitivity profile or curve passes quality control and is suitable for release. From there, the sensor sensitivity profile can be converted into an evaluation algorithm function or a look-up table. The algorithm function or look-up table can be stored in a computer-readable memory and accessed, for example, by a computer processor.

[0256] The evaluation algorithm function can be formed by applying a curve fitting technique that retrofits a curve to data points by adjusting the function (e.g., by adjusting the constants of the function) until an optimal fit for the available data points is obtained. Put simply, a "curve" (i.e., a function, sometimes called a "model") that relates one data value to one or more other data is fitted and generated, and the parameters of the curve are selected such that the curve estimates the relationship of the data values. By way of example, the selection of the parameters of the curve can include the selection of the coefficients of a polynomial function. In some embodiments, the curve fitting process can include an evaluation of how closely the curve determined in the curve fitting process estimates the relationship between the data values in order to determine the optimal fit. As used herein, the term "curve" is a broad term and is given its ordinary and customary meaning to those of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to a function, or the graph of a function that can include a rounded curve or a straight curve (i.e., a line).

[0257] This curve can be formed by any of a variety of curve fitting techniques such as the linear least squares fitting method, the non-linear least squares fitting method, the Nelder-Mead simplex method, the Levenberg-Marquardt method, and variations thereof. Additionally, this curve can be fitted using any of a variety of functions including, but not limited to, linear functions (including constant functions), logarithmic functions, quadratic functions, cubic functions, square root functions, power functions, polynomial functions, rational functions, exponential functions, sinusoidal functions, and variations and combinations thereof. For example, in some embodiments, the evaluation algorithm includes a linear function element with a first weight w1, a logarithmic function element with a second weight w2, and an exponential function element with a third weight w3. In further embodiments, the weights associated with each element can vary as a function of time and / or other parameters, although in alternative embodiments, one or more of these weights are constants as a function of time.

[0258] In certain embodiments, the correlation (e.g., R2 value) of an evaluation algorithm function for data obtained from a sample sensor is a measure of the curve fitting quality for data points, but can be used as one measure criterion for determining whether the function is optimal. In certain embodiments, the evaluation algorithm function formed from curve fitting analysis can be adjusted to compensate for other parameters, such as sensor sensitivity, or other parameters that can affect sensor sensitivity or provide additional information regarding sensor sensitivity. For example, the evaluation algorithm function can be adjusted to compensate for the sensitivity of the sensor to hydrogen peroxide or other chemical species.

[0259] The evaluation algorithm formed and used to accurately estimate the sensitivity of individual sensors can be based on factory calibration and / or a single early reference measurement (e.g., using a single point blood glucose monitor) at any time during the sensor session. In some embodiments, all sensors in a population of continuous analyte sensors manufactured in substantially the same way under substantially the same conditions exhibit a substantially fixed relationship of in vivo to ex vivo sensitivity. For example, in one embodiment, the in vivo sensitivity of a sensor at a certain time after starting sensor use (e.g., t = 5, 10, 15, 30, 60, 120, or 180 minutes after sensor use) is consistently equal to the measured ex vivo sensitivity of that sensor or an equivalent sensor. From this relationship, an initial value of in vivo sensitivity can be generated, and then a function of the algorithm corresponding to the sensor sensitivity profile can be formed. In other words, from this initial value (representing one point of the sensor sensitivity profile), the entire remainder of the sensor sensitivity profile can be determined and plotted. The initial value of in vivo sensitivity can be associated with any part of the sensor sensitivity profile. In certain embodiments, multiple initial values corresponding to multiple ex vivo sensitivities at time intervals of in vivo sensitivity can be calculated and combined together to generate a sensor sensitivity profile.

[0260] In some embodiments, as shown in FIG. 2A, the initial value 210 of the in-vivo sensitivity is related to the time corresponding to the start (close to the start) of the sensor session. As shown in FIG. 2B, based on this initial value 210, the remaining sensor sensitivity profile 220 is plotted (i.e., plotted back and forth across the entire x-axis corresponding to time). However, as shown in FIG. 2C, in some embodiments, the initial value 210' can be related to any other time of the sensor session. For example, as shown in FIG. 2C, in one embodiment, the initial value 210' of the in-vivo sensitivity is related to the time when the sensitivity reaches a substantially steady state (e.g., about 3 days). From the initial value 210', the remaining sensor sensitivity profile 220' is plotted as shown in FIG. 2D.

[0261] In other embodiments, the relationship of the in-vivo sensitivity to the ex-vivo was not equal, but nevertheless the relationship included a consistent fixed ratio. By making the relationship of the in-vivo sensitivity to the ex-vivo substantially constant, some of the sensors described herein evaluate the ex-vivo sensitivity characteristics of the sensors from a particular sensor lot (e.g., one or more sensitivity values measured over a period of time) at the manufacturing facility and determine the in-vivo sensitivity characteristics of other sensors in the same sensor lot based on the relationship to the measured ex-vivo sensitivity characteristics, and store the calculated in-vivo sensitivity characteristics on the electronics associated with the sensor (e.g., the computer memory of the sensor electronics configured to be connected to the sensor in an operable state while the sensor is being used, as discussed in other parts of this specification), so that factory calibration can be performed.

[0262] Accordingly, high-level sensor accuracy factory calibration is achieved based on information regarding the relationship of in-vivo to ex-vivo sensor sensitivity obtained prior to the sensor session and a predetermined sensor sensitivity profile. For example, in some embodiments, the sensor was able to achieve an accuracy corresponding to an average absolute relative difference of no more than about 10% over a sensor session of at least about 3 days, sometimes at least about 4, 5, 6, 7 or 10 days. In some embodiments, the sensor was able to achieve an accuracy corresponding to an average absolute relative difference of no more than about 7%, about 5% or about 3% over a sensor session of at least about 3 days. Factory calibration can eliminate the need for recalibration or only be required in certain situations such as in response to detection of sensor failure.

[0263] Referring back to the study associated with FIG. 1A, the sensor is manufactured having a working electrode configured to measure a glucose + baseline signal and a corresponding auxiliary electrode configured to measure only the baseline signal. The sensor electronics of the sensor system subtract the baseline signal from the glucose + baseline signal to obtain a signal that is fully or substantially fully related to the glucose concentration. Additionally, an algorithmic function is generated and stored in the sensor electronics associated with the sensor to estimate the sensitivity of those sensors over the useful life of those sensors. This algorithmic function is plotted in FIG. 1A and is shown to lie closely over the measured sensor sensitivity of the sensor. A conversion function is formed by determining the baseline and sensitivity at any given time during the life of the sensor, whereby the sensor signal is converted to the glucose concentration that is estimated.

[0264] As shown in FIG. 3A, which is a Bland-Altman plot showing the differences between YSI reference measurements and an in vivo continuous analyte sensor calibrated at the factory, the measurements from these sensors show very high accuracy. The lines in FIG. 3A represent criteria for accuracy corresponding to deviations from the actual measurements (using the YSI test) of less than ±10 mg / dL at glucose concentrations of about 40 - 75 mg / dL and less than ±15% at glucose concentrations between about 75 mg / dL and 400 mg / dL. In fact, the difference between the estimated glucose concentration values calculated using a predefined sensor sensitivity profile and the actual measurements (using the YSI test) over the sensor lifetime does not exceed about 10 mg / dL at glucose concentrations between about 40 - 75 mg / dL and does not exceed 15% at glucose concentrations between about 75 mg / dL and 400 mg / dL. Further, at glucose concentrations between about 40 mg / dL and 75 mg / dL, about 97% of the estimated glucose concentration values are within ±5 mg / dL of the corresponding YSI measurement values, and at glucose concentrations between about 70 mg / dL and 400 mg / dL, about 99% of the estimated glucose concentrations are within ±10% of the corresponding YSI measurement values.

[0265] Figure 3B shows a Clarke error grid related to the factory calibration study associated with Figure 3A. The Clarke error grid of Figure 3B is based on a correlation plot of the performance of the above-described factory calibration method against a reference method in the form of YSI measurements. If the correlation is perfect, all points lie on the 45-degree line. The area surrounding this line is divided into zones that predict the clinical outcome for the measures taken by the patient, depending on where the measurement by the factory calibration method falls on the line. Zone A corresponds to clinically accurate decisions (e.g., take insulin, take glucose, or do nothing), zone B corresponds to clinically acceptable decisions, and zone D corresponds to clinically incorrect decisions. As shown in Figure 3B, all of the data points from the factory calibration study fell into either zone A or zone B. In fact, almost all of the data points fell into zone A, thus establishing that the factory calibration study described above yields very accurate glucose concentration measurements.

[0266] Individual sensors of a group of sensors manufactured under substantially the same conditions were found to generally exhibit substantially similar or nearly identical sensor sensitivity profiles and to have a relationship of in vivo to in vitro sensor sensitivity that is substantially similar or nearly identical. However, it has been found that sometimes the actual sensor sensitivity (i.e., the sensitivity expressed as an actual sensitivity value, not a percentage of sensitivity in a substantially stable state) can vary between sensors. For example, individual sensors can be manufactured under substantially the same conditions, but if they are exposed to different environmental conditions during use (e.g., radiation, very severe temperature, abnormal dehydration, or exposure to any environment that would damage the enzyme of the sensor membrane or other parts of the sensor), they may have different sensitivity characteristics over the time between sensor manufacture and sensor use.

[0267] Accordingly, in order to compensate for potential effects resulting from these states, in one embodiment, the continuous analyte sensor is configured to request and receive one or more reference measurements at the start of a sensor session (e.g., from a fingerstick glucose measurement or from a calibration solution). For example, the request for one or more reference measurements can be made about 15 minutes, 30 minutes, 45 minutes, 1 hour, 2 hours, 3 hours, etc. after activating the sensor. In some embodiments, the sensor electronics are configured to process and use reference data to generate (or adjust) a sensor sensitivity profile in response to the input of one or more reference measurements to the sensor. For example, if a reference measurement of glucose concentration is obtained and input to the sensor at time = x, a sensor sensitivity algorithm function can be generated by matching the sensor sensitivity profile at time = x to the reference measurement. Using one of the one or more reference measurements in relation to a predetermined sensor sensitivity profile at the start of the sensor enables self-calibration of the sensor without or with reduced need for further reference measurements.

[0268] Figure 4 shows a Bland-Altman plot line demonstrating the difference between YSI reference measurements and in vivo continuous analyte sensors that received one reference measurement approximately 1 hour after insertion into the patient. The lines in Figure 4 represent accuracy criteria of less than approximately ±20 mg / dL at glucose concentrations between approximately 40 mg / dL and 75 mg / dL, and less than approximately +20% at glucose concentrations between approximately 75 mg / dL and 400 mg / dL, corresponding to deviations from the actual measurements (using the YSI test). An initial value of sensor sensitivity in vivo is generated from this reference measurement, which then enables the formation of an algorithmic function corresponding to the sensitivity profile for the remainder of the sensor session. The sensor is manufactured with a working electrode and a counter electrode used to measure a baseline signal that is subtracted from the glucose + baseline signal acquired by the working electrode. As shown, approximately 85% of the estimated glucose concentrations were within a range 410 defined as ±20 mg / dL from the corresponding YSI measurements for glucose concentrations between approximately 40 mg / dL and 75 mg / dL and ±20% from the corresponding YSI measurements for glucose concentrations between approximately 75 mg / dL and 400 mg / dL. In addition, for glucose concentration states between approximately 40 mg / dL and 75 mg / dL, approximately 95% of the estimated glucose concentration values were within the range of ±20 mg / dL of the corresponding YSI measurements. The sensors of this study achieved a level of accuracy corresponding to an average absolute relative difference of approximately 12% over a sensor session of at least 7 days, and an accuracy level on the first day corresponding to an average absolute relative difference of approximately 11%. For both overall and first-day accuracy levels, the median absolute relative difference obtained was approximately 10%.

[0269] FIG. 5 is a diagram showing different types of information that can be input into a sensor system to define a sensor sensitivity profile over a period of time in one embodiment. This input information can include information acquired before sensor session 510 and information acquired during sensor session 520. In the embodiment shown in FIG. 5, both the information acquired before sensor session 510 and the information acquired during sensor session 520 are used to generate, condition, and update a function 530 that is related to the sensor sensitivity profile, although in other embodiments, the sensor system can be configured to use only the information acquired before the sensor session. In one embodiment, the formation of the initial sensor sensitivity profile can occur before, at the start of, or immediately after the start of the sensor session. Additionally, in one embodiment, the sensor sensitivity profile can be continuously conditioned, reproduced, or updated to compensate for parameters that can affect sensor sensitivity or provide additional information regarding sensor sensitivity during the sensor session. The information acquired before the sensor session can include, for example, as described above, a sensor sensitivity profile generated before or at the start of the sensor session. It can also include, as described above, sensitivity values related to a substantially fixed relationship of in-vivo to ex-vivo sensor sensitivity.

[0270] Alternatively, instead of a fixed sensitivity value, the relationship of in-vivo sensor sensitivity to the ex-vivo can be defined as a function of the time between the completion of sensor manufacturing (or the time when calibration tests are performed for sensors from the same lot) and the start of the sensor session. As shown in FIG. 6, it has been found that the sensitivity of the sensor to the analyte concentration may change as a function of the time between the completion of sensor manufacturing and the start of the sensor session. FIG. 6 shows this phenomenon by a plot line, which is similar to the downward trend of sensitivity over a certain period between the completion of sensor manufacturing and the start of the sensor session. Similar to the discovered change or drift of sensitivity over a certain period of the sensor session, this change or drift of sensitivity over a certain period between the completion of sensor manufacturing and the start of the sensor session is not only manufactured in substantially the same way under substantially the same conditions, but also exposure to certain conditions (e.g., radiation, very severe temperature, abnormal dehydration state, or any environment that can damage the enzyme of the sensor membrane or other parts of the sensor, exposure to others) is avoided and is generally consistent among sensors. Therefore, the change or drift of sensitivity over a certain period between the completion of sensor manufacturing and the start of the sensor session can also be modeled by a mathematical function 620 that accurately estimates this change or drift. The evaluation algorithm function 620 can be any of various functions, such as linear functions (including constant functions), logarithmic functions, quadratic functions, cubic functions, square root functions, power functions, polynomial functions, rational functions, exponential functions, sinusoidal functions, and combinations thereof.

[0271] The information obtained before the sensor session can also include information regarding the characteristics or attributes of a sensor. By way of example, and not limitation, the information obtained before the sensor session can include the specific materials used to manufacture the sensor (e.g., the materials used to form the sensor membrane), the thickness of the sensor membrane, the permeability of the membrane to glucose or other chemical species, the in vivo or in vitro sensor sensitivity profile of other sensors manufactured in substantially the same manner under substantially the same conditions, and the like. In certain embodiments, the information obtained before the sensor session can include information regarding the process conditions under which the sensor was manufactured. This information can include, for example, the temperature at which the sensor membrane was cured, the duration of time the sensor was immersed in a particular coating solution, and others. In other embodiments, the information obtained before the sensor session can be related to the physiological information of the patient. For example, the patient's age, body mass index, gender, and / or historical patient sensitivity profile can be used as parameters to form the sensor sensitivity profile. Other information that can be obtained and used before the sensor session includes information regarding the insertion of the sensor, such as the location (e.g., abdomen vs. back) or the depth of sensor insertion.

[0272] Generally, the sensitivity function of a sensor can be generated by theoretical or empirical methods or both, and stored as a look-up table as a function, thereby enabling self-calibration of the sensor that eliminates (or substantially reduces) the need for a reference measurement value. The sensitivity function of the sensor can be generated at the manufacturing facility and shipped with the system, or generated by the system immediately before (or during) use. As used herein, the term "self-calibration" is a broad term and is given its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning), and it refers to, but is not limited to, calibration of a sensor or device performed by someone other than the manufacturer or installer of the control system, the manufacturer of the sensor, or the user of the sensor. Calibration of the sensor can be performed on the individual sensors on which the control system is mounted, or it can be performed on a reference sensor, such as one from the same sensor lot, and the calibration function can be transferred from one control system to another. In some embodiments, the system can be shipped with several self-calibration functions and can be added by others by the sensor user. Also, the sensor system can be shipped with a self-calibration function and only requires adjustment or touch-up calibration during use.

[0273] In some embodiments, the sensor sensitivity profile can be adjusted during sensor use to compensate in real time for parameters that can affect sensor sensitivity or provide additional information regarding sensor sensitivity. These parameters can include, but are not limited to, parameters related to sensor attributes such as the permeability of the sensor membrane or the level of hydration of the sensor, or parameters related to the physiological information of the patient such as the patient's body temperature (e.g., the temperature of the sample location or the skin temperature), the pH of the sample location, the level of hematocrit value, or the oxygen level of the sample location. In some embodiments, a thermistor can be attached to the ex vivo portion of the sensor and a thermally conductive wiring can be extended from the thermistor to the sample location for a continuous analyte sensor.

[0274] In some embodiments, the calibration method can be improved by algorithmically quantifying the respective unique sensor / user environment. Quantification can be achieved by generating or adjusting the sensitivity profile, which can include an inference engine that includes a causal probability network such as a Bayesian network. A Bayesian network includes a network based on conditional probabilities that depend on Bayes' theorem, and characterizes the likelihood of different outcomes based on known prior probabilities (e.g., the prevalence of in vivo sensitivity observed under a certain parameter state) and newly acquired information (e.g., the occurrence or non-occurrence of the above-described state).

[0275] In one embodiment, quantification is achieved by applying an analytical Bayesian framework to data that already exists within the scope of the system algorithm. Quantification includes (1) wedge (e.g., maximum or minimum) values for parameters related to sensitivity and baseline; (2) sensitivity values calculated during a sensor session; and (3) a baseline calculated during a sensor session. In some embodiments, the first set of wedge values is based on the parameter distribution of "prior" data (e.g., data collected from previous clinical studies). By using a Bayesian network, the system can learn from data collected during a sensor session and adapt its wedge values (or other algorithm parameters and / or constraints) to a particular sensor / user environment. As a result, the calibration method of the system can be improved to have better accuracy, reliability, and overall performance.

[0276] In one embodiment, the Bayesian framework used includes establishing the framework using known prior information, and then reasoning from their combination (i.e., the combination of prior and new information) considering the newly collected information to generate subsequent information. When the prior information and the new information are mathematically related and combined to form subsequent information that can be functionally represented, that relationship is defined as a junction. The Bayesian framework of one embodiment uses a combined distribution relationship that reduces to posterior distribution parameters (e.g., mean value, variance) that are directly available. This would be advantageous under limited computational time and resources and limited computational space.

[0277] In some embodiments, under the Bayesian framework, the wedge parameters follow a prior substantially normal distribution for each parameter, and the algorithmically computed sensitivity and baseline values follow their substantially normal distributions representing new information, and independent posterior distributions for the sensitivity and / or baseline can be generated. Subsequently, these two posterior distributions are used in parallel to create a glucose threshold used for accepting, rejecting, or modifying the user's manual calibration input (e.g., input from finger stick measurements). In one embodiment, the posterior distributions of the sensitivity and baseline can also be used for semi-self-calibration, thereby reducing the number of finger stick measurements required for manual calibration.

[0278] In one exemplary embodiment, from prior information, it is known that the minimum / maximum values of the wedge for sensor sensitivity to glucose are A and B in a 95% confidence interval in a substantially normal distribution. By using the algorithmically computed sensitivity value as new information, the previous three, four, five, six, seven or more sensitivity values calculated by manual calibration can be used as new information for generating a posterior distribution, which typically has reduced variability compared to the distribution based on prior information and is a direct quantification of the specific sensor / user environment. The reduced variability causes the generated posterior distribution to typically have a wedge value closer to the difference between A and B, i.e., the wedge values of the distribution generated from prior information. This tightening of the difference between the wedge values of the prior and posterior distributions enables the system to more reliably reject clearly erroneous manual calibration inputs, such as a calibration input of 60 mg / dL that was intended by the user to be an input of 160 mg / dL.

[0279] A Bayesian network uses causal knowledge between different events and models probabilistic dependence or independence relationships. FIG. 7A shows a distribution curve of sensor sensitivity corresponding to a Bayesian learning process according to one embodiment. FIG. 7B shows the confidence level associated with the sensor sensitivity profile corresponding to the distribution curve shown in FIG. 7A. The distribution curve 720 and the confidence levels 730 (e.g., 25%, 33%, 50%, 75%, 95% or 99% confidence levels) are related to the lack of initial knowledge about a certain parameter that affects sensor sensitivity or provides additional information about sensor sensitivity. For example, the distribution curve 720 can be related to factory information. As information about a certain parameter is obtained and the certainty of the sensor sensitivity profile 710 increases, the distribution curve 720' becomes steeper and the confidence interval 730' becomes narrower. Examples of information that can be used to change the distribution curve can include reference specimen values, calibration tests of sensors in the factory, patient medical history information, and any other information described in other parts of this specification that can affect sensor sensitivity or provide information about sensor sensitivity. As information about yet another parameter is obtained and the certainty of the sensor sensitivity profile 710 further increases, the distribution curve 720″ becomes even steeper and the confidence interval 730″ becomes even narrower.

[0280] While the sensor is in use, the confidence interval curves 730, 730', and / or 730'' can be used to form a sensitivity profile 710 that gives the sensitivity values estimated at a given time. Next, the estimated sensitivity values can be used to calibrate the sensor, which enables processing of the sensor data to generate glucose concentration values for display to the user. In some embodiments, a first estimated sensitivity profile 710, formed from the confidence interval curves 730, 730', and / or 730'', can be used to monitor and display glucose concentration. Additionally, the confidence interval curves 730, 730', 730'', or combinations thereof can be used to form a second estimated sensitivity profile that is not as accurate as the first estimated sensitivity profile but nonetheless results in detection in the hypoglycemic or hyperglycemic range more so than the first estimated sensitivity profile 710.

[0281] Determination of Baseline When the sensor is implanted in a recipient, various types of noise may occur. Some implantable sensors measure a signal (e.g., a count) consisting of two components: a baseline signal and an analyte signal. The baseline signal consists substantially of signal contributions from factors other than the measured analyte (e.g., interfering species, non-reaction related hydrogen peroxide, or other electroactive chemical species having an oxidation potential overlapping with the analyte or co-analyte). The analyte signal (e.g., glucose signal) consists substantially of the signal contribution from the analyte. Thus, since the signal contains these two components, calibration can be performed by solving the equation y = mx + b to determine the analyte (e.g., glucose) concentration, where the value of b represents the baseline of the signal. In some situations, the baseline consists of both constant and non-constant non-analyte factors. Generally, it is desirable to reduce or remove the background signal to give a more accurate analyte concentration to the patient or healthcare professional.

[0282] In one embodiment, a sample sensor (e.g., a glucose sensor) is configured to be inserted into a recipient to measure a sample within the recipient. The sensor includes a working electrode disposed under an active enzyme portion of a membrane on the sensor, a counter electrode disposed under an inert or non-enzyme portion of the membrane on the sensor, and sensor electronics operably connected to the working electrode and the counter electrode. The sensor electronics are configured to process signals from the electrodes to generate an estimate of the sample (e.g., glucose) concentration that substantially excludes signal contributions from non-glucose related noise artifacts.

[0283] In some embodiments, the working electrode is configured to generate a first signal related to the sample and a non-sample related electroactive compound having an oxidation potential less than or similar to a first oxidation potential, via the sensor electronics. The counter electrode is configured to generate a second signal related to the non-sample related electroactive compound. The non-sample related electroactive compound is any compound present in the local environment of the sensor and having an oxidation potential less than or similar to the oxidation potential of the species being measured (e.g., H2O2). Without wishing to be bound by theory, it is thought that both signals directly related to the enzymatic reaction of glucose (generating H2O2 oxidized at the first working electrode) and signals from unknown compounds in the extracellular environment surrounding the sensor can be measured by the electrode measuring glucose. These unknown compounds may or may not be constant in concentration and / or effect (e.g., intermittent or transient). In some situations, some of these unknown compounds are thought to be related to the recipient's disease state. For example, during / after a heart attack, it is known that blood chemistry changes dramatically (e.g., changes in pH, changes in the concentration of various blood components / proteins, etc.). As another example, percutaneous insertion of a needle-type sensor can initiate a series of events, including the release of various reactive molecules by macrophages. Other components that can contribute to non-glucose related signals are compounds related to the wound healing process, which can be initiated by implantation / insertion of the sensor into the recipient, as described in more detail with reference to Patent Document 6.

[0284] As described above, the auxiliary electrode is configured to generate a second signal associated with an analyte-unrelated electroactive compound having an oxidation potential less than or similar to the first oxidation potential. Analyte-unrelated electroactive species may include interfering species, non-reaction-related species corresponding to the species being measured (e.g., H2O2), and other electroactive species. Interfering species include any compound that is not directly related to the electrochemical signal generated by the enzymatic reaction of the analyte, such as electroactive species in the local environment generated by other bodily processes (e.g., cell metabolism, wound healing, disease processes, etc.). Non-reaction-related species include any compound from a source other than an enzymatic reaction, such as H2O2 released by nearby cells during the course of cell metabolism, H2O2 generated by other enzymatic reactions (e.g., extracellular enzymes that may be released during the death of cells surrounding or near the sensor, or by activated macrophages). Other electroactive species include any compound having an oxidation potential less than or similar to that of H2O2.

[0285] Non-analyte signals generated by compounds other than the analyte (e.g., glucose) are considered background noise, which obscures the signal associated with the analyte and thereby contributes to the inaccuracy of the sensor. As described in more detail in other parts of this specification, background noise includes both constant and non-constant components and can be removed to accurately calculate the analyte concentration.

[0286] In some embodiments, the analyte sensor system is configured such that the working electrode and the auxiliary electrode are affected by substantially the same external / environmental factors, thereby enabling substantially equivalent measurements of constant and non-constant species / noises (e.g., symmetric, coaxial design, and / or integrated configuration). This enables substantial removal of noise on the sensor signal, as described in other parts of this specification using sensor electronics. As a result, a substantial reduction or elimination of the effect of noise-related signals, including non-constant noise (e.g., transient, unpredictable biologically-related noise), increases the accuracy of the continuous sensor signal.

[0287] In some embodiments, the sensor electronics is connected to the working electrode and the auxiliary electrode in an operable state. The sensor electronics can be configured to measure a current (or voltage) to generate first and second signals. The first and second signals can be used together to generate glucose concentration data without substantial signal contribution from non-glucose related noise. This can be performed, for example, by subtracting the second signal from the first signal, or by alternative data analysis techniques, to generate a signal related to the analyte concentration without substantial noise contribution.

[0288] In other embodiments, since an auxiliary electrode is not required, the sensor electronics is connected in an operable state to only one or more working electrodes. For example, the sensor membranes of some embodiments can be composed of a polymer that includes a mediator and an enzyme chemically attached thereto. The mediator used can be oxidized at a potential lower than hydrogen peroxide, and thus some interferents oxidizable at these low potentials are oxidized. Thus, in some embodiments, a very low baseline (i.e., a baseline approaching 0 baseline and substantially not receiving signal contribution from non-glucose related noise) can be achieved, thereby potentially eliminating (or reducing) the need for an auxiliary electrode to measure signal contribution from non-glucose related noise.

[0289] Sensor electronics can include a potentiostat, an A / D converter, a RAM, a ROM, a transceiver, a processor, and / or others. The potentiostat can provide a bias to the electrode and be used to convert raw data (e.g., raw counts) collected from the sensor into an analyte concentration value (e.g., a glucose concentration value expressed in units of mg / dL). The transmitter can be used to communicate the first and second signals to the receiver, where additional data analysis and / or calibration of the analyte concentration can be processed. In certain embodiments, the sensor electronics can perform additional operations such as, for example, data filtering and noise analysis.

[0290] In certain embodiments, the sensor electronics can be configured to analyze a sample equivalent baseline or a normalized baseline instead of a baseline. The normalized baseline is calculated as the y-intercept divided by b / m or the slope (from the equation y = mx + b). The unit of the sample equivalent baseline can be expressed as the unit of analyte concentration (mg / dL) associated with the output of the continuous sample sensor. By using the sample equivalent baseline (normalized baseline), the effect of glucose sensitivity on the baseline can be eliminated, thereby enabling the baselines of different sensors (e.g., from the same sensor lot or different sensor lots) to be evaluated by different glucose sensitivities.

[0291] Some embodiments are described herein that use a counter electrode that allows subtraction of the baseline signal from the glucose + baseline signal, it being understood here that the use of this electrode is optional and not available in other embodiments. For example, in one embodiment, the membrane system covering the working electrode can substantially prevent interference and substantially reduce the baseline to a negligible level so that the baseline can be estimated. The estimation of the baseline can be based on the assumption of the baseline of the sensor in a physiological state relevant to a typical patient. For example, the baseline estimation can be modeled after in vivo or in vitro measurements of the baseline according to a physiological level with interference specific to inside the body. FIG. 8 is a graph that provides a comparison between the estimated glucose equivalent baseline and the detected glucose equivalent baseline according to one study. The estimated glucose equivalent baseline was formed by deriving the in vitro measurement value of the baseline of the glucose sensor in a solution mimicking the physiological level of human interference. The interference contains uric acid and ascorbic acid at a concentration of about 4 mg / dL, and the concentration of ascorbic acid is about 1 mg / dL. As shown in FIG. 8, in this study, it was found that the estimated baseline closely resembles the detected baseline. Thus, due to the possibility of accurate assessment of the baseline and / or a negligible baseline, a single working electrode (i.e., without the use of a counter electrode) of a predetermined sensor sensitivity profile is sufficient to provide self-calibration to the sensor system.

[0292] In some embodiments, it has been found that not only does the sensitivity of the sensor tend to drift over time, but the baseline of the sensor also drifts over time. Thus, in one embodiment, the concepts underlying the methods and systems used to predict sensitivity drift can be applied to generate a model for predicting baseline drift over time. Without wishing to be bound by theory, it is believed that the overall signal received by the sensor electrode is composed of a component of the glucose signal, a component of the interference signal, and a component of the baseline signal associated with the electrode that is substantially independent of the surrounding conditions (e.g., extracellular matrix) surrounding the electrode. As noted above, the term "baseline" as used herein refers to, but is not limited to, the component of the analyte sensor signal that is not related to the analyte concentration. Thus, the baseline as a term defined herein consists of the component of the interference signal and the signal of the component of the electrode-related baseline. Without wishing to be bound by theory, an increase in membrane permeability is thought to typically result not only in an increase in the rate of glucose diffusion across the entire sensor membrane, but also in an increase in the rate of interferent diffusion across the entire sensor membrane. Thus, the change in permeability of the sensor membrane over time that causes sensor sensitivity drift can similarly cause the component of the baseline interference signal to drift. Put simply, the baseline interference signal component is not static and typically changes as a function of time, which then causes baseline drift over time. By analyzing how each of the above-described components of the baseline responds to changes in state and time (e.g., as a function of time, temperature), a predictive model can be developed to predict how much the baseline of the sensor will drift during a sensor session. It is believed that by being able to predict the sensitivity and baseline of the sensor into the future, a self-calibrating continuous analyte sensor can be achieved, i.e., a sensor that does not require the use of a reference measurement (e.g., finger stick measurement) for calibration.

[0293] Calibration Code The process of manufacturing continuous specimen sensors may sometimes be exposed to some variation between sensor lots. To compensate for this variation, one or more calibration codes can be assigned to each sensor or group of sensors to define parameters that affect sensor sensitivity or provide additional information regarding the sensitivity profile. This calibration code can reduce the variation between different sensors and ensure that the results obtained from using sensors from different sensor lots are generally equal and consistent by applying an algorithm that adjusts for the differences. In one embodiment, the specimen sensor system can be configured such that one or more calibration codes are manually entered into the system by the user. In other embodiments, the calibration code can be part of a calibration-compliant label that is attached to (or inserted into) a package of multiple sensors. This calibration-compliant label itself can be read or queried by any of a variety of technologies including, but not limited to, optical technologies, RFID (radio frequency identification), and combinations thereof. These technologies for transferring the code to the sensor system can be made more automated, accurate, and convenient for the patient and have a lower tendency for errors compared to manual entry. Manual entry has an inherent risk of errors, for example, incorrect calibration, which may lead to inaccurate glucose concentration readings caused by patients or hospital staff entering the wrong code. As a result, this can result in patients or hospital staff taking inappropriate measures (e.g., injecting insulin while in a hypoglycemic state).

[0294] In some embodiments, the calibration code assigned to the sensor can include a first calibration code related to a predefined logarithmic function corresponding to the sensitivity profile, a second calibration code related to the sensitivity value in the first in vivo, and other calibration codes, each code defining a parameter that affects the sensor sensitivity or provides information regarding the sensor sensitivity. The other calibration codes can be related to any prior information or parameters described in other parts of the present specification, and / or any parameters that help define the mathematical relationship between the measured signal and the analyte concentration.

[0295] In some embodiments, a package used to store and transport a continuous specimen sensor (or sensor set) includes a detector configured to measure parameters that affect sensor sensitivity or provide additional information regarding sensor sensitivity or other sensor characteristics. For example, in one embodiment, the sensor package can include a temperature detector configured to provide calibration information regarding whether the sensor has been exposed to temperature conditions that exceed (and / or are less than) one or more predetermined temperature values. In some embodiments, the one or more predetermined temperature values can be values that exceed about 75 degrees Fahrenheit, exceed about 80 degrees Fahrenheit, exceed about 85 degrees Fahrenheit, exceed about 90 degrees Fahrenheit, exceed about 95 degrees Fahrenheit, exceed about 100 degrees Fahrenheit, exceed about 105 degrees Fahrenheit, and / or exceed about 110 degrees Fahrenheit. Additionally or alternatively, the one or more predetermined temperature values can be values that are less than about 75 degrees Fahrenheit, less than about 70 degrees Fahrenheit, less than about 60 degrees Fahrenheit, less than about 55 degrees Fahrenheit, less than about 40 degrees Fahrenheit, less than about 32 degrees Fahrenheit, less than about 10 degrees Fahrenheit, and / or less than about 0 degrees Fahrenheit. In one embodiment, the sensor package can include a moisture exposure indicator configured to provide calibration information regarding whether the sensor has been exposed to moisture levels that exceed or are less than one or more predetermined humidity values. In some embodiments, the one or more predetermined humidity values can be values that exceed about 60% relative humidity, exceed about 70% relative humidity, exceed about 80% relative humidity, and / or exceed about 90% relative humidity. Alternatively or additionally, the one or more predetermined humidity values can be values that are less than about 30% relative humidity, less than about 20% relative humidity, and / or less than about 10% relative humidity.

[0296] When exposure of the sensor to a certain level of temperature and / or humidity is detected, the corresponding calibration code can be changed to compensate for the possible effects of this exposure on sensor sensitivity or other sensor characteristics. This change in the calibration code can be automatically performed by a control system associated with the sensor package. Alternatively, in other embodiments, an indicator (e.g., a color-changing indicator) adapted to change (e.g., change color) when exposed to an environment can be used. As an example, and not by way of limitation, the sensor package can include an indicator that irreversibly changes color from blue to red when the package is exposed to a temperature higher than about 85 degrees Fahrenheit, and can include an instruction to the user to enter a certain calibration code when the indicator is red. Examples of conditions that can be detected by the sensor package and are described herein for temperature and humidity and are used to trigger a change in calibration code information, but it should be understood that other conditions can also be detected and used to trigger a change in calibration code information.

[0297] In certain embodiments, a continuous analyte system can comprise a library storing sensor sensitivity functions or calibration functions associated with one or more calibration codes. Each sensitivity function or calibration function results in calibrating the system to different settings of conditions. Different conditions during sensor use can be related to temperature, body mass index, various conditions, or any parameter that can affect sensor sensitivity or provide additional information regarding sensor sensitivity. The library can also include sensitivity profiles, or calibrations for different types of sensors or different sensor lots. For example, a single sensitivity profile library can include sub-libraries of sensitivity profiles for different sensors manufactured from different sensor lots and / or manufactured with different design configurations (e.g., different design configurations customized for patients with different body mass indices).

[0298] Determination of Sensor Attributes and Calibration of Sensor Data Using One or More Stimulus Signals To determine the attributes of a sensor and / or calibrate sensor data, some embodiments apply one or more stimulus signals to the sensor. As used herein, the term "stimulus signal" is a broad term and is given its ordinary and customary meaning to those of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, a signal (e.g., a quantity that varies with time or space such as voltage, current, or electric field strength) applied to a system (e.g., a specimen sensor) for generating or eliciting a response.

[0299] Non-limiting examples of stimulus signals that can be used in the embodiments described herein include a step increase in voltage of a first magnitude, a step decrease in voltage of a second magnitude (where the first and second magnitudes can be the same or different), an increase in voltage at a first rate over a period of time, a stepwise decrease in voltage at a second rate over a period of time (where the first rate and the second rate can be different or the same), one or more sine waves of the same or different frequencies and / or amplitudes superimposed on an input signal, and can be a waveform including one or more of these. The response to the stimulus signal can subsequently be measured and analyzed (the response is also referred to herein as the "signal response"). The analysis can include one or more of calculating impedance values, capacitance values, and relating the signal response to one or more predetermined relationships. As used herein, the term "impedance value" can mean a value representing electrical impedance, and can include a value representing only the magnitude of the impedance or a value representing the magnitude and phase of the impedance when the impedance is represented in polar form, or a value representing only the real impedance or both the real and complex impedance when the impedance is represented in Cartesian form, but is not limited to these. Based on the calculated impedance values, capacitance values and / or predetermined relationships, various sensor attributes, such as one or more of the sensor attributes discussed herein, can be determined and / or characterized.

[0300] Next, sensor information can be used to determine whether the analyte sensor is functioning properly and / or to calibrate the sensor. For example, the techniques described herein can be used to generate calibration information (e.g., one or more of a baseline, sensor sensitivity, and temperature information), but as described in more detail elsewhere herein, that calibration information forms or modifies a conversion function or calibration factor used to convert sensor data (e.g., in units of current) to blood glucose data (e.g., a glucose concentration value in units of mg / dL or mmol / L). Sensor information can alternatively or additionally be used to first correct uncalibrated sensor data (e.g., raw sensor data), and then apply a conversion function to convert the corrected, uncalibrated data to calibrated sensor data (e.g., a glucose concentration value in units of glucose concentration).

[0301] For example, one technique that can be used to determine the attributes of a system being used (e.g., an analyte sensor) is electrochemical impedance spectroscopy (EIS). EIS is an electrochemical technique based on the measurement of the electrical impedance of a system used over different ranges of frequencies. Changes in the system being used can be reflected in changes in the frequency spectrum. As an example, if the sensitivity of the system being used changes over a period of time, a decrease in impedance at a particular frequency can be observed during that period. As further discussed below, other techniques can also be used to determine the attributes of the system being used.

[0302] As an example illustrative of how a stimulation signal can be used to determine the attributes of a sensor, reference is made here to the schematic diagram of an equivalent sensor circuit model 900 shown in FIG. 9. The sensor circuit model 900 can represent the electrical properties of a specimen sensor, such as an embodiment of a continuous glucose sensor. The circuit 900 includes a working electrode 904 and a reference electrode 902. What is operably connected in series to the reference electrode is Rsolution, which represents the bulk resistance between the working electrode and the reference electrode. This bulk is the liquid or other medium in which the sensor is disposed, such as the buffer solution in a research example in a bench test. In an example of the use of a sensor disposed subcutaneously, this bulk can represent the resistance of the subcutaneous tissue between the working electrode and the reference electrode. What is operably connected to Rsolution is Cmembrane, which represents the capacitance of the sensor membrane, and Rmembrane, which represents the resistance of the sensor membrane. A parallel network of Cdouble layer and Rpolarization is operably connected to Rmembrane. The parallel network of Cdouble layer and Rpolarization represents the reaction occurring on the surface of the platinum interface of the working electrode. In particular, Cdouble layer represents the charge built up when the platinum electrode is in the bulk, and Rpolarization is the polarization resistance of the electrochemical reaction occurring at the platinum interface.

[0303] FIG. 10 is a board diagram of a specimen sensor of one embodiment (i.e., |Z real | versus log ω, where Z real(where \(Z\) is the real impedance, \(\omega = 2\pi f\), and \(f\) is the frequency). The analyte sensor can have the attributes of the sensor circuit model 900 in FIG. 9. Returning to the board diagram of FIG. 10, the x-axis is the frequency of the stimulus signal applied to the analyte sensor, and the y-axis is the impedance derived from the response signal of the analyte sensor. Although not bound by theory, it is thought that different frequencies can be used to measure or determine different material attributes of the sensor. For example, in the graph of FIG. 10, the impedance value at a frequency of approximately 7 Hz, which is derived from the response measured for the input signal, can represent the Cdouble layer, a frequency of approximately 1 kHz can represent the Rmembrane, and frequencies in the range of approximately 10 - 20 kHz can represent the Cmembrane.

[0304] Based on this information, the state of a particular attribute of the sensor can be determined by applying a stimulus signal containing a particular frequency or a plurality of frequencies to the sensor and determining the sensor impedance based on the signal response. For example, the capacitance of a sensor having the characteristics of the board diagram of FIG. 10 can be determined using a stimulus signal at a frequency in the range of approximately 1 Hz to 100 Hz, such as a frequency above 10 Hz or 10 kHz. In addition, the resistance of the sensor can be determined using a stimulus signal at a frequency in the range of approximately 100 Hz to 10 kHz, such as a frequency of 1 kHz.

[0305] FIG. 11 is a flowchart showing a process 1100 for determining the impedance of a sensor according to an embodiment. At step 1102, a stimulus signal in the form of an effective current (alternating current) voltage at a given frequency is applied to the working electrode of the sensor under study. The AC voltage can be superimposed on the bias potential and can be relatively small compared to the bias potential, such as a voltage in the range of about 1% to 10% of the bias voltage. In one embodiment, the AC voltage is a sine wave with an amplitude in the range of 10 to 50 mV and a frequency in the range of 100 to 1 kHz. This sine wave can be superimposed on a bias potential of 600 mV. The response signal can then be measured at step 1104 and analyzed at step 1106 to determine the impedance at a given frequency. If there is interest in the impedance of the sensor over a range of frequencies, the process 1100 can be repeated by applying the AC voltage at each frequency of interest and analyzing the corresponding output response.

[0306] Referring now to FIG. 12, a process according to one embodiment is described for determining the impedance or impedances of a sensor under study by applying one or more stimulus signals and transforming a response signal into the frequency domain. The data can be transformed into the frequency domain using Fourier transform techniques such as the fast Fourier transform (FFT), the discrete time Fourier transform (DTFT), or others. In step 1202, a stimulus signal in the form of a voltage step can be applied to the bias voltage of the sensor. The voltage step can be in the range of 10 - 50 mV, for example 10 mV, and the bias voltage can be 600 mV. Next, in step 1204, the signal response can be measured and recorded (e.g., the output current), and in step 1206, the derivative of the response can be obtained. In step 1208, subsequently, the Fourier transform of the derivative of the response can be calculated to obtain the alternating current in the frequency domain. One or more impedances of the sensor over a wide spectrum of frequencies can be calculated in step 1210 based on the alternating current.

[0307] FIG. 13 is a flowchart of a process 1300 for determining the impedance of a sensor under study, such as the impedance of the membrane of the sensor, according to one embodiment. In step 1302, a stimulus signal in the form of a voltage step above the bias voltage is applied to the sensor. The signal response is measured in step 1304, and the peak current of the response is determined in step 1306. Next, in step 1308, one or more impedance characteristics (e.g., resistance) of the membrane of the sensor (e.g., Rmembrane) are calculated based on the peak current. Subsequently, this one or more impedance characteristics can be associated with the attributes of the sensor.

[0308] In an alternative embodiment, instead of calculating the sensor impedance based on the peak current, the peak current can be associated with one or more predetermined sensor relationships to determine attributes of the sensor such as sensor sensitivity. That is, in an alternative embodiment, the step of calculating one or more impedance characteristics is omitted.

[0309] The relationship between the signal response resulting from the stimulus signal in the form of a voltage step and the sensor film resistance of the embodiment of the analyte sensor will be discussed further here with reference to FIGS. 14A, 14B, and 9.

[0310] FIG. 14A is a graph of an input voltage 1400 applied to an analyte sensor over a period of time according to one embodiment. The input voltage 1400 first applied to the analyte sensor corresponds to a bias voltage, which is about 600 mV in one embodiment. Subsequently, a stimulus signal in the form of a voltage step is applied to the input voltage at time t1. The magnitude Δv of the voltage step can range from 10 to 50 mV, for example 10 mV.

[0311] FIG. 14B is a graph of the current response 1402 of the analyte sensor to the input voltage 1400 of FIG. 14A. As shown in FIG. 14B, the current response 1402 can include a sharp spike in the current that begins at time t2 corresponding to the time when the voltage step begins to affect the response. The current response 1402 has a peak current at point 1404, and the current response 1402 tapers off and stabilizes at a slightly higher level due to the increase in the input voltage 1400 compared to before the voltage step.

[0312] In one embodiment, the change in current Δi measured as the difference between the magnitude of the current response 1402 before the voltage step and the magnitude of the peak current 1404 resulting from the voltage step can be used to estimate the sensor film resistance, such as Rmembrane in FIG. 9. In one embodiment, the estimated sensor film resistance can be calculated based on Ohm's law. Here, Rmembrane = Δv / Δi It is.

[0313] As described above, △v is the increase in the voltage step, and △i is the change in the current response due to the increase in the step voltage.

[0314] Although not desired to be bound by theory, certain embodiments of the sensor are thought to provide a direct relationship between the change in current in response to a voltage step and the impedance characteristics (e.g., resistance) of the membrane of the sensor.

[0315] As a non-limiting example of such a relationship, the following description refers back to the sensor circuit model 900 in FIG. 9. In some embodiments, the capacitance Cmembrane is much smaller than the capacitance Cdouble layer. For example, the value of Cmembrane is about 1 / 1000 smaller than Cdouble layer. The bulk resistance Rsolution of the sensor circuit 900 is typically much smaller than the resistance Rmembrane, and the resistance Rpolarization can be very large, such as about 3 megaohms. Due to such sensor attributes, the voltage step Δv applied to the circuit 900 causes current to flow substantially through the circuit 900 along a path that continues from the conductor 902 through Rsolution, Rmembrane, Cdouble layer, and finally to the conductor 904. Specifically, since the capacitive resistance is inversely proportional to the capacitance and the frequency, and theoretically the voltage step is at a very high frequency, the capacitive resistance of Cmembrane is initially extremely large due to the voltage step. Substantially all of the current flows through Rmembrane rather than through Cmembrane due to the high capacitive resistance of Cmembrane. Further, the current flows substantially through Cdouble layer instead of Rpolarization. This is because the capacitive resistance of Cdouble is initially small due to the voltage step (the high capacitance value of the Cdouble layer results in a low capacitive resistance at high frequencies, such as at the time of the voltage step) and the relatively large resistance of Rpolarization. Therefore, the initial overall resistance through which substantially all of the current flows through the circuit 900 when the step voltage is applied to the circuit 900 can be summarized as the series resistance of Rsolution plus Rmembrane. However, in this example, since Rsolution is much smaller than Rmembrane, the overall resistance can be estimated as the resistance of the membrane, Rmembrane.

[0316] Thus, since the resistance Rmembrane of the membrane at the time of the voltage step is essentially the resistance of the circuit 900, it has been found that the value of Rmembrane can be estimated using Ohm's law with known values of a stepwise increase Δv and a change Δi measured in the current response due to the voltage step.

[0317] a. Sensitivity As discussed in this specification, the sensitivity of the sensor to the analyte concentration during a sensor session will often change as a function of time. This change in sensitivity can manifest as an increase in current for a particular level of sensitivity. In some embodiments, the sensitivity increases as a relative change of several tens of percent during the first 24 - 48 hours. To provide the user with an accurate measured value of the analyte concentration, system calibration using a reference measurement (e.g., a strip - based blood glucose measurement) may be required. Typically, the calibration rate can be once, twice, or more per day.

[0318] As further discussed below, a relationship between sensitivity and impedance has been observed in embodiments of analyte sensors. Without wishing to be bound by theory, embodiments of analyte sensors are thought to have a relationship between the impedance of the sensor's membrane and the diffusion coefficient of the membrane. For example, a change in the impedance of the analyte sensor can indicate a proportional change in the diffusion coefficient of the analyte sensor's membrane. Further, an increase in the diffusion coefficient results in an increase in the transport of the measured analyte (e.g., glucose) through the membrane, resulting in an increase in the sensor output current. That is, a change in the diffusion coefficient can result in a proportional change in sensor sensitivity. It should be noted that depending on the characteristics of the sensor and the environment in which the sensor is used, other factors may actually be involved in the change in sensitivity, separate from the change in the diffusion coefficient of the sensor membrane.

[0319] The relationship between sensitivity and impedance can be used to estimate the sensitivity value of a sensor and / or to correct for the change in sensitivity of a sensor over time during a period, resulting in an improvement in accuracy, a reduction in the necessary calibration, or both. In addition to detecting sensitivity, some embodiments can detect other characteristics of the analyte sensor system based on measured values of electrical impedance at one or more frequencies. These characteristics include, but are not limited to, temperature, ingress of moisture into components making up the sensor electronics, and damage to the sensor film.

[0320] In some exemplary embodiments, the relationship between the impedance of a sensor and the sensitivity of the sensor can be used to calculate and compensate for changes in the sensitivity of the analyte sensor. For example, a change in the impedance of the analyte sensor may correspond to a proportional change in the sensitivity of the sensor. In addition, the absolute value of the impedance of the analyte sensor can correspond to the absolute value of the sensitivity of the analyte sensor, and a corresponding sensitivity value can be determined based on a predetermined relationship determined from prior studies of similar sensors. Sensor data can be compensated for changes in sensitivity based on the relationship between impedance and sensitivity.

[0321] FIG. 15 is a flowchart of an exemplary process 1500 for compensating sensor data for changes in sensitivity, according to one embodiment. At step 1502, a stimulus signal, such as a signal having a given frequency that can be used to determine the impedance of the sensor's membrane as discussed with reference to FIG. 11, can be applied to the sensor. Subsequently, at step 1504, the response to the applied signal is measured, and at step 1506, the impedance of the sensor's membrane is determined based on that response. Next, at step 1508, the determined impedance is compared to the established relationship between impedance and sensor sensitivity. This established relationship can be determined from prior studies of a reference sensor that shows the relationship between impedance and sensor sensitivity for an impedance similar to that of the currently used analyte sensor, e.g., a sensor manufactured in substantially the same way under substantially the same conditions as the currently used sensor. At step 1510, the sensor signal (e.g., in units of current or count) of the currently used sensor is corrected using the relationship between impedance and sensitivity. Subsequently, at step 1512, an estimated analyte concentration value is calculated based on the corrected sensor signal, e.g., using a conversion function. This estimated analyte concentration value can subsequently be used for further processing and / or output, such as triggering an alarm, displaying information representing the estimated value on a user device, and / or outputting information to an external device.

[0322] It should be understood here that Process 1500 is one example of using the impedance of a sensor to compensate for changes in sensor sensitivity, and that various modifications can be made to Process 1500 that are within the scope of its embodiments. For example, the established relationship between impedance and sensitivity can be used to determine the sensitivity value of the sensor being used. Subsequently, the sensitivity value can be used to modify or form a conversion function that is used to convert the sensor signal of the sensor being used into one or more estimated glucose concentration values. Additionally, instead of calculating an impedance value based on the stimulus signal response, one or more attributes of the response of the stimulus signal (e.g., peak current value, count, etc.) can be directly associated with the sensitivity based on a predetermined relationship between the attribute of the stimulus signal and the sensitivity.

[0323] Some embodiments use one or more impedance values of a sensor to form, modify, or select a sensitivity profile of the analyte sensor. As described above, the sensor can have a sensitivity profile that indicates changes in the sensitivity of the sensor over a period of time. Although sensors manufactured in substantially the same manner under substantially the same conditions may exhibit similar sensitivity profiles, the profiles may still vary. For example, the environment in which a particular sensor is used may cause the sensitivity profile of the sensor to differ from that of other similar sensors. Accordingly, some embodiments can select a sensitivity profile from a plurality of predetermined sensitivity profiles based on, for example, the correlation between the calculated one or more impedance values and the selected sensitivity profile. Further, some embodiments modify the sensor sensitivity profile already associated with the analyte sensor being used based on one or more impedance values to more closely predict the sensitivity profile of the sensor.

[0324] FIG. 16 is a flowchart of an exemplary process 1600 for determining a predicted sensitivity profile using one or more sensor membrane impedance measurements. At step 1602, a stimulation signal is applied to the analyte sensor to be used, and at step 1604, the response is measured. One or more sensor membrane impedance values are then calculated based on the response at step 1606. Various techniques for calculating the impedance value of the sensor membrane based on the response, such as one or more of the techniques discussed with reference to FIGS. 11-14, can be used in process 1600 and are described in other parts of this specification. Subsequently, at step 1608, a sensitivity profile is determined based on the one or more calculated impedance values. Subsequently, process 1600 (which can include looking back and revising and / or looking ahead and calculating in the past) uses the determined sensitivity profile to calculate an estimated analyte concentration value. Subsequently, the estimated analyte concentration value can be used for further processing and output, such as displaying information representing the estimated value on a user device and / or outputting the information to an external computer.

[0325] In addition to step 1608, various techniques can be used to determine the sensitivity profile. One exemplary technique is to compare one or more calculated impedance values with a plurality of different predicted sensitivity profiles and select the predicted sensitivity profile that best fits the one or more calculated impedance values. The plurality of different predicted sensitivity profiles can be predefined and, for example, stored in the computer memory of the sensor electronics. Other techniques that can be used include using an evaluation algorithm to predict or determine a sensitivity profile based on one or more calculated impedance values. Further techniques include determining a sensitivity profile by modifying a sensitivity profile associated with the sensor being used (e.g., a sensor profile previously used to generate an estimated glucose value using the sensor). Modifying the sensitivity profile includes using an evaluation algorithm to modify the sensitivity profile to more closely match the sensitivity profile of the sensor being used based on one or more calculated impedance values.

[0326] In some embodiments, to determine whether a sensor is functioning properly, one or more impedance values of the analyte sensor being used are compared to a predetermined or predicted sensitivity profile associated with the sensor. As described above, the sensor can be predicted to have a particular sensitivity profile based on, for example, a study of the sensitivity changes over a period of sensors that are manufactured in substantially the same way and used under substantially the same conditions. However, if the sensor is considered to not adequately track its predicted sensitivity profile based on the sensitivity derived from the impedance measurement of the sensor, due to, for example, improper sensor insertion, damage to the sensor during transport, manufacturing defects, etc., then it can be determined that the sensor is not functioning properly. In other words, (since, for example, the impedance of the sensor membrane can indicate the sensitivity of the sensor) if one or more impedance values of the sensor membrane do not adequately correspond to the predicted sensitivity profile of the sensor, it can be determined that the sensor is not functioning properly.

[0327] Figure 17 is a flowchart of an exemplary process 1700 for determining whether a specimen sensor being used is functioning properly based on a predicted sensitivity profile and one or more impedance measurements. At step 1702, a stimulation signal is applied to the specimen sensor being used, and at step 1704, a response is measured. Next, at step 1706, one or more sensor membrane impedance values are calculated based on the signal response. Various stimulation signals and techniques for calculating sensor membrane impedance values based on a signal response, such as any one of the techniques discussed with reference to FIGS. 11 - 14, can be used in process 1700 and are described in other parts of this specification. Next, process 1700 determines, at step 1708, the correspondence between the one or more calculated impedance values and the sensitivity profile. Then, at decision step 1710, process 1700 queries whether the one or more calculated impedance values sufficiently correspond to the predicted sensitivity profile. If it is determined that the one or more calculated impedance values sufficiently correspond to the predicted sensitivity profile, then subsequently, process 1700 confirms proper operation of the specimen sensor being used. If confirmed as appropriate at step 1710, process 1700 can be repeated after a predetermined time delay ranging from about 1 minute to 1 day, such as about 10 minutes, 1 hour, 12 hours, or 1 day. However, if it is determined that the one or more calculated impedance values do not sufficiently correspond to the predicted sensitivity profile, then process 1700 can initiate an error routine 1712. Error routine 1712 can include one or more of activating an audible alarm, displaying an error message on a user display, interrupting the display of sensor data on the user display, and sending a message to a remote communication device on a communication network, such as a cellular phone on a cellular phone network or a remote computer on the Internet.The error routine can include, for example, modifying a predicted sensitivity profile based on one or more impedance measurements or selecting a newly predicted sensitivity profile based on one or more impedance measurements. The modified predicted sensitivity profile or the newly predicted sensitivity profile can be a sensitivity profile that more closely corresponds to a change in sensitivity of the sensor used when compared to an unmodified or previously used predicted sensitivity profile based on the one or more impedance measurements.

[0328] In addition to step 1708 of process 1700, various statistical analysis techniques can be used to determine the correspondence between one or more impedance values and a predicted sensitivity profile. As one example, the correspondence can be determined based on whether a sensitivity value derived from a calculated impedance value (e.g., derived from a predefined relationship between impedance and sensitivity) differs from a predicted sensitivity value determined from the predicted sensitivity profile by a predefined threshold amount or more. The predefined threshold amount can be by absolute value or percentage. As another example, the correspondence can be determined based on a data-related function. The term "data-related function" as used herein is a broad term and is used in its ordinary meaning, including but not limited to statistical analysis of data and in particular correlation to or deviation from a particular curve. A data-related function can be used to show the relatedness of data. For example, sensor sensitivity data derived from impedance measurements described herein can be mathematically analyzed to determine the correlation to or deviation from a curve (e.g., a line or set of lines) that defines the sensitivity profile of the sensor, and this correlation or deviation is the relatedness of the data. Examples of data-related functions that can be used include, but are not limited to, linear regression, non-linear mapping / regression, rank (e.g., non-parametric) correlation, least mean squares fitting, mean absolute deviation (MAD), and mean absolute relative difference. In one such example, the correlation coefficient of linear regression represents the amount of data-relatedness of sensitivity data derived from impedance measurements from the sensitivity profile, and thus the quality of the data and / or sensitivity profile. Of course, in addition to those described herein, other statistical analysis methods for determining the correlation to a curve of one or more points can be used in process 1700.

[0329] As described above, processes 1600 and 1700 can use one or more impedance values. When using more than one impedance value, each impedance value can be spaced in time from other impedance values. In other words, one impedance value can be obtained at an initial time point t1 (representing the sensor impedance at time t1), a second impedance value can be obtained at a second, later time point t2 (representing the sensor impedance at time t2), and a third impedance value is obtained at a third, still later time point t3 (representing the sensor impedance at time t3). Further, the time between t1 and t2 can be a first period, and the time between t2 and t3 can be a second period that is the same as or different from the first period. Subsequently, the impedance values spaced in time can be used separately or combined using a statistical algorithm (e.g., calculating the average or median of the time-spaced values). Subsequently, the separate or combined values can be used, for example, to determine a sensitivity value and / or sensitivity profile in step 1608 of process 1600 or to determine a correspondence with a sensitivity profile in step 1708 of process 1700. In addition or alternatively, more than one impedance value can be obtained at substantially the same time, each being obtained using a different measurement technique, such as two of the measurement techniques described herein. For example, a first impedance can be calculated using the step voltage method as described in the process of FIG. 13, and a second impedance can be calculated using the sine wave overlay technique as described in the process of FIG. 11. Impedance values derived from different measurement techniques can subsequently be applied to a statistical algorithm (e.g., calculating an average or median) to determine a processed impedance value. The processed impedance value can subsequently be used, for example, to determine a sensitivity value and / or sensitivity profile in step 1608 of process 1600 or to determine a correspondence with a sensitivity profile in step 1708 of process 1700.

[0330] b. Temperature In some embodiments, signal processing techniques can be used to determine the temperature of the sensor. For example, a stimulus signal can be applied to the sensor, the signal response can be measured, and the temperature of the sensor can be extracted based on the signal response.

[0331] For example, the impedance of the sensor film determined using one of the techniques described with reference to FIGS. 11-14 can be used to estimate the temperature of the sensor according to one embodiment. Without wishing to be bound by theory, it is believed that the sensitivity of the sensor is affected by temperature, such that a high temperature can be associated with a high sensitivity and a low temperature can be associated with a low sensitivity. Similarly, since the impedance of the sensor film has a direct relationship with the sensitivity of the sensor, it is believed that a high temperature can be associated with a low impedance and a low temperature can be associated with a high impedance. That is, sensitivity and impedance can have a direct relationship with the temperature of the sensor. Thus, for example, using the known relationship between impedance and temperature based on previous studies of substantially similar sensors, the temperature of the sensor can be estimated based on the impedance measurement of the sensor.

[0332] FIG. 18 is a flowchart of an exemplary process 1800 for determining sensor temperature according to one embodiment. At step 1802, a stimulation signal is applied to the analyte sensor being used, and at step 1804 a response is measured and recorded. The impedance is calculated at step 1806 based on the signal response. The impedance can be calculated using any of the techniques described herein, such as the techniques described with reference to FIGS. 11-14. Subsequently, at step 1808, the temperature of the sensor is estimated based on a predetermined relationship between the impedance and the temperature. Subsequently, the temperature can be used to estimate an analyte concentration value (e.g., glucose concentration) using the sensor data or, alternatively, for further processing and / or output. For example, the temperature can be used to compensate for the effect of temperature on the sensitivity of the sensor, more accurate analyte concentration values can be estimated based on the sensitivity compensation, and more accurate analyte concentrations can be output to a display or used to trigger an alarm using a glucose monitoring system.

[0333] The relationship between sensor sensitivity and different temperatures can be mathematically modeled (e.g., by fitting a mathematical curve to data using one of the modeling techniques described herein), and subsequently, this mathematical model can be used to compensate for the effect of temperature on the sensitivity of the sensor. That is, the sensitivity of the sensor (which is affected by the temperature of the sensor) can be determined based on relating the measured impedance of the sensor to a mathematical curve. The predetermined relationship between impedance and temperature can be determined by studying the impedance of similar sensors over a range of temperatures. Subsequently, the sensor data can be converted to an analyte concentration value estimated based on the determined sensor sensitivity.

[0334] As a non-limiting example, in some embodiments of the analyte sensor, after a conditioning period of the sensor (e.g., a period after sensor implantation during which the sensor stabilizes, which may last from 1 to 5 hours in some embodiments), it can have an essentially linear relationship between temperature and impedance. The slope of the linear correlation can be established by studying sensors manufactured in substantially the same manner as the sensors used over the temperature range. Thereby, the sensor temperature can be estimated by measuring the impedance value of the sensor membrane and applying the impedance value to the established linear correlation. Other embodiments can have a non-linear relationship between impedance and temperature, and for these other embodiments, this relationship is characterized by using the established non-linear relationship.

[0335] In some embodiments, a first sensor temperature can be compared to a second sensor temperature, where the first temperature is derived from an impedance measurement of the analyte sensor and the second sensor temperature is derived independently of the impedance measurement. The second estimated temperature can be measured, for example, using a thermistor. In embodiments using a thermistor, the thermistor can be configured to measure temperature in vivo or in vitro and can be placed on or remotely from the analyte sensor. As a non-limiting example, the thermistor can be integral with the analyte sensor and placed on the surface of the recipient's skin adjacent to the insertion site where the analyte sensor is implanted therein, placed on the recipient's skin at a location remote from the insertion site, or placed on a portable device carried by the recipient, such that it is placed completely spaced from the recipient. Subsequently, factors causing changes in sensor sensitivity or other sensor attributes can be determined or confirmed based at least in part on the comparison of the first and second temperatures.

[0336] c. Moisture Ingress In some embodiments, ingress of moisture into the sensor electronics can be determined based on measuring the impedance of the sensor at a specific frequency or frequency range. If the measured impedance does not sufficiently correspond to a predetermined impedance value, the sensor system operably connected to the sensor can initiate a moisture ingress error routine. The correspondence can be determined using one or more threshold values, data-related functions, etc. Further, it should be noted here that phase information of the impedance can provide useful information when determining moisture release. Thus, in some embodiments, the impedance measurement can be separated into separate impedance magnitude and phase components, and one or both of the components of the impedance can be compared to a predetermined value to determine the correspondence.

[0337] FIG. 19 is a flowchart of an exemplary process 1900 for determining moisture ingress. At step 1902, a stimulus signal having a specific frequency or a signal having a spectrum of frequencies (e.g., a voltage step) is applied to the sensor under test, and at step 1904 the signal response is measured and recorded. The magnitude and phase of the impedance are calculated based on the signal response at step 1906. Subsequently, process 1900 determines at decision step 1908 whether the magnitude and phase values of the impedance are each included within a predefined range of levels. If the magnitude and phase values of the impedance exceed one or both of the predefined levels, process 1900 subsequently initiates an error routine at step 1910. This error routine can include activation of one or more audible alarms and / or visual alarms on a display screen to warn the user that the sensor system may not be functioning properly. This alarm can inform the user, for example, that there is a defect in the current sensor system. On the other hand, if one or both of the impedance and phase values fall into the predefined levels respectively, process 1900 ends.

[0338] The above has described calculating separate impedance and phase values, but it is understood here that the above process 1900 can determine complex impedance values by using data-related functions or by comparing the determined complex impedance with a threshold or range of predetermined complex impedance values, and can determine the correspondence between the determined complex impedance value and one or more predetermined complex impedance values. An error routine can be initiated in response to the correspondence.

[0339] d. Membrane Damage In some embodiments, membrane damage can be detected based on measuring impedance at a particular frequency or frequency range. If the measured impedance does not sufficiently correspond to one or more predetermined impedance values, a sensor system operably connected to the sensor can subsequently initiate a membrane damage error routine. The correspondence can be determined using a data-related function.

[0340] FIG. 20 is a flowchart of an exemplary process 2000 for determining membrane damage. At step 2002, a stimulus signal of a specific frequency, a plurality of signals having different frequencies, and / or a signal having a frequency spectrum are applied to a specimen sensor to be used, and at step 2004, the signal response is measured and recorded. At step 2006, both the magnitude and phase of the impedance are calculated based on the signal response. Subsequently, process 2000 determines, at decision step 2008, whether the magnitude and phase values of the impedance are each included within a predefined level range. If the magnitude and phase values of the impedance exceed one or both of the predefined levels, process 2000 subsequently initiates an error routine at step 2010. This error routine can include one or more activations of an audible alarm and a visual alarm on a display screen to warn the user that the sensor system is not functioning properly. This alarm can inform the user, for example, that the currently used sensor is damaged and needs to be replaced. On the other hand, if one or both of the magnitude and phase values of the impedance fall into the predefined levels respectively, process 2000 ends.

[0341] The above description describes the use of separate impedance magnitude and phase values, but it is understood here that the above process 2000 can use complex impedance values and can determine the correspondence between the complex impedance values and predefined values or levels. The error routine can be initiated in response to the determined correspondence.

[0342] e. Reuse of Sensor In some embodiments, it is possible to detect the reuse of a sensor. Embodiments of the glucose sensors described herein may have a defined lifespan during which the sensor can provide reliable sensor data. After the defined lifespan, the reliability of the sensor is no longer high and may provide inaccurate sensor data. To prevent use beyond a predefined lifespan, some embodiments notify the user to replace the sensor after it has been determined that the sensor should no longer be used. Various methods can be used to determine whether the sensor should no longer be used. For example, a predefined period since the sensor was first used (e.g., when first implanted in the user or when first electrically connected to the sensor electronics module), or a determination that the sensor has a defect (e.g., membrane rupture, unstable sensitivity, etc.). Once it is determined that the sensor should no longer be used, the sensor system can notify the user to use a new sensor, for example, audibly and / or visually prompting the use of a new sensor and / or shutting down the display or ceasing to display sensor data on the display. However, the user may attempt to reuse the same sensor instead of using a new sensor. This could be dangerous for the user, as the user is relying on inaccurate data that the sensor may provide.

[0343] Accordingly, some embodiments can be configured to determine sensor reuse based at least in part on one or more measurements of the impedance of the sensor. As described in more detail elsewhere in this specification, the impedance related to the membrane resistance of the sensor is initially typically high but gradually decreases as the sensor is broken in. Thus, in one embodiment, if the impedance measured shortly after implanting the sensor exceeds what the sensor would typically have when first implanted, this indicates that the sensor has already been used and sensor reuse can be detected.

[0344] FIG. 21 is a flowchart of an exemplary process 2100 for determining sensor reuse, according to one embodiment. At step 2102, a sensor insertion event is triggered. The insertion event can be one of many possible events indicating that a new sensor has been implanted, such as the user entering into the sensor system that a new sensor has been implanted, the sensor system detecting an electrical connection to the sensor, a predetermined period that has elapsed since the system prompted the user to use the new sensor, and the like. Subsequently, at step 2104, a stimulation signal is applied to the analyte sensor to be used, and a response is measured and recorded at step 2106. At step 2108, an impedance is calculated based on the signal response. The stimulation signal and technique for calculating the impedance at steps 2106 and 2108 can be any of the signals and techniques described herein, such as those described with reference to FIGS. 11-14. Subsequently, at decision step 2110, the calculated impedance is compared to a predetermined threshold. If it is determined that the impedance exceeds the threshold, then subsequently, a sensor reuse routine is initiated at step 2112. If it is determined at decision step 2110 that the impedance does not exceed the threshold, then subsequently, process 2100 ends at step 2114.

[0345] The sensor reuse routine of step 2112 includes activating an audible and / or visual alarm to notify the user of inappropriate sensor reuse. This alarm can also inform the user of why sensor reuse is undesirable, such as potentially outputting inaccurate and unreliable sensor measurements. The sensor reuse routine 2112 can instead or in addition cause a complete or partial shutdown of the sensor system and / or discontinue the display of sensor data on the user interface of the sensor system.

[0346] In one embodiment, the most recent impedance measurement information (e.g., one or more recent impedance measurements) of a previously used sensor (e.g., the sensor used immediately prior to a newly implanted sensor), or a predetermined sensor profile, can be stored in a computer memory and compared to the impedance measurement information of the newly implanted sensor (e.g., what is presumed to be the newly used sensor). Subsequently, if a comparison using, for example, a data correlation function indicates that the impedance at a time close to when the use of the sensor preceding the previously used sensor was interrupted (e.g., removed) is too similar to the impedance of the newly inserted sensor, then shortly after the new sensor is first implanted, since the sensor should have an impedance that is significantly different from the impedance of the previously used sensor substantially prior to the interruption of its use, it can be determined that the sensor is being reused. If it is determined that the sensor is being reused, subsequently, the sensor system can initiate an error routine, which, as described above with reference to step 2112, may include notifying the user of the inappropriate sensor reuse via an audible and / or visual alarm using the user interface of the sensor system, and prompting the user to use a new sensor.

[0347] FIG. 22 is a flowchart of another exemplary process 2200 for determining sensor reuse, according to one embodiment. At step 2202, a sensor insertion event is initiated. The insertion event can be one of many possible events indicating that a new sensor has been implanted, such as the user entering into the sensor system that a new sensor has been implanted, the sensor system detecting an electrical connection to the sensor, a predetermined period of time having elapsed since the system prompted the user to use the new sensor, etc. Next, at step 2204, a stimulation signal is applied to the analyte sensor being used, and at step 2206 a response is measured and recorded. The impedance is calculated at step 2208 based on the signal response. The stimulation signal and techniques for calculating the impedance at steps 2206 and 2208 can be any of the signals and techniques described herein, such as those described with reference to FIGS. 11-14. Subsequently, at decision step 2210, the calculated impedance is compared to one or more previously measured impedance values measured using one or more previously implanted sensors (even if the sensor system is made to say what the previously implanted sensors were). If it is determined that the calculated impedance correlates with one or more of the previously measured impedance measurements within a predetermined amount, then subsequently, a sensor reuse routine is initiated at step 2112. The correlation can be determined using a data correlation function, such as one of the data correlation functions described herein. If at decision step 2110 it is determined that the impedance does not correlate with the previously measured impedance values within a predetermined amount, then process 2100 ends at step 2114 and the system continues to use the sensor to measure the recipient's glucose concentration.

[0348] The sensor reuse routine of step 2212 can include activating an audible and / or visual alarm to notify the user of inappropriate sensor reuse. This alarm can also inform the user of why sensor reuse is undesirable, such as potentially outputting inaccurate and unreliable sensor measurements. The sensor reuse routine 2212 can, instead or in addition, cause a complete or partial shutdown of the sensor system and / or discontinue the display of sensor data on the display of the sensor system.

[0349] f. Sensor Overpotential Some embodiments apply an overpotential routine based on one or more measured impedances of a sensor membrane. Applying an overpotential (e.g., a voltage potential higher than the bias voltage applied to the sensor when used to continuously detect a specimen) to some embodiments of the specimen sensor has been found to assist in stabilizing the specimen sensor, thereby shortening the sensor's conditioning period. The overpotential needs to be interrupted once the sensor is sufficiently stable; otherwise, sensor damage may occur. Accordingly, one or more impedance measurements of the sensor can be used to determine the sensitivity or change in sensitivity of the sensor. Any of the techniques described in this specification, such as those described with reference to FIGS. 11 - 14, can be used to measure the impedance of the sensor. The determined sensitivity or change in sensitivity can then be used, for example, to determine whether the sensor has stabilized or will stabilize within a determined period by determining the correspondence between the measured impedance and a predefined sensitivity - impedance relationship. Once the sensor is determined to have stabilized or will stabilize within the determined period, the application of the overpotential can be interrupted or reduced. Additionally, the magnitude of the overpotential and / or the total time for which the overpotential should be applied to the sensor can be determined or modified based on one or more impedance measurements obtained before or during the application of the overpotential. That is, the overpotential routine, according to some embodiments, can be modified or interrupted according to one or more impedance measurements.

[0350] g. Multi-Electrode or Multi-Sensor Configuration Some embodiments of the sensor system include a plurality of sensor electrodes. For example, as described above, in addition to the specimen - sensing electrode, some embodiments can include a reference electrode to enable subtraction of the reference signal from the specimen + reference signal. Some embodiments can also include one or more redundant specimen sensors.

[0351] According to one embodiment, a first stimulation signal can be applied to a first sensor, and a second stimulation signal can be applied to a second sensor. The first sensor can be configured to detect an analyte concentration (e.g., glucose), and in this regard, can generate an analyte + baseline signal. The second sensor can be an auxiliary sensor configured to measure a baseline signal that can be subtracted from the signal of the first sensor. Additionally, the first stimulation signal can have the same waveform or a different waveform than the second stimulation signal. The response to the first stimulation signal and the response to the second stimulation signal can each be measured and recorded. The first response and the second response can be processed, and sensor characteristics can be determined based on the processing, such as impedance values associated with each sensor. This processing can include comparing the first and second response signals to each other (e.g., using the data-related functions described herein), and / or comparing each of the first and second response signals to an established relationship. The sensor characteristics can include any of the sensor characteristics described herein, including sensitivity values or changes in sensitivity of the first and / or second electrodes, the temperature of the first and / or second electrodes, the magnitude of the detected membrane damage of the first electrode, etc. Further, the sensor data can be compensated for changes in sensor characteristics, such as changes in sensor sensitivity, based on the processing of the first and second response signals.

[0352] FIG. 23 is a schematic diagram of a dual sensor configuration that can be used in a sensor system according to some embodiments. This dual sensor configuration includes a first sensor 2302 having a working electrode 2304, a reference electrode 2306, and a membrane 2308, and a second sensor 2310 having a working electrode 2312, a reference electrode 2314, and a membrane 2316. Each sensor can be made essentially the same, including having the same type of membrane, or each sensor can be made different, such as having different membranes or even one of the sensors not having a membrane. In one embodiment, each sensor 2302 and 2310 is configured to measure the analyte concentration of a recipient. In other embodiments, the second sensor 2310 does not have a separate reference electrode. In this alternative embodiment, the reference electrode of the first sensor can also function as the reference electrode of the second sensor.

[0353] As shown in FIG. 23, a stimulation signal can be applied to the first sensor 2302 using a signal generator / detector 2318 (the stimulation signal can be any stimulation signal described in this specification, such as a voltage step). The stimulation signal can induce an electric field line 2320 generated from the first sensor 2302 and cause an electrical response in the second sensor 2310. The electrical response can be measured using a signal generator / detector 2322 electrically connected to the second sensor 2314. The signal generators / detectors 2318 and 2322 can include any known electrical circuit configuration that can generate the desired stimulation signal described in this specification and measure the response to the stimulation signal.

[0354] In addition to FIG. 23, the response measured at the second sensor 2310 can then be used to determine sensor attributes such as any of the sensor attributes described herein. For example, the response can be used to calculate the impedance of the first sensor 2302, and then one of the processes described herein can be used to determine the sensitivity of the first sensor 2302 and / or to correct the sensor data generated by the first sensor.

[0355] Alternatively or in addition, the first stimulation signal can be applied to the first sensor 2302 and measured using the second sensor 2310, and the second stimulation signal can be applied to the second sensor 2310 and measured using the first sensor 2302. The first and second stimulation signals can be essentially the same or different. The responses to each of the first and second stimulation signals can then be used to determine sensor attributes such as any of the sensor attributes described herein.

[0356] The sensor to which the stimulation signal is applied can also be used to measure the response to the stimulation signal in addition to the other sensor that measures the response to the stimulation signal. For example, the first stimulation signal can be applied to the first sensor 2302, and the first and second responses to the first stimulation signal can be measured by the first sensor 2302 and the second sensor 2310, respectively. The first and second responses can then be used to determine the sensor attributes described herein for either or both of the first sensor 2302 and the second sensor 2310.

[0357] h. Scaling Factor In some embodiments, the scaling factor can be used to correct for differences in the response of the two - electrode analyte sensor. In some embodiments, the two - electrode analyte sensor that can be used is a reference sensor / system, thereby providing reference data for calibration (e.g., from the inside to the system) without using an external (e.g., separate from the system) analyte measurement device. In some embodiments, the two - electrode analyte sensor has a first electrode (which can be called a plus - enzyme electrode) that contains an enzyme that reacts with a specific analyte and a second electrode (which can be called a minus - enzyme electrode) that does not contain the enzyme.

[0358] In some embodiments, the sensor system (e.g., the sensor electronics module of the sensor system) is configured to determine a scaling factor (k). Briefly, the scaling factor defines the relationship between the electrodes of the two - electrode analyte sensor. Thus, in some embodiments, the sensor system is configured to calibrate the analyte sensor data using the scaling factor so that the calibrated sensor data does not include inaccuracies caused by the difference between each of the first and second working electrodes. That is, the scaling factor can be used to calculate an analyte value estimated based on the data generated by the sensor system.

[0359] Patent Document 7 describes in more detail a two - electrode analyte sensor system, a scaling factor, and a method of using the scaling factor that can be used in some embodiments, the entire content of which is incorporated herein by reference.

[0360] According to some embodiments, the membrane impedance of each electrode of the two - electrode system can be used to determine or update the scaling factor. The scaling factor can subsequently be used to calculate an analyte concentration value estimated based on the data generated by the sensor system.

[0361] The following shows an exemplary process for determining a scaling factor using impedance according to one embodiment. First, the membrane impedance is measured for both electrodes of a two-electrode sensor system. Techniques described herein, such as those described under the heading "Multi-Electrode or Multiple Sensor Configurations" with reference to FIGS. 11-14, can be used to measure the membrane impedance of each electrode of a two-electrode analyte sensor. The impedance can be measured periodically while using the sensor. The scaling factor can be generated using the ratio of the measured membrane impedances of the two electrodes (e.g., the ratio of the membrane impedance of the plus enzyme electrode to the membrane impedance of the minus enzyme electrode). The scaling factor for generating an estimated analyte value can subsequently be determined or updated based on the generated scaling factor. The determined or updated scaling factor can subsequently be used to generate an estimated analyte value.

[0362] Acetaminophen has been found to interfere with glucose measurements using some sensor embodiments. The response of acetaminophen can be proportional to diffusion through the sensor membrane, and it has been found that the sensor impedance can represent the membrane impedance. Thus, the above-described process for determining a scaling factor can be used to periodically determine the scaling factor for acetaminophen by determining the ratio of the impedances of the plus enzyme electrode and the minus enzyme electrode. The acetaminophen scaling factor can be used to update a scale factor, such as one of those described above, and can be used to calculate an estimated glucose concentration.

[0363] i. Calibration An exemplary calibration process according to some embodiments will now be described with reference to FIG. 24. The calibration process 2400 can use one or more pre-implantation information 2402, internal diagnostic information 2404, and external reference information 2406 as input to form or modify a conversion function 2408. The conversion function 2408 can be used to convert sensor data (e.g., in units of current or count) to an estimated analyte value 2410 (e.g., in units of analyte concentration). Information representing the estimated analyte value is then output 2412, such as being displayed on a user display, transmitted to an external device (e.g., an insulin pump, a PC computer, a mobile computing device, etc.), and / or further processed. The analyte can be, for example, glucose.

[0364] In process 2400, the pre-implantation information 2402 can mean information generated before implanting the currently calibrated sensor. The pre-implantation information 2402 can include any of the following types of information: - A predefined sensitivity profile, such as a predicted profile of changes in sensitivity over a period of the sensor, associated with the currently used (e.g., implanted) sensor; - A previously determined relationship between the output of a specific stimulus signal (e.g., an output representing the impedance, capacitance, or other electrical or chemical attribute of the sensor) and the sensor sensitivity (e.g., determined from previous in-vivo and / or in-vitro studies); - A previously determined relationship between the output of a specific stimulus signal (e.g., an output representing the impedance, capacitance, or other electrical or chemical attribute of the sensor) and the sensor temperature (e.g., determined from previous in-vivo and / or in-vitro studies); - Sensor data obtained from a previously implanted analyte concentration sensor; - A calibration code associated with the sensor being calibrated as described herein; - Patient-specific relationships between sensors and sensitivity, baseline, drift, impedance, impedance / temperature relationships (e.g., characteristics common to the patient, determined from prior studies of the patient or other patients); - Specific relationships to the sensor implantation site (abdomen, arm, etc.) (different sites may have different vascular densities); - Time since sensor manufacture (e.g., time the sensor was on the shelf, date the sensor was manufactured and / or shipped, time between when the sensor was manufactured and / or shipped and when it was implanted); and - Temperature, humidity, and exposure to external factors of the shelved sensor.

[0365] In process 2400, internal diagnostic information 2402 can mean information generated by the sensor system in which the implanted specimen sensor (whose data is calibrated) is used internally. Internal diagnostic information 2402 can include any of the following types of information: - Output of a stimulation signal of a sensor using any of the techniques of stimulation signals described herein (where the output of the stimulation signal can be obtained and processed in real time), e.g., the output of the stimulation signal representing the impedance of the sensor; - Sensor data representing the analyte concentration measured by the implanted sensor (real-time data using the currently implanted sensor and / or previously generated sensor data); - Measured temperature values using an implanted sensor or auxiliary sensor (such as a thermistor) placed at the same location as the implanted specimen sensor or separately from the implanted specimen sensor; - Sensor data from a multi-electrode sensor, e.g., one electrode of the sensor is designed to determine a baseline signal as described herein; - Sensor data generated by redundant sensors, where one or more of the redundant sensors are designed to be substantially the same as at least some, if not all, of the other redundant sensors (e.g., having the same sensor membrane type); - Sensor data generated by one or more auxiliary sensors having different modalities (such as optical, thermal, capacitive, etc.) and arranged at the same position as the analyte sensor or remotely from the analyte sensor; - The time since the sensor was implanted and / or since it was connected (e.g., physically or electronically) to the sensor electronics of the sensor system; - Data representing the pressure on the sensor / sensor system generated, for example, by a pressure sensor (e.g., to detect compression artifacts); - Data generated by an accelerometer (e.g., representing the movement / motion / activity of the recipient); - A certain amount of moisture ingress; and (e.g., representing the integrity of the moisture seal of the sensor system) - A certain amount of noise in the analyte concentration signal (which in some embodiments can be referred to as the residual between the raw signal and the filtered signal).

[0366] In process 2400, the external reference information 2402 can mean information generated from a source while the implanted analyte sensor (whose data is calibrated) is in use. The external reference information 2402 can be any of the following types of information: - Real-time and / or prior analyte concentration information obtained from a reference monitor (e.g., analyte concentration values obtained from a separate sensor, such as a fingerstick glucose meter); - The type / brand of the reference meter (different meters may have different biases / accuracies); - Information representing the carbohydrates consumed by the patient; - Information from a drug pen / pump such as insulin on board, insulin sensitivity, glucagon on board; - Glucagon sensitivity information; and - Information collected from population-based data (e.g., based on data collected from sensors having similar characteristics, such as sensors from the same lot).

[0367] Exemplary Sensor System Configuration Embodiments of the present invention are described above and below with reference to flowcharts of methods, apparatuses, and computer program products. It is understood here that each block of the flowchart diagrams and combinations of blocks of the flowchart diagrams can be implemented by the execution of computer program instructions. These computer program instructions can be loaded onto a computer or other programmable data processing machine (such as a controller, microcontroller, microprocessor, or others) in a sensor electronics system in order to produce a machine, and the instructions executed on the computer or other programmable data processing machine cause instructions for implementing the functions specified in the blocks of the flowchart. These computer program instructions can also be stored in a computer-readable memory that instructs the computer or other programmable data processing machine to function in a specific manner, and the instructions stored in the computer-readable memory are adapted to produce a product that includes instructions for executing the functions specified in the blocks of the flowchart. The computer program instructions can also be loaded onto a computer or other programmable data processing machine to generate a computer-executed process such that a series of steps for performing a function are executed on the computer or other programmable device, and the instructions executed on the computer or other programmable device are adapted to provide steps for implementing the functions specified in the blocks of the flowchart shown herein.

[0368] In some embodiments, a sensor system is provided that includes a continuous analyte sensor configured to continuously measure the concentration of an analyte (e.g., glucose) inside a recipient, and a sensor electronics module physically connected to the continuous analyte sensor while using the sensor to continuously measure the concentration of the analyte inside the recipient. In one embodiment, the sensor electronics module includes electronic components configured to process a data stream associated with the analyte concentration measured by the continuous analyte sensor, and processes the sensor data to generate displayable sensor information including, for example, raw sensor data, transformed sensor data, and / or any other sensor data. The sensor electronics module can include electronic components configured to process a data stream associated with the analyte concentration measured by the continuous analyte sensor, and processes the sensor data to generate displayable sensor information including, for example, raw sensor data, transformed sensor data, and / or any other sensor data. The sensor electronics module can include a processor and computer program instructions for performing the processes described herein, including the functions specified in the blocks of the flowcharts shown herein.

[0369] In some embodiments, the receiver, which may also be referred to as a display device, communicates with a sensor electronics module (e.g., via wired or wireless communication). The receiver can be a portable device for a specific purpose or a general-purpose device such as a personal computer, a smartphone, a tablet computer, etc. In one embodiment, the receiver can communicate with the sensor electronics module for receiving sensor data such as raw and / or displayable data, and includes a processing module for processing and / or displaying the received sensor data. The receiver can also include an input module configured to receive input from a user via a keyboard or a touch-sensitive display screen, such as calibration codes, reference specimen values, and any other information described in this specification, and can be configured to receive information from external devices such as insulin pumps and reference meters via wired or wireless data communication. This input can be processed alone or in combination with the information received from the sensor electronics module. The processing module of the receiver can include a processor and computer program instructions for performing any of the processes described in this specification, including the functions specified in the blocks of the flowcharts shown in this specification.

[0370] Examples The embodiments will be described in more detail in the following examples, which are provided as examples and are not intended to limit the invention in any way. As disclosed in the present specification, a study was conducted using a Gamry potentiostat system to analyze the complex impedance of a glucose sensor placed in a buffer solution. This Gamry potentiostat system is commercially available from Gamry under the model name Ref600. The buffer solution is a modified Isolyte (isotonic electrolyte preparation) having a known glucose concentration. In many examples, the glucose concentration is about 100 mg / dL. By testing the system at known impedances, it was found that the impedance measurement error in the examples is about 1 - 5%.

[0371] Some of the following examples are run on a laboratory bench, but the sensors under test are configured for in vivo use to continuously or substantially continuously measure the glucose concentration of a recipient.

[0372] The analyte sensors used in the following examples were selected from different types of sensors. The sensors under test include sensors selected from different sensor lots, and the sensors from the first lot were manufactured in different ways and in different states, and different lots of sensors may result in different sensitivity profiles. Further, some of the sensors studied in these examples are configured to be placed in the recipient's transdermal tissue to measure the recipient's glucose concentration, while on the other hand, other sensors are configured to measure the recipient's venous blood glucose concentration. In the following experiments, sensors intended to be used to measure venous blood glucose concentration can be referred to as "IVBG sensor type", and sensors intended to measure the blood glucose concentration in the recipient's transdermal tissue can be referred to as "transdermal sensor type".

[0373] [Example 1] Relationship between Sensitivity and Impedance Example 1 shows the relationship between the sensitivity of the sensor and the impedance of the sensor. In this example, the IVBG sensor was connected to a Gamry potentiostat system and placed in a buffered solution of modified isolite having a glucose concentration of 100 mg / dL. The temperature during the experiment was 37 °C. Impedance spectra were acquired at fixed intervals of time. The impedance spectra analyzed in this experiment ranged from 1 Hz to 100 kHz, and the measured values of sensor impedance and sensor sensitivity were acquired at 15-minute intervals over a period of approximately 1200 minutes.

[0374] Referring now to FIG. 25, the graph shows the sensitivity of the sensor and the absolute value of the impedance based on an input signal at a frequency of 1 kHz. Data point 2502 shows the measured values of sensor sensitivity over a period of 1200 minutes (20 hours), where t = 0 corresponds to the time when the sensor was first placed in the buffer solution. Data point 1104 represents the impedance values measured over the same period.

[0375] The sensitivity and impedance values in FIG. 25 appear to have an inverse correlation. That is, the sensitivity initially increases rapidly, but eventually the rate of increase slows down and levels off, while the impedance initially decreases rapidly, but eventually the rate of decrease gradually slows down and levels off. Without wishing to be bound by theory, the initial increase in sensitivity and decrease in impedance are thought to be due to sensor conditioning.

[0376] FIG. 26 is a plot of the data in FIG. 25, but the sensitivity and impedance are in terms of the rate of increase or decrease per hour instead of the absolute value. As can be seen, the relative changes per hour in sensitivity 2602 and impedance 2604 also appear to have an inverse correlation.

[0377] [Example 2] Compensation for Past Sensitivity Drift Using Impedance Figure 27 is a plot of sensitivity and impedance points measured over various periods using seven different sensors, sensors A - G. Sensors A - G are transcutaneous type sensors, but are selected from several different sensor lots. Thus, although sensors A - G are all transcutaneous sensors, sensors from different lots may have slightly different manufacturing methods or be under slightly different conditions, resulting in sensors from different lots showing different sensitivity profiles. In this example, sensors A and D are selected from the first lot, sensor B is selected from the second lot, and sensors C, E, F, and G are selected from the third lot.

[0378] Regarding Figure 27, the plotted data points are the sensitivity and impedance values of each of the sensors A - G. Since the sensitivity of each of the sensors A - G gradually increases over time, the rightmost point among the plotted data points of each sensor tends to correspond to the value measured around t = 0, and the leftmost point tends to correspond to the value around t = 24 hours.

[0379] As can be seen from Figure 27, the impedance and sensitivity values of each of the sensors A - G have an essentially linear relationship, with the sensitivity gradually increasing and the impedance decreasing over time. The data obtained from all seven sensors A - G generally follows this linear relationship, but the data points of each sensor may be shifted compared to the data points of other sensors. In other words, the initial impedance and sensitivity values of each sensor may be different, but the changes in sensitivity and impedance of each sensor change at the same linear rate.

[0380] Figure 28 is a graph further showing the linear correlation between the impedance and sensitivity of sensors A - G. The graph in Figure 28 is based on the same data used in Figure 27, but the data is graphed as rates of increase and decrease rather than absolute values. As can be seen from Figure 28, sensors A - G appear to show very similar correspondence between the change in impedance and the change in sensitivity.

[0381] To model the relationship between the change in sensitivity and the change in impedance value of the sensor data generated by sensors A to G, an evaluation algorithm function executed by a computer can be used. As described above regarding forming the evaluation curve of the sensitivity profile, the evaluation algorithm function applies a curve fitting technique that regressively fits a curve to the data points by adjusting the function (for example, by adjusting the constants of the function) until an optimal fit for the available data points is obtained. Additionally or alternatively, this relationship can be modeled in a lookup table stored in computer memory.

[0382] Adding further to FIG. 28, an evaluation curve 2802 of the combined sensor data of sensors A to G is plotted. Although the curve in FIG. 28 is a straight line, it may be other types of curves depending on the relationship between the impedance and sensitivity of the sensor. As discussed in this specification, curve 2802 can be used to compensate for the drift of sensor sensitivity.

[0383] FIGS. 29 and 30 show the compensation of sensor data obtained by the same sensors used to extract the evaluation curve 2802. The measurements in FIGS. 29 and 30 were obtained at 37°C. (Note that, as discussed above with reference to FIGS. 27 and 28, the evaluation curve 2802 was extracted based on sensor measurements obtained at 37°C) FIG. 29 is a plot of uncompensated measurements using sensors A to G. FIG. 30 is a plot of the percent estimated sensitivity error of the compensated measurements using the relationship between sensitivity and impedance based on the evaluation curve 2802. The mean absolute relative difference (MARD) of the uncompensated sensor data is 21.8%. The MARD of the compensated data is 1.8%, which represents a significant improvement over the uncompensated data.

[0384] Figures 31 and 32 are plots of the percent sensitivity error of uncompensated data and compensated data, respectively. The data in Figures 31 and 32 are based on measurements using sensors H - L. Sensors H, J, K, and L were selected from the same lot as sensors C, F, and G described with reference to Figure 27, and sensor I was selected from the same lot as sensors A and D described with reference to Figure 27. Further, the measurements plotted in Figures 31 and 32 were taken at 25°C instead of 37°C. However, the evaluation curve 2802 derived from sensors A - G was used to compensate the data measured by sensors H - L (note that the evaluation curve 2802 is based on measurements taken at 37°C). The MARD of the uncompensated data is 21.9%, which is approximately the same as the 21.8% MARD calculated for the sensor data in Figure 29. The MARD of the compensated data in Figure 18 is 4.4%, which is slightly higher than but close to the MARD of the compensated data in Figure 30 and much smaller than the uncompensated MARD.

[0385] Figures 33 and 34 are graphs of the percent sensitivity error of uncompensated data and compensated data, respectively. The data in Figures 33 and 34 are based on data obtained using sensors M - Q. Sensors M, O, P, and Q were selected from the same lot as sensors C, F, and G described with reference to Figure 27, and sensor N was selected from the same lot as sensors A and D described with reference to Figure 27. The measurements of sensors M - Q were taken at 42°C. The evaluation curve 2802 derived from sensors A - G was also used to compensate the data in Figure 34. Here, the MARD of the uncompensated data is 13.1%, and the MARD of the compensated data is 4.6%.

[0386] Therefore, Example 2 shows that the relationship between the change in sensitivity and the change in impedance determined at the first temperature can be used to compensate for the sensitivity drift at a temperature different from the first temperature.

[0387] [Example 3] Predictive Calibration of Sensor Data Using Impedance Measurements Example 5 relates to predictive calibration. Further, in this experiment, the calibration of the sensor data is based on the relationship between the change in sensitivity and the change in impedance derived in advance from sensors from different sensor lots. That is, in Example 3, the evaluation curve 2802 was used to compensate for the data obtained using sensors R to U respectively selected from the fourth sensor lot, but the fourth sensor lot was not included in the group of sensors used to derive the evaluation curve 2802. Example 5 shows that data can be calibrated using the relationship between the change in sensitivity and the change in impedance derived from sensors of a type different from the type of sensor to be calibrated. This indicates that it is not necessary to use the factory calibration code of the sensor to compensate for sensitivity drift.

[0388] Figures 35 and 36 show the predictive calibration of the sensor data obtained from sensors R to U. Figure 35 is a plot of percent sensitivity change versus 30 minutes over approximately 1400 minutes for each sensor. Figure 36 shows the percent estimated sensitivity error of the compensated data. The MARD of the uncompensated sensor data is 24.8% (the MARD of the uncompensated data in the example of Figure 15 was 21.8%), and the MARD of the compensated sensor data is 6.6% (the MARD of the compensated data in the example of Figure 16 was 1.8%).

[0389] Here, the predictive calibration of the sensor will be discussed with reference to FIGS. 37 to 39. Here, the sensitivity and impedance data were collected from sensors V to X, Z, and AA having films formed by the dipping method, and sensor Y having a film formed by the spraying method. Referring to FIG. 37, the evaluation curve 3702 is calculated based on the sensitivity and impedance data from all six sensors V to Z and AA (i.e., using sensor data obtained from both sensors having dipping and spraying method films). FIG. 39 is a graph of percent sensitivity change versus 60 minutes for the uncompensated data of each of the six sensors. FIG. 39 is a graph of the percent estimated sensitivity error of the data of all six sensors after being compensated using the evaluation curve 3702 discussed above with respect to FIG. 37. The MARD of the uncompensated data is 25.3%, and the MARD of the compensated data is 5.2%.

[0390] Therefore, Example 5 shows that using the relationship between the change in sensitivity and the change in impedance derived from sensors selected from different lots can significantly compensate for the drift of sensor sensitivity, even more so than the calibrated sensor. It should be noted here that the curve 3702 is a straight line. What can be considered here is that a non-linear fit or correlation can be used instead, which may yield better results.

[0391] [Example 4] Effect of Temperature FIG. 40 shows the relationship between the impedance and sensitivity of the sensor and temperature. Point 4002 is the sensitivity value of the sensor measured over a three-day period, and point 4004 is the impedance value of the sensor measured over the same period. In Example 4, the sensor is a transdermal type sensor. As shown in FIG. 40, the temperature is first set and maintained at 37° C., then increased to 45° C., and finally decreased to 25° C.

[0392] As shown in FIG. 40, both the sensitivity and impedance of the sensor appear to have an inverse proportional relationship with the change in temperature.

[0393] FIG. 41 is a plot of the measured sensitivity values against the measured impedance values of FIG. 40. FIG. 41 shows the points measured during the sensor conditioning in diamonds and the points measured after conditioning in squares.

[0394] [Example 5] Temperature Compensation FIG. 42 shows the effect of temperature after sensor conditioning on the compensation of analyte concentration data measured by the sensor of Example 4. Here, the relationship between impedance and temperature was used to compensate the sensor data. In this example, the relationship is based on an evaluation curve derived from the data of FIG. 41.

[0395] The relationship between the sensor sensitivity and different temperatures can subsequently be mathematically modeled (e.g., by fitting a mathematical curve to the data using one of the modeling techniques used in this specification), and this mathematical model can subsequently be used to compensate for the effect of temperature on the sensor sensitivity. That is, the sensitivity of the sensor (which is affected by the temperature of the sensor) can be determined based on the measured impedance of the sensor to which the mathematical curve is applied. The sensor data can subsequently be converted into glucose values estimated based on the determined sensor sensitivity.

[0396] Adding further to FIG. 42, the MARD of the uncompensated data was calculated to be 9.3% and the MARD of the compensated data was calculated to be 2.8%.

[0397] [Example 6] Detection of Moisture Ingress Example 6 involves detecting moisture ingress within the components of the sensor electronics used to drive the sensor. In this example, an input signal with a frequency ranging from 100 Hz to 1 kHz is applied to the sensor. The response of the signal is subsequently measured over the frequency range, and both changes in impedance and phase are derived therefrom. Next, the contacts typically placed inside the sealed portion of the sensor electronics module are wetted. The frequency of the input signal is changed between 100 Hz and 1000 Hz. The response is measured once again, and both changes in impedance and phase are derived.

[0398] In the first experiment, the contacts were wetted to the point where they caused significant failure of the sensor. The current of the sensor increased from 2.3 nA when dry to 36 nA when wet. In the second experiment, the contacts were only slightly wetted, and the sensor current increased from 2.3 nA when dry to 6 nA when wet.

[0399] Figure 43 is a graph of the changes in impedance and phase for the first experiment, where the contacts were wetted to the point where they caused significant failure of the sensor system. Curve 4302 and curve 4304 are the impedance and phase values of the sensor before wetting the contacts, respectively. Curve 4306 and curve 4308 are the impedance and phase values of the sensor after wetting the contact surface, respectively. As shown in Figure 43, the impedance of the dry curve 4302 and the wet curve 4306 differ significantly at around 1000 Hz, and the phase of the dry curve 4304 and the wet curve 4308 differ significantly at approximately 100 Hz.

[0400] Figure 44 is a graph of the impedance and phase changes in the second experiment, where the contacts are only slightly wetted. Curve 4402 and curve 4404 are the impedance and phase before wetting the contacts of the sensor, respectively. Curve 4406 and curve 4408 are the impedance and phase of the sensor after wetting the contacts, respectively. As shown in Figure 44, the impedance of the dry curve 4402 and the wet curve 4406 differ significantly at about 100 Hz, and the phase of the dry curve 4404 and the wet curve 4408 differ significantly at about 1 kHz.

[0401] [Example 7] Detection of Membrane Damage Example 7 involves the detection of damage to the sensor film using impedance measurements. In this example, the impedance of the specimen sensor was measured over a frequency range from 100 to 1 kHz. A portion of the sensor film was subsequently cut using a razor blade to cause film damage. The impedance of the sensor was measured again over the same frequency range. To determine whether impedance can be used to detect film damage, the impedance measurement values of the sensor before cutting the film were subsequently compared with the measurement values of the sensor after cutting the film.

[0402] Figure 45 is a graph of the sensor measurement values before and after film damage. Curve 4502 and curve 4504 are the impedance and phase of the sensor before film damage, respectively, and curve 4506 and curve 4508 are the impedance and phase of the sensor after film damage, respectively. Both the relationship of impedance and phase appear to change at a frequency of about 1 kHz. This relationship can subsequently be used to detect film damage.

[0403] [Example 8] Study Using FFT to Calculate Impedance FIG. 46 is a graph plotting the impedance calculated using the Fast Fourier Transform (FFT) and the impedance measured using Gamry for comparison. FIG. 46 shows the FFT and the corresponding Gamry measurements for input data at 30 minutes, 1.5 hours, 3 hours, and 13 hours. The FFT data substantially followed the Gamry data up to a frequency of about 1 kHz. The discrepancy after 1 kHz in FIG. 33 is thought to be due to known limitations of the system used to calculate the FFT. Thus, the use of the FFT is considered to be able to provide accurate impedance data even beyond the 1 kHz spectrum, thereby providing a good match to the impedance measured using Gamry.

[0404] [Example 9] Compensation for Sensitivity Changes in Vivo FIGS. 47 to 49 relate to the compensation of sensitivity changes inside a human using an in-vivo glucose sensor.

[0405] FIG. 47 is a graph showing in-vivo data of sensor impedance and sensitivity over a 25-hour period of a subcutaneous continuous glucose sensor (referred to herein as a CGM sensor). FIG. 47 is similar to FIG. 25, but uses in-vivo data instead of in-vitro data. The sensitivity data was generated using data obtained from a blood glucose finger stick detection device and raw sensor current measurements using the CGM sensor (i.e., the raw CGM sensor current in counts was divided by the meter glucose value corresponding to the time in mg / dL to generate the sensitivity value). The impedance data was generated by applying a step voltage to the CGM sensor and calculating the impedance based on the peak current of the response, as discussed herein with reference to FIG. 13. As shown in FIG. 47, the impedance and sensitivity data generally appear to follow a profile similar to the in-vitro data of FIG. 25.

[0406] Figure 48 is a graph of glucose values estimated using data generated by a CGM sensor before and after sensitivity compensation using the impedance measurement of Figure 47. The compensated and uncompensated data were calibrated once at the one hour mark using measurements from a blood glucose reference meter. Compensation was performed in a manner similar to that discussed above for the in vitro study of Figure 30. Figure 48 also shows the recipient's blood glucose reference measurements. Based on the data in Figure 48, the compensated CGM sensor data appears to correspond more closely to the reference measurement data than the uncompensated CGM sensor data.

[0407] Figure 49 is an accuracy plot showing the difference between uncompensated and compensated CGM sensor data and finger stick meter reference data, where the reference data corresponds to the zero line of the chart. The MARD of the uncompensated CGM sensor data is 28.5% and the MARD of the compensated CGM sensor data is 7.8%. Thus, this compensation appears to improve the accuracy of the CGM sensor data.

[0408] [Example 10] Comparison between Linear Sensitivity-Impedance Correlation and Nonlinear Sensitivity-Impedance Correlation Laboratory experiments have shown an inverse relationship between changes in sensor sensitivity (drift) and changes in impedance. Based on the measured impedance, the drift in sensor sensitivity can be compensated using a linear or non-linear correlation between sensor sensitivity and impedance.

[0409] The linear correlation can be expressed as an equation ΔS = a*ΔI + b, where a and b can be predetermined coefficients determined from prior testing of similar sensors. The non-linear correlation is ΔS = (a*log(t) + b)*ΔI It can be expressed as. Here, a and b are constants determined from the prior tests of similar sensors. The prior tests of similar sensors are performed using in vitro bench tests, and the constants are derived using conventional computer plotting techniques in this experiment.

[0410] The linear and non-linear equations are derived from the relationship between the change in sensitivity and the change in impedance at time t from the last calibration. The percentage change in sensitivity at time t is expressed as follows. △S = % change in sensitivity = (sensitivity at time t - Sc) / Sc * 100% Here, Sc is the sensitivity determined in calibration, and t is the time from the last calibration.

[0411] The change in sensor impedance from time t is expressed as follows. △I = % impedance change = (impedance at time t - Ic) / Ic * 100% Here, Ic is the impedance at calibration.

[0412] Based on the in vitro test data, the change in sensitivity (y-axis) versus the change in impedance (x-axis) was plotted, and conventional computer plotting software was used to calculate the linear correlation to determine the best linear fit of the data. In this experiment, the best linear fit was △S = -4.807 * △I - 0.742 It was. This best linear fit was used as the correlation between linear sensitivity and impedance.

[0413] To plot the change in sensitivity ΔS / ΔI (y-axis) against log(t) for the change in impedance, conventional computer plotting software was used to calculate the non-linear correlation. The best linear fit of the plotted data was then determined using the plotting software to yield ΔS / ΔI = a*log(t) + b, from which the change in sensitivity was derived: ΔS = (a*log(t) + b)*ΔI. In this experiment, the plotting software yielded ΔS / ΔI = -1.175*log(t) - 1.233. From this equation, ΔS was derived: ΔS = (-1.175*log(t) - 1.233)*ΔI.

[0414] To correct for changes in the sensitivity of the sensor using the impedance measurements of the sensor, a linear or non-linear correlation can subsequently be applied.

[0415] Figure 50 shows a process 5000 for correcting the estimated glucose values used in this experiment. Process 5000 starts at block 5002, where sensor insertion is at time t = 0. Process 5000 then branches into two.

[0416] The first branch starts at block 5004, where sensor data is generated using a continuous glucose sensor in the form of current or counts. The first branch then proceeds to decision block 5006, where process 5000 determines whether calibration is required. In this experiment, it is determined that calibration is required at 1 hour and 24 hours after sensor insertion. If it is determined that calibration is not required, the first branch essentially ends. On the other hand, if it is determined that calibration is required, process 5000 obtains reference measurements using a finger stick meter at blocks 5008 and 5010. The sensor data corresponding to the reference measurements (at time Tc) is determined at block 5012 and used with the reference data to calculate the sensor sensitivity (Sc) at block 5014.

[0417] The second branch of process 5000 begins by measuring the impedance of the sensor at block 5016. The impedance is measured using the peak current technique discussed in this application with reference to FIG. 13. Next, at decision block 5018, it is determined whether calibration is necessary. In this experiment, this is the same determination made at decision block 5006. If it is determined that calibration is necessary, then the impedance measured at block 5016 is flagged and stored as impedance Ic (impedance calibration) at block 5020. If calibration is not necessary, process 5000 skips block 5020 and determines the change in impedance from the impedance flagged as impedance Ic at block 5022, which change is the difference between the impedance measured at block 5016 and impedance Ic in this experiment. A compensation algorithm (using a linear or non-linear compensation algorithm) is subsequently used at block 5024 to calculate the change in sensitivity at block 5026 based on the change in impedance determined at block 5022.

[0418] The first and second branches of process 5000 converge at block 5028 and a modified sensitivity is determined. This modified sensitivity is subsequently used to convert sensor data in units of current or count to an estimated glucose value in units of glucose concentration.

[0419] Process 5000 is repeated for each sensor data point that is converted to an estimated glucose value.

[0420] In this experiment, the improvement of sensor performance by impedance compensation is demonstrated below. The sensor was tested using a continuous glucose sensor placed in sheep serum for two days. Sensor sensitivity was calculated at 1 hour and 24 hours. In FIG. 51, the sensor sensitivity obtained during calibration (1 hour or 24 hours) was used throughout the day without any correction. In FIG. 52, the sensitivity was corrected using linear impedance correction according to the process shown in FIG. 50. In FIG. 53, the sensitivity was adjusted by non-linear sensitivity impedance correlation according to the process shown in FIG. 50. Both linear and non-linear corrections demonstrated improved sensor performance, but the non-linear correction yielded better results.

[0421] Some embodiments disclosed herein apply a stimulation signal continuously or repeatedly during a continuous sensor session, or use and extrapolate information from outputs associated with the stimulation, such as the use of peak current measurements, EIS, etc. Although certain electrochemical analysis techniques are described herein, it is understood that many other techniques can be used instead of detecting the characteristics of the sensors described herein, such as voltammetry, chronopotentiometry, current or potential step techniques, differential chronopotentiometry, oxygen uptake rate measurements, potential / current sweep or pulse methods, etc.

[0422] This disclosure, which has been illustrated and described in detail in the drawings and foregoing description, is considered to be illustrative or exemplary and not restrictive. This disclosure is not limited to the disclosed embodiments. Changes to the disclosed embodiments can be understood and accomplished by those skilled in the art from a study of the drawings, the disclosure, and the appended claims when practicing the claimed disclosure.

[0423] All references cited in this specification are hereby incorporated by reference in their entirety. To the extent that any incorporated publication, patent, or patent application contradicts the disclosure contained herein, this specification is intended to supersede and / or take precedence over any such conflicting material.

[0424] Unless otherwise defined, all terms (including technical and scientific terms) shall have their ordinary and customary meaning to those of ordinary skill in the art and shall not be limited to a special or customized meaning unless expressly so defined in this specification. It should be noted here that the use of a particular term, when describing a certain feature or aspect of this disclosure, should not be taken to mean that the term is redefined restrictively in this specification so as to include characteristics specific to the feature or aspect of this disclosure to which the term is associated. The terms and expressions used in this application, and their variations, should be construed as open-ended rather than restrictive, unless expressly stated otherwise, especially in the appended claims. As an example of the foregoing, the term "comprising" should be read to mean "comprising without limitation", "including but not limited to", etc.; "comprising" as used in this specification is synonymous with "including", "containing", "characterized by" and is inclusive or open-ended and does not exclude additional, unrecited elements or method steps; the term "having" should be construed as "having at least"; the term "including" should be construed as "including but not limited to"; the term "example" is a term used to provide an illustrative instance of an item and is not an exhaustive or limiting list; the adjectives "known", "normal", "standard" and terms of similar meaning should not be construed as limiting the item described to items available during a given period or at a given point in time, but rather should be read to include known, normal or standard techniques that are or will be available or known at any time, present or future; and the terms "preferably", "preferred", "desired" or "desirable" and terms of similar meaning should not be understood to mean that a certain feature is critical, essential or important to the structure or function of the invention, but rather are intended merely to emphasize alternative or additional features that may or may not be utilized in a particular embodiment of the invention.Similarly, a group of items joined by the conjunction "and" should not be read as requiring that one of each and every one of those items be grouped and present, but rather should be read as "and / or" unless explicitly stated otherwise. Similarly, a group of items joined by the conjunction "or" should not be read as requiring mutual exclusivity within the group, but rather should be read as "and / or" unless explicitly stated otherwise.

[0425] Where a range of values is provided, it is understood that the upper and lower limits, and each value falling therebetween within the range of the embodiment, are included within the scope of the embodiment.

[0426] Regarding the use of substantially plural and / or singular terms in the present specification, one of ordinary skill in the art can translate from plural to singular and / or from singular to plural as appropriate for the context and / or application. Various singular / plural substitutions can be explicitly stated in the present specification for clarity. The indefinite article "a" or "an" does not exclude a plurality. A single processor or other unit can perform the functions of several items detailed in the claims. The mere fact that certain means are detailed in mutually different dependent claims does not indicate that a combination of these means cannot be used effectively. Any reference signs in the claims are not to be construed as limiting the scope.

[0427] It will be further understood by those skilled in the art that where a specific number of the introduced claim recitations are intended, such intent is clearly recited in the claim and absent such recitation no such intent exists. For example, by way of illustration, the appended claims below may include the use of introductory phrases that introduce claim recitations of "at least one" and "one or more." However, the use of such phrases should not be construed to limit any particular claim that includes such a recited claim introduced by such phrases to embodiments that include only such recitation, even where the same claim includes an introductory phrase of "one or more" or "at least one" and an indefinite article such as "a" or "an" (e.g., "a" and / or "an" are typically construed to mean at least one or one or more); the same applies to the use of definite articles used to introduce claim recitations. Additionally, those skilled in the art will understand that even where a specific number of the introduced claim recitations is clearly recited, such recitation should typically be construed to mean the recited number "at least" (e.g., a bare recitation of "two" without other modification typically means a recitation of "at least two" or "two or more"). Still further, in instances where notations similar to "at least one of A, B, and C, among others" are used, generally such constructs are intended to be understood by those skilled in the art in the sense that they convey (e.g., a system having at least one of A, B, and C includes, but is not limited to, a system having only A, only B, only C, A and B together, A and C together, B and C together, and / or A, B, and C together, among others).In cases where a notation similar to "at least one of A, B, or C, and others" is used, generally, such a configuration is intended to mean what a person skilled in the art would understand from such notation (for example, "a system having at least one of A, B, or C" includes, but is not limited to, a system having only A, only B, only C, A and B together, A and C together, B and C together, and / or A, B, and C together, and others). Further, what a person skilled in the art should understand is that substantially any disjunctive word and / or phrase indicates two or more alternative terms, and regardless of whether it is in the description, the claims, or the drawings, it should be understood to encompass the possibility of including one of the terms, any of the terms, or both of the terms. For example, the phrase "A or B" is understood to include the possibility of "A" or "B" or "A and B".

[0428] All numbers representing amounts of components, reaction conditions, and others used in the specification should be understood to be modified by the term "about" in all cases. Accordingly, unless otherwise specified, the numerical parameters described in the specification of this application are approximate values that can vary depending on the desired attribute to be obtained. At least, without attempting to limit the application of the doctrine of equivalents to the claims in any application claiming priority to this application, each numerical parameter should be interpreted in consideration of the number of significant digits and the usual rounding approaches.

[0429] Furthermore, although the foregoing has been described in some detail by way of illustration and example for clarity and understanding, it will be apparent to those skilled in the art that some changes and modifications can be made. Accordingly, the description and examples are not to be construed as limiting the scope of the invention to the specific embodiments and examples described in the specification of this application. Rather, they cover all modifications and variations that fall within the true scope and spirit of the invention.

Claims

1. 1. A method of calibrating sensor data generated by a continuous analyte sensor, comprising: generating sensor data using the continuous analyte sensor; iteratively determining, by an electronic device, a sensitivity value of the continuous analyte sensor as a function of time by applying a priori information including sensor sensitivity information; and calibrating the sensor data based at least in part on the determined sensitivity value; The method includes:

2. The method of claim 1 , wherein calibrating the sensor data is performed repeatedly throughout substantially an entire sensor session.

3. 2. The method of claim 1, wherein iteratively determining sensitivity values ​​is performed periodically or at irregular intervals as determined by the a priori information.

4. The method of claim 1 , wherein iteratively determining the sensitivity value is performed substantially throughout an entire sensor session.

5. The method of claim 1 , wherein determining the sensitivity value is performed substantially in real time.

6. The method of claim 1 , wherein the a priori information is associated with at least one predetermined sensitivity value that is associated with a predetermined time after a sensor session begins.

7. 7. The method of claim 6, wherein the at least one predetermined sensitivity value is associated with a correlation between sensitivity determined from in vitro analyte concentration measurements and sensitivity determined from in vivo analyte concentration measurements at a given time.

8. The method of claim 1 , wherein the a priori information is associated with a predetermined sensitivity function that uses time as an input.

9. The method of claim 8 , wherein the time corresponds to a time after a sensor session begins.

10. 9. The method of claim 8, wherein the time corresponds to at least one of a time of manufacture or a time since manufacture.

11. 10. The method of claim 1, wherein the sensitivity value of the continuous analyte sensor is also a function of at least one other parameter.

12. 12. The method of claim 11, wherein the at least one other parameter is selected from the group consisting of temperature, pH, level or duration of hydration, curing conditions, analyte concentration of a fluid surrounding the continuous analyte sensor during sensor activation, and combinations thereof.

13. The method of claim 1 , wherein calibrating the sensor data is performed without the use of reference blood glucose data.

14. the electronic device is configured to provide a level of accuracy corresponding to a mean absolute relative difference of no more than about 10% over a sensor session of at least about 3 days; 2. The method of claim 1, wherein the reference measurement associated with the calculation of the mean absolute relative difference is determined by analysis of blood.

15. The method of claim 14 , wherein the sensor session is at least about four days.

16. The method of claim 14 , wherein the sensor session is at least about 5 days.

17. The method of claim 14 , wherein the sensor session is at least about 6 days.

18. The method of claim 14 , wherein the sensor session is at least about 7 days.

19. The method of claim 14 , wherein the sensor session is at least about 10 days.

20. 15. The method of claim 14, wherein the average absolute relative difference does not exceed about 7% over the sensor session.

21. 15. The method of claim 14, wherein the average absolute relative difference does not exceed about 5% over the sensor session.

22. 15. The method of claim 14, wherein the average absolute relative difference does not exceed about 3% over the sensor session.

23. The method of claim 1 , wherein the a priori information is associated with a calibration code.

24. The method of claim 1 , wherein the a priori sensitivity information is stored in the sensor electronics prior to using the sensor.

25. 1. A method of calibrating sensor data generated by a continuous analyte sensor, comprising: generating sensor data using a continuous analyte sensor; determining, by an electronic device, a plurality of distinct sensitivity values ​​of the continuous analyte sensor as a function of time and as a function of sensitivity information associated with a priori information; and calibrating the sensor data based at least in part on at least one of the plurality of different sensitivity values. The method includes:

26. 26. The method of claim 25, wherein calibrating the continuous analyte sensor is performed repeatedly throughout substantially an entire sensor session.

27. 26. The method of claim 25, wherein the plurality of different sensitivity values ​​are stored in a look-up table in a computer memory.

28. 26. The method of claim 25, wherein determining the plurality of different sensitivity values ​​is performed only once throughout substantially an entire sensor session.

29. 26. The method of claim 25, wherein the a priori information is associated with at least one predetermined sensitivity value that is associated with a predetermined time after a sensor session begins.

30. 30. The method of claim 29, wherein the at least one predetermined sensitivity value is associated with a correlation between sensitivity determined from in vitro analyte concentration measurements and sensitivity determined from in vivo analyte concentration measurements at a given time.

31. 26. The method of claim 25, wherein the a priori information is associated with a predetermined sensitivity function that uses time as an input.

32. 26. The method of claim 25, wherein the time corresponds to a time after a sensor session begins.

33. 26. The method of claim 25, wherein the time corresponds to a time at or since the time of manufacture.

34. 26. The method of claim 25, wherein the plurality of sensitivity values ​​is also a function of at least one parameter other than time.

35. 26. The method of claim 25, wherein the at least one other parameter is selected from the group consisting of temperature, pH, level or duration of hydration, curing conditions, analyte concentration of a fluid surrounding the continuous analyte sensor during startup of the sensor, and combinations thereof.

36. 26. The method of claim 25, wherein calibrating the continuous analyte sensor is performed without the use of reference blood glucose data.

37. the electronic device is configured to provide a level of accuracy corresponding to a mean absolute relative difference of no more than about 10% over a sensor session of at least about 3 days; 26. The method of claim 25, wherein the reference measurement associated with the calculation of the mean absolute relative difference is determined by analysis of blood.

38. 38. The method of claim 37, wherein the sensor session is at least about four days.

39. 40. The method of claim 37, wherein the sensor session is at least about 5 days.

40. 38. The method of claim 37, wherein the sensor session is at least about 6 days.

41. 38. The method of claim 37, wherein the sensor session is at least about 7 days.

42. 40. The method of claim 37, wherein the sensor session is at least about 10 days.

43. 38. The method of claim 37, wherein the average absolute relative difference does not exceed about 7% over the sensor session.

44. 40. The method of claim 37, wherein the average absolute relative difference does not exceed about 5% over the sensor session.

45. 40. The method of claim 37, wherein the average absolute relative difference does not exceed about 3% over the sensor session.

46. 38. The method of claim 37, wherein the a priori information is associated with a calibration code.

47. 1. A method for processing data from a continuous analyte sensor, comprising: receiving, by the electronic device, sensor data from the continuous analyte sensor, the sensor data including at least one sensor data point; iteratively determining a sensitivity value of the continuous analyte sensor as a function of time and at least one predetermined sensitivity value associated with a predetermined time after initiating a sensor session; forming a conversion function based at least in part on said sensitivity values; and determining an analyte output value by applying the transformation function to the at least one sensor data point. The method includes:

48. 48. The method of claim 47, wherein the iteratively determining the sensitivity of the continuous analyte sensor is performed continuously.

49. 48. The method of claim 47, wherein the iteratively determining the sensitivities is performed substantially in real time.

50. 48. The method of claim 47, further comprising determining a baseline for the continuous analyte sensor, wherein the conversion function is based at least in part on the baseline.

51. 51. The method of claim 50, wherein determining the continuous analyte sensor baseline is performed continuously.

52. 52. The method of claim 51, wherein determining the sensitivity of the continuous analyte sensor and determining the baseline of the analyte sensor are performed substantially simultaneously.

53. 48. The method of claim 47, wherein the at least one predetermined sensitivity value is set at a manufacturing facility for the continuous analyte sensor.

54. accepting at least one calibration code; and 48. The method of claim 47, further comprising applying at least one calibration code to the electronic device at a predetermined time after the sensor session begins.

55. 55. The method of claim 54, wherein repeatedly determining sensitivity is performed at regular intervals or at irregular intervals as defined by the at least one calibration code.

56. 56. The method of claim 55, wherein the at least one calibration code is associated with at least one predetermined sensitivity.

57. 56. The method of claim 55, wherein the at least one calibration code is associated with a predetermined sensitivity function that uses time in function of time as an input.

58. 48. The method of claim 47, wherein the time corresponds to a time after starting a sensor session.

59. 48. The method of claim 47, wherein the time corresponds to a time at or since the time of manufacture.

60. 48. The method of claim 47, wherein the sensitivity value of the continuous analyte sensor is also a function of at least one other parameter.

61. 61. The method of claim 60, wherein the at least one other parameter is selected from the group consisting of temperature, pH, level or duration of hydration, curing conditions, analyte concentration of a fluid surrounding the continuous analyte sensor during startup of the sensor, and combinations thereof.

62. 1. A method of calibrating a continuous analyte sensor, comprising: accepting sensor data from a continuous analyte sensor; forming or accepting a predetermined sensitivity profile corresponding to a change in sensor sensitivity to the analyte substantially throughout the sensor session as a function of at least one predetermined sensitivity value associated with a predetermined time after initiating the sensor session; and applying said sensitivity profile by an electronic device in a real-time calibration; The method includes:

63. 63. The method of claim 62, wherein the at least one predetermined sensitivity value, the predetermined sensitivity profile, or both are established at a manufacturing facility for the continuous analyte sensor.

64. accepting at least one calibration code; and 63. The method of claim 62, further comprising applying at least one calibration code to the electronic device a predetermined time after initiating the sensor session.

65. 65. The method of claim 64, wherein said at least one calibration code is associated with said at least one predetermined sensitivity.

66. 65. The method of claim 64, wherein said at least one calibration code is associated with a predetermined sensitivity function that uses time as an input.

67. 63. The method of claim 62, wherein the sensitivity profile is a function of time.

68. 68. The method of claim 67, wherein the time corresponds to a time after initiating a sensor session.

69. 68. The method of claim 67, wherein the time corresponds to a time at or since the time of manufacture.

70. 63. The method of claim 62, wherein the sensitivity value is a function of at least one parameter selected from the group consisting of time, a predetermined sensitivity value, temperature, pH, hydration level or duration, curing conditions, analyte concentration of a fluid surrounding the continuous analyte sensor during activation of the sensor, and combinations thereof.

71. 1. A method for processing data from a continuous analyte sensor, comprising: receiving, by the electronic device, sensor data from the continuous analyte sensor, the sensor data including at least one sensor data point; Accepting or creating a sensitivity profile that corresponds to changes in sensor sensitivity substantially throughout a sensor session; forming a conversion function based at least in part on the sensitivity profile; and determining an analyte output value by applying the transformation function to the at least one sensor data point; The method includes:

72. 72. The method of claim 71, wherein the sensitivity profile is established at a manufacturing facility for the continuous analyte sensor.

73. accepting at least one calibration code; 72. The method of claim 71, further comprising: and applying the at least one calibration code to the electronic device a predetermined time after initiating a sensor session.

74. 74. The method of claim 73, wherein said at least one calibration code is associated with said at least one predetermined sensitivity.

75. 74. The method of claim 73, wherein the at least one calibration code is associated with the sensitivity profile.

76. 72. The method of claim 71, wherein the sensitivity profile is a function of time.

77. 77. The method of claim 76, wherein the time corresponds to a time after initiating a sensor session.

78. 77. The method of claim 76, wherein the time corresponds to a time at or since the time of manufacture.

79. 72. The method of claim 71, wherein the sensitivity is a function of time and the at least one parameter is selected from the group consisting of temperature, pH, level or duration of hydration, curing conditions, analyte concentration of a fluid surrounding the continuous analyte sensor during startup of the sensor, and combinations thereof.

80. 1. A system for monitoring an analyte concentration within a host, comprising: a continuous analyte sensor configured to measure an analyte concentration within a host and to provide factory-calibrated sensor data, the factory-calibrated sensor data being calibrated without the use of reference blood glucose data; the system is configured to provide a level of accuracy corresponding to a mean absolute relative difference of no more than about 10% over a sensor session of at least about 3 days; The reference measurement associated with the calculation of the mean absolute relative difference is determined by analysis of blood. system.

81. 81. The system of claim 80, wherein the sensor session is at least about four days.

82. 81. The system of claim 80, wherein the sensor session is at least about 5 days.

83. 81. The system of claim 80, wherein the sensor session is at least about 6 days.

84. 81. The system of claim 80, wherein the sensor session is at least about 7 days.

85. 81. The system of claim 80, wherein the sensor session is at least about 10 days.

86. 81. The system of claim 80, wherein the average absolute relative difference does not exceed about 7% over the sensor session.

87. 81. The system of claim 80, wherein the average absolute relative difference does not exceed about 5% over the sensor session.

88. 81. The system of claim 80, wherein the average absolute relative difference does not exceed about 3% over the sensor session.

89. 1. A method for determining an attribute of a continuous analyte sensor, comprising: applying a bias voltage to the analyte sensor; applying a voltage step above the bias voltage to the analyte sensor; measuring a signal response of said voltage step using sensor electronics; determining a peak current of said signal response using sensor electronics; and determining an attribute of the sensor by relating the peak current to a predetermined relationship using sensor electronics; The method includes:

90. 90. The method of claim 89, wherein relating the peak current to the predetermined relationship comprises calculating an impedance of the sensor based on the peak current, and relating the sensor impedance to the predetermined relationship.

91. 90. The method of claim 89, wherein the attribute of the sensor is a sensitivity of the sensor or a temperature of the sensor.

92. 90. The method of claim 89, wherein the peak current is the difference between the magnitude of the response prior to the voltage step and the largest measured magnitude of the response due to the voltage step.

93. 90. The method of claim 89, wherein the predetermined relationship is a relationship between impedance and sensor sensitivity, and the attribute of the sensor is the sensitivity of the sensor.

94. 90. The method of claim 89, further comprising compensating sensor data using the determined sensor attributes.

95. 95. The method of claim 94, wherein the compensating includes associating a predetermined relationship of the peak current to a sensor sensitivity or change in sensor sensitivity and modifying a value of the sensor data in response to the associated sensor sensitivity or change in sensor sensitivity.

96. 90. The method of claim 89, wherein the predetermined relationship is a linear relationship over a period of time that the analyte sensor is used.

97. 90. The method of claim 89, wherein the predetermined relationship is a non-linear relationship over a period of use of the analyte sensor.

98. 90. The method of claim 89, wherein the predetermined relationship is determined by prior testing of a sensor similar to the analyte sensor.

99. A sensor system configured to carry out a method according to any one of claims 89 to 98.

100. The sensor system comprises instructions stored in a computer memory; A sensor system according to claim 99, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to perform a method according to any one of claims 89 to 98.

101. 1. A method of calibrating an analyte sensor, comprising: applying a time varying signal to the analyte sensor; Measuring a signal response to the applied signal; determining a sensitivity of the analyte sensor using sensor electronics relating at least one attribute of the signal response to a predetermined sensitivity profile; generating, using sensor electronics, an estimated analyte concentration value using the determined sensitivity and sensor data generated by the analyte sensor; The method includes:

102. 102. The method of claim 101, wherein the sensitivity profile comprises sensitivity values ​​that change over a period of time since implantation of the sensor.

103. 102. The method of claim 101, wherein the predetermined sensitivity profile comprises a plurality of sensitivity values.

104. 102. The method of claim 101, wherein the predetermined sensitivity profile is based on sensor sensitivity data generated from studying sensitivity changes of analyte sensors similar to the analyte sensor.

105. applying a bias voltage to the sensor; 102. The method of claim 101, wherein the time varying signal comprises a step voltage above the bias voltage or a sinusoidal voltage superimposed on the bias voltage.

106. The determining step comprises: calculating an impedance value based on the measured signal response; 102. The method of claim 101, further comprising: and correlating the impedance values ​​with sensitivity values ​​of a predetermined sensitivity profile.

107. applying a DC bias voltage to the sensor to generate sensor data; 102. The method of claim 101, wherein estimating the analyte concentration value comprises generating corrected sensor data using the determined sensitivity.

108. 108. The method of claim 107, further comprising applying a conversion function to the corrected sensor data to generate the estimated analyte concentration value.

109. forming a conversion function based at least in part on the determined sensitivity; 108. The method of claim 107, further comprising applying the conversion function to sensor data to generate an estimated analyte concentration value.

110. 102. The method of claim 101, wherein the attribute is a peak current value of the signal response.

111. The determining step comprises:

102. The method of claim 101, comprising using at least one of performing a Fast Fourier Transform on the signal response data, fitting at least a portion of a curve of the signal response, and determining a peak current of the signal response.

112. 102. The method of claim 101, wherein said determining further comprises selecting a predetermined sensitivity profile from a plurality of different predetermined sensitivity profiles based on the determined sensor attributes.

113. the selecting includes performing a data association analysis to determine a correlation between the determined sensor attribute and each of the plurality of different predetermined sensitivity profiles; 113. The method of claim 112, wherein the selected predetermined sensitivity profile has the highest correlation.

114. 113. The method of claim 111 or 112, further comprising generating an estimated analyte concentration value using the selected sensitivity profile.

115. determining a second sensitivity value using the selected sensitivity profile; a first set of estimated analyte concentration values ​​is generated using the determined sensitivity value and sensor data associated with a first time period; 115. The method of claim 114, and wherein the second set of concentration values ​​is generated using sensor data associated with a second sensitivity value and a second time period.

116. A sensor system configured to carry out a method according to any one of claims 101 to 115.

117. The sensor system comprises instructions stored in a computer memory; A sensor system as claimed in claim 116, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to perform a method as claimed in any one of claims 101 to 115.

118. 1. A method for determining whether an analyte sensor system is functioning properly, comprising: applying a stimulation signal to the analyte sensor; measuring a response to said stimulus signal; estimating values ​​of sensor attributes based on the signal responses; relating the sensor attributes to predetermined relationships of the sensor attributes and to predetermined sensor sensitivity profiles; and initiating an error routine if the correlation does not exceed a predetermined correlation threshold. The method includes:

119. 119. The method of claim 118, wherein associating comprises performing a data association analysis.

120. 119. The method of claim 118, wherein the error routine includes displaying a message to a user indicating that the analyte sensor is not functioning properly.

121. 119. The method of claim 118, wherein the sensor attribute is the impedance of the sensor membrane.

122. A sensor system configured to carry out a method according to any one of claims 118 to 121.

123. The sensor system comprises instructions stored in a computer memory; A sensor system as claimed in claim 122, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to perform a method as claimed in any one of claims 118 to 121.

124. 1. A method for determining a temperature associated with an analyte sensor, comprising: applying a stimulation signal to the analyte sensor; measuring a signal response of said signal; and determining a temperature associated with the analyte sensor, said determining including relating at least one attribute of the signal response to a predetermined relationship of a sensor attribute to temperature.

125. 125. The method of claim 124, further comprising generating an estimated analyte concentration value using the determined temperature and sensor data generated from the analyte sensor.

126. 126. The method of claim 125, wherein the generating includes compensating the sensor data using the determined temperature and converting the compensated sensor data using a conversion function to a generated estimated analyte value.

127. 126. The method of claim 125, wherein the generating comprises generating or modifying a conversion function using the determined temperature, and converting the sensor data to the generated estimated analyte value using the generated or modified conversion function.

128. measuring a temperature using a second sensor; 125. The method of claim 124, wherein the determining further comprises determining a temperature associated with the analyte sensor using the measured temperature.

129. 129. The method of claim 128, wherein the second sensor is a thermistor.

130. A sensor system configured to carry out a method according to any one of claims 124 to 129.

131. The sensor system comprises instructions stored in a computer memory; A sensor system as claimed in claim 130, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to perform a method as claimed in any one of claims 124 to 129.

132. 1. A method for determining moisture ingress into an electronic sensor system, comprising: applying a stimulus signal having a particular frequency or a signal including a frequency spectrum to the analyte sensor; measuring a response to said stimulus signal; calculating an impedance based on said measured signal response using sensor electronics; determining, using sensor electronics, whether the impedance falls within a predetermined level corresponding to moisture ingress; and initiating an error routine using the sensor electronics if the impedance exceeds one or both of the respective predetermined levels. The method includes:

133. 133. The method of claim 132, wherein the error routine includes one or more of activating an audible alarm and a visual alarm on a display screen to alert a user that the sensor system may not be functioning properly.

134. 133. The method of claim 132, wherein the stimulation signal has a predetermined frequency.

135. 133. The method of claim 132, wherein the stimulus signal has a spectrum of frequencies.

136. the calculated impedance includes a magnitude value and a phase value; 133. The method of claim 132, wherein said determining comprises comparing said impedance magnitude value to a predefined impedance magnitude threshold and said phase value to a predefined phase threshold.

137. 133. The method of claim 132, wherein the calculated impedance is a complex impedance value.

138. A sensor system arranged to carry out the method according to any one of claims 132 to 137.

139. The sensor system comprises instructions stored in a computer memory; A sensor system as claimed in claim 138, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to perform a method as claimed in any one of claims 132 to 137.

140. 1. A method for determining membrane damage of an analyte sensor using a sensor system, comprising: applying a stimulation signal to the analyte sensor; measuring a response to said stimulus signal; calculating an impedance based on said signal response using sensor electronics; determining, using sensor electronics, whether said impedance falls within a predefined level corresponding to membrane damage; using sensor electronics to initiate an error routine if said impedance exceeds a predefined level; The method includes:

141. 141. The method of claim 140, wherein the error routine includes activating one or more of an audible alarm and a visual alarm on a display screen.

142. 141. The method of claim 140, wherein the stimulation signal has a predetermined frequency.

143. 141. The method of claim 140, wherein the stimulus signal has a spectrum of frequencies.

144. the calculated impedance includes a magnitude value and a phase value; 141. The method of claim 140, wherein said determining comprises comparing said impedance magnitude value to a predefined impedance magnitude threshold and said phase value to a predefined phase threshold.

145. 141. The method of claim 140, wherein the calculated impedance is a complex impedance value.

146. A sensor system configured to carry out a method according to any one of claims 140 to 145.

147. The sensor system comprises instructions stored in a computer memory; A sensor system according to claim 146, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to perform a method according to any one of claims 140 to 145.

148. 1. A method for determining reuse of an analyte sensor, comprising: applying a stimulation signal to the analyte sensor; measuring a response to said stimulus signal; calculating an impedance response based on said response; comparing the calculated impedance to a predetermined threshold; and initiating a sensor reuse routine if the impedance is determined to exceed a threshold. The method includes:

149. 149. The method of claim 148, wherein the sensor reuse routine includes activating an audible and / or visual alarm to notify a user of improper sensor reuse.

150. 149. The method of claim 148, wherein the step of recycling the sensor includes completely or partially shutting down the sensor system and / or ceasing to display sensor data on a user interface of the sensor system.

151. A sensor system configured to carry out the method of any one of claims 148 to 150.

152. The sensor system comprises instructions stored in a computer memory; A sensor system as claimed in claim 151, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to perform a method as claimed in any one of claims 148 to 150.

153. 1. A method for determining reuse of an analyte sensor, comprising: applying a stimulation signal to the analyte sensor; measuring a response to said stimulus signal; calculating an impedance based on the response; determining a correlation between the calculated impedance using a data correlation function and one or more recorded impedance values; and initiating a sensor reuse routine if the correlation is determined to exceed a predetermined threshold. The method includes:

154. 154. The method of claim 153, wherein the sensor reuse routine includes activating an audible and / or visual alarm to notify a user of improper sensor reuse.

155. 154. The method of claim 153, wherein the step of recycling the sensor includes completely or partially shutting down the sensor system and / or ceasing to display sensor data on a user interface of the sensor system.

156. A sensor system configured to carry out a method according to any one of claims 153 to 155.

157. The sensor system comprises instructions stored in a computer memory; A sensor system as described in claim 156, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to perform a method as described in any one of claims 153 to 155.

158. 1. A method of applying an over potential to an analyte sensor, comprising: applying a stimulation signal to the analyte sensor; measuring a response to said stimulus signal; determining a sensitivity of the sensor or a change in sensitivity of the sensor based on the response; and applying an excess potential to the sensor based on the determined sensitivity or change in sensitivity. The method includes:

159. 159. The method of claim 158, wherein said determining includes calculating an impedance based on the response, and determining a sensitivity or sensitivity change based on the impedance.

160. 160. The method of claim 159, wherein determining the sensitivity or change in sensitivity further comprises relating an impedance to sensitivity relationship to a predetermined impedance.

161. 159. The method of claim 158, wherein the applying step includes determining or modifying the amount of time the excess potential is applied to the sensor.

162. 159. The method of claim 158, wherein the applying step includes determining or modifying a magnitude of an overpotential applied to the sensor.

163. A sensor system configured to carry out a method according to any one of claims 158 to 162.

164. The sensor system comprises instructions stored in a computer memory; A sensor system as described in claim 163, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to perform a method as described in any one of claims 158 to 162.

165. 1. A method for determining an attribute of a continuous analyte sensor, comprising: applying a stimulation signal to a first analyte sensor having a first working electrode and a first reference electrode; measuring a signal response of the stimulation signal using a second analyte sensor having a second working electrode and a second reference electrode; and determining an attribute of the first sensor by relating the response to a predetermined relationship; The method includes:

166. applying a bias voltage to a first working electrode to generate sensor data; and measuring a response to the bias voltage.

167. 167. The method of claim 166, further comprising calibrating sensor data using the determined attributes.

168. 166. The method of claim 165, wherein the determined attribute is one of impedance and temperature.

169. 166. The method of claim 165, further comprising determining damage to the sensor membrane using the determined attributes.

170. 166. The method of claim 165, further comprising determining moisture ingress into a sensor system surrounding the first and second analyte sensors using the determined attributes.

171. A sensor system configured to carry out the method of any one of claims 165 to 170.

172. The sensor system comprises instructions stored in a computer memory; A sensor system as claimed in claim 171, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to perform a method as claimed in any one of claims 165 to 170.

173. 1. A method for determining a scaling used in a continuous analyte sensor system, comprising: applying a first stimulation signal to a first working electrode of the analyte sensor; measuring a response to the first stimulus signal; applying a second stimulation signal to a second working electrode of the analyte sensor; measuring a response to the second stimulus signal; determining a scaling factor based on the measured responses to the first and second stimulus signals using sensor electronics; and using a scaling factor to generate an estimated analyte value based on sensor data generated by the analyte sensor. The method includes:

174. 174. The method of claim 173, wherein the method is performed periodically.

175. The determining step comprises: calculating a first impedance using a response to the first stimulus signal; calculating a second impedance using the response to the second stimulus signal; 174. The method of claim 173, wherein the scaling factor is a ratio of the first impedance to the second impedance.

176. 174. The method of claim 173, wherein a first working electrode has a thin film containing an enzyme configured to react with the analyte and a second working electrode has a thin film not containing the enzyme.

177. 174. The method of claim 173, wherein determining the scaling factor comprises updating a previous scaling factor based on measured responses to the first and second stimulus signals.

178. The scaling factor is the acetaminophen scaling factor, The method includes updating a further scaling factor based on the acetaminophen scaling factor; 174. The method of claim 173, and wherein the further scaling factor is applied to the sensor data to generate the estimated analyte value.

179. A sensor system configured to carry out a method according to any one of claims 173 to 178.

180. The sensor system comprises instructions stored in a computer memory; A sensor system as described in claim 179, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to perform a method as described in any one of claims 173 to 178.

181. 1. A method of calibrating an analyte sensor, comprising: applying a predetermined signal to the analyte sensor; Measuring a response to an applied signal; determining, using sensor electronics, a change in impedance associated with a membrane of the analyte sensor based on the measured response; calculating a change in sensitivity of the analyte sensor based on the determined impedance; calculating a revised sensitivity based on the calculated change in sensitivity and a previously used sensitivity of the analyte sensor; and generating an estimated analyte value using the revised sensitivity. The method includes:

182. 182. The method of claim 181, wherein calculating the change in sensitivity comprises applying a non-linear compensation function.

183. The nonlinear compensation function is expressed as the following equation: △S = (a*log(t)+b)*△I 183. The method of claim 182, wherein ΔS is the change in sensitivity, t is the time since calibrating the analyte sensor, ΔI is the change in determined impedance, and a and b are predetermined coefficients.

184. 184. The method of claim 183, wherein a and b are determined by previously testing similar analyte sensors.

185. A sensor system configured to carry out a method according to any one of claims 181 to 184.

186. The sensor system comprises instructions stored in a computer memory; A sensor system as described in claim 185, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to perform a method as described in any one of claims 181 to 184.

187. 1. A method for calibrating an analyte sensor, comprising: generating sensor data using a subcutaneous analyte sensor; forming or modifying a transformation function using pre-implant information, internal diagnostic information, and / or external reference information as inputs; calibrating sensor data using said transformation function; The method includes:

188. Pre-planting information: information selected from the group consisting of a predetermined sensitivity profile associated with the analyte sensor, a predetermined relationship between a measured sensor attribute and a sensor sensitivity, and one or more predetermined relationships between a measured sensor attribute and a sensor temperature; sensor data obtained from a previously used analyte sensor; a calibration code associated with the analyte sensor; a patient-specific relationship between the analyte sensor and one or more of sensitivity, baseline, drift, and impedance; Information representing the implantation site of the sensor; the time since manufacture of the analyte sensor; and information representative of the specimen being exposed to temperature or humidity.

189. The method of claim 187 or 188, wherein the internal diagnostic information is selected from the group consisting of output of a stimulation signal, sensor data indicative of analyte concentration measured by the sensor, temperature measurements using a sensor or a separate sensor, sensor data generated by a redundant sensor designed substantially similar to the analyte sensor, sensor data generated by an auxiliary sensor having a different format than the analyte sensor, time since the sensor was implanted or connected to sensor electronics connected to the sensor, data generated by a pressure sensor indicative of pressure on the sensor or sensor system, data generated by an accelerometer, moisture ingress criteria, and analyte concentration signal noise criteria.

190. The method of claim 187, 188 or 189, wherein the reference information includes information selected from the group consisting of real-time and / or preceding analyte concentration information obtained from a reference monitor, information regarding the type / brand of reference monitor used to provide the reference data, information regarding the amount of carbohydrates consumed by the user, information received from a drug delivery device, glucagon sensitivity information, and information compiled from population-based data.

191. A sensor system configured to carry out a method according to any one of claims 187 to 190.

192. The sensor system comprises instructions stored in a computer memory; The sensor system of claim 191, wherein the instructions, when executed by one or more processors of the sensor system, cause the sensor system to perform a method as recited in one of claims 187 to 190.

193. 2. Apparatus substantially as shown and / or described in the specification and / or drawings.

194. 2. A method substantially as shown and / or described in the specification and / or drawings.

195. 2. A system substantially as shown and / or described in the specification and / or drawings.

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