Multi-analyte sensor systems

Abstract

Various examples are directed to systems and methods for using analyte sensor systems such as, for example, analyte sensor and analyte sensor configured to sense a first analyte when operated in a first mode and two cents a second analyte when operated in a second mode. In some examples, a concentration of the first analyte is determined based on a temperature value generated by a temperature sensor. In some examples, an error of an analyte sensor is determined. In some examples, a baseline sensor characteristic is determined.

Description

Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01MULTI-ANALYTE SENSOR SYSTEMSPRIORITY

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 739,289, filed on December 27, 2024, which is incorporated herein by reference in its entirety'.BACKGROUND

[0002] Diabetes is a metabolic condition relating to the production or use of insulin by the body. Insulin is a hormone that allows the body to use glucose for energy or store glucose as fat.

[0003] When a person eats a meal that contains carbohydrates, the food is processed by the digestive system, which produces glucose in the person’s blood. Blood glucose can be used for energy or stored as fat. The body normally maintains blood glucose levels in a range that provides sufficient energy to support bodily functions and avoids problems that can arise when glucose levels are too high or too low. Regulation of blood glucose levels depends on the production and use of insulin, which regulates the movement of blood glucose into cells.

[0004] When the body does not produce enough insulin or when the body is unable to effectively use insulin that is present, blood sugar levels can elevate beyond normal ranges. The state of having a higher-than-normal blood sugar level is called “hyperglycemia.” Chronic hyperglycemia can lead to a number of health problems, such as cardiovascular disease, cataract and other eye problems, nerve damage (neuropathy), and kidney damage. Hyperglycemia can also lead to acute problems, such as diabetic ketoacidosis - a state in which the body becomes excessively acidic due to the presence of blood glucose and ketones, which are produced when the body cannot use glucose. The state of having lower than normal blood glucose levels is called “hypoglycemia.” Severe hypoglycemia can lead to acute crises that can result in seizures or death.

[0005] A diabetes patient can receive insulin to manage blood glucose levels. Insulin can be received, for example, through a manual injection with a needle.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01Wearable insulin pumps are also available. Diet and exercise also affect blood glucose levels. A glucose sensor can provide an estimated glucose concentration level, which can be used as guidance by a patient or caregiver.

[0006] Diabetes conditions are sometimes referred to as “Type 1” and “Type 2.” A Type 1 diabetes patient is ty pically able to use insulin when it is present, but the body is unable to produce sufficient amounts of insulin, because of a problem with the insulin-producing beta cells of the pancreas. A Type 2 diabetes patient may produce some insulin, but the patient has become “insulin resistant” due to a reduced sensitivity' to insulin. The result is that even though insulin is present in the body, the insulin is not sufficiently used by the patient’s body to effectively regulate blood sugar levels.

[0007] Blood sugar concentration levels may be monitored with an analyte sensor, such as a continuous glucose monitor. A continuous glucose monitor is used by a host (e.g., patient) to provide information, such as an estimated blood glucose value or a trend of estimated blood glucose levels.SUMMARY

[0008] This present application discloses, among other things, systems, devices, and methods related to analyte sensor, including, for example, improvements in accuracy and / or calibration in analyte sensors.

[0009] Example 1 is a method for operating a subject analyte sensor configured to sense a first analyte when operated in a first mode and to sense a second analyte when operated in a second mode, the method comprising: while operating the subject analyte sensor in the first mode, exposing the subject analyte sensor to a first concentration of the first analyte; while operating the subject analyte sensor in the first mode, exposing the subject analyte sensor to a second concentration of the first analyte, the second concentration being different than the first concentration; accessing baseline sensitivity’ data describing a sensitivity of a baseline set of analyte sensors to the first analyte, the baseline set of analyte sensors not including the subject analyte sensor; generating subject analyte sensor sensitivity’ data describing a sensitivity' of the subject analyte sensor to the first analyte, the generating of the subject analyte sensor sensitivity data being based at least in part on a response of the subject analyte sensor to the first analyte at the first concentration and at least in part onAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01a response of the subject analyte sensor to the first analyte at the second concentration; accessing first mode in vivo sensor data, wherein the first mode in vivo sensor data is generated by the subject analyte sensor while operating in the first mode and the subject analyte sensor is inserted into a host; and generating a first analyte concentration value using the first mode in vivo sensor data and the subject analyte sensor sensitivity data.

[0010] In Example 2, the subject matter of Example 1 optionally includes accessing second mode in vivo sensor data generated by the subject analyte sensor while operating in the second mode and inserted into the host; and generating a second analyte concentration value using the second mode in vivo sensor data.

[0011] In Example 3, the subject matter of any one or more of Examples 1-2 optionally includes while operating the subject analyte sensor in the first mode, exposing the subject analyte sensor to a third concentration of the first analyte; and while operating the subject analyte sensor in the first mode, exposing the subject analyte sensor to a fourth concentration of the first analyte, the generating of the subject analyte sensor sensitivity data also being based at least in part on a response of the subject analyte sensor to the first analyte at the third concentration and at least in part on a response of the subject analyte sensor to the first analyte at the fourth concentration.

[0012] In Example 4, the subject matter of Example 3 optionally includes, the first concentration being between about 1 ppm and 3 ppm, the second concentration being between about 0.4 ppm and 1.2 ppm, the third concentration being between about 0.05 ppm and 0.4 ppm, and the fourth concentration being between about 0 ppm and 0.2 ppm.

[0013] In Example 5, the subject matter of any one or more of Examples 3-4 optionally includes wherein: the exposing of the subject analyte sensor to the first concentration of the first analyte is before the exposing of the subject analyte sensor to the second concentration of the first analyte; the exposing of the subject analyte sensor to the second concentration of the first analyte is before the exposing of the subject analyte sensor to the third concentration of the first analyte; and the exposing of the subject analyte sensor to the third concentration of the first analyte is before the exposing of the subject analyte sensor to the fourth concentration of the first analyte.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0014] In Example 6, the subject matter of any one or more of Examples 1-5 optionally includes repeatedly exposing the baseline set of analyte sensors to a first sequence of concentrations of the first analyte for a baseline time period, the baseline sensitivity data being based at least in part on a response of the baseline set of analyte sensors to the first sequence of concentrations of the first analyte; and exposing the subject analyte sensor to a second sequence of concentrations of the first analyte for a pre-session time period, the sequence of concentrations of the first analyte comprising the first concentration of the first analyte and the second concentration of the first analyte.

[0015] In Example 7, the subject matter of Example 6 optionally includes the exposing of the subject analyte sensor to the second sequence of concentrations of the first analyte comprising repeatedly exposing the subject analyte sensor to the second sequence of concentrations of the first analyte.

[0016] In Example 8, the subject matter of any one or more of Examples 6-7 optionally includes the pre-session time period being shorter than the baseline time period.

[0017] In Example 9, the subject matter of any one or more of Examples 6-8 optionally includes the pre-session time period being between about one hour and about eight hours.

[0018] In Example 10, the subject matter of any one or more of Examples 6-9 optionally includes the pre-session time period being between about two hours and about four hours.

[0019] In Example 11, the subject matter of any one or more of Examples 6-10 optionally includes the baseline time period being between about five days and about twenty days.

[0020] In Example 12, the subject matter of any one or more of Examples 6-11 optionally includes the baseline time period being between about fifteen days.

[0021] In Example 13, the subject matter of any one or more of Examples 1-12 optionally includes while exposing the subject analyte sensor to the first concentration of the first analyte, exposing the analyte sensor to a first concentration of the second analyte; and while exposing the subject analyte sensor to the second concentration of the first analyte, exposing the analyte sensor to the first concentration of the second analyte.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0022] In Example 14, the subject matter of any one or more of Examples 1-13 optionally includes storing the subject analyte sensor sensitivity data to subject sensor electronics associated with the subject analyte sensor, the generating of the first analyte concentration value being performed by the subject sensor electronics.

[0023] In Example 15, the subject matter of Example 14 optionally includes receiving, by the subject sensor electronics, an indication that the subject analyte sensor has been inserted into a host; and operating, by the subject sensor electronics, the subject analyte sensor in the first mode to generate the first mode in vivo sensor data.

[0024] In Example 16, the subject matter of any one or more of Examples 1-15 optionally includes generating a modified baseline characteristic data based at least in part on the first mode in vivo sensor data.

[0025] Example 17 is a system comprising: at least one processor and at least one memory’ storing instructions, which when executed by the at least one processor perform operations comprising the method of any of Examples 1 to 16.

[0026] Example 18 is an analyte sensor system configurable to a first mode for generating a sensor signal indicative of a first analyte and to a second mode for generating a sensor signal indicative of a second analyte, the analyte sensor system comprising: a first analyte sensor; a temperature sensor; and sensor electronics configured to perform operations comprising: accessing a first sensor signal sample of a first sensor signal indicative of the first analyte; accessing a temperature value generated by the temperature sensor; accessing first analyte temperature compensation data describing a relationship between temperature and a characteristic of the first analyte sensor; and generating an estimated first analyte concentration using the first sensor signal sample, the temperature value, and the first analyte temperature compensation data.

[0027] In Example 19, the subject matter of Example 18 optionally includes the sensor electronics further configured to perform operations comprising: accessing a second sensor signal sample indicative of the second analyte; and generating an estimated second analyte concentration using the second sensor signal sample.

[0028] In Example 20, the subject matter of Example 19 optionally includes the sensor electronics further configured to perform operations comprising: biasingAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01the first analyte sensor to a first bias condition: while the first analyte sensor is biased to the first bias condition, generating the first sensor signal sample from the first sensor signal; biasing the first analyte sensor to a second bias condition different than the first bias condition; and while the first analyte sensor is biased to the second bias condition, generating the second sensor signal sample from the second sensor signal sample.

[0029] In Example 21, the subject matter of any one or more of Examples 19-20 optionally includes a second analyte sensor, the first sensor signal being generated by the first analyte sensor and the second sensor signal sample being sampled from a second sensor signal generated by the second analyte sensor.

[0030] In Example 22, the subject matter of any one or more of Examples 18-21 optionally includes the first analyte temperature compensation data describing a relationship between temperature and a first analyte sensitivity' of the first analyte sensor.

[0031] In Example 23, the subject matter of Example 22 optionally includes the relationship being a linear relationship over a first temperature range.

[0032] In Example 24, the subject matter of any one or more of Examples 18-23 optionally includes the relationship being an Arrhenius relationship.

[0033] Example 25 is a method for using an analyte sensor configured to generate a first sensor signal describing a concentration of a first analyte and a second sensor signal describing a concentration of a second analyte, the method comprising: accessing a first sensor signal sample of a first sensor signal indicative of the first analyte generated by a first analyte sensor; accessing a temperature value generated by a temperature sensor; accessing first analyte temperature compensation data describing a relationship between temperature and a characteristic of the first analyte sensor; and generating an estimated first analyte concentration using the first sensor signal sample, the temperature value, and the first analyte temperature compensation data.

[0034] In Example 26, the subject matter of Example 25 optionally includes accessing a second sensor signal sample indicative of the second analyte; and generating an estimated second analyte concentration using the second sensor signal sample.

[0035] In Example 27, the subject matter of Example 26 optionally includes biasing the first analyte sensor to a first bias condition; while the first analyteAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01sensor is biased to the first bias condition, generating the first sensor signal sample from the first sensor signal; biasing the first analyte sensor to a second bias condition different than the first bias condition; and while the first analyte sensor is biased to the second bias condition, generating the second sensor signal sample from the second sensor signal.

[0036] In Example 28, the subject matter of any one or more of Examples 26-27 optionally includes a second analyte sensor, the first sensor signal being generated by the first analyte sensor and the sensor signal sample being sampled from a second sensor signal generated by the second analyte sensor.

[0037] In Example 29, the subject matter of any one or more of Examples 25-28 optionally includes the first analyte temperature compensation data describing a relationship between temperature and a first analyte sensitivity of the first analyte sensor.

[0038] In Example 30, the subject matter of Example 29 optionally includes the relationship being a linear relationship over a first temperature range.

[0039] In Example 31, the subject matter of any one or more of Examples 29-30 optionally includes the relationship being an Arrhenius relationship.

[0040] In Example 32, the subject matter of any one or more of Examples 25-31 optionally includes while operating the analyte sensor in a first mode, exposing the analyte sensor to a series of concentrations of the first analyte a first temperature; while operating the analyte sensor in a first mode, exposing the analyte sensor to the series of concentrations of the first analyte at a second temperature different than the first temperature; and determining analyte sensor temperature compensation data describing a relationship between temperature and a characteristic of the analyte sensor based at least in part on a response of the analyte sensor to the series of concentrations of the first analyte at the first temperature and the response of the analyte sensor to the series of concentrations of the first analyte at the second temperature.

[0041] Example 33 is a method for testing analyte sensors configured to sense a first analyte when operated in a first mode and to sense a second analyte when operated in a second mode, the method comprising: generating a baseline sensor characteristic using a baseline set of analyte sensors; exposing a subject analyte sensor to a first concentration of the first analyte; while the subject analyte sensor is exposed to the first concentration of the first analyte, transitioning theAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01subject analyte sensor from the second mode to the first mode; after transitioning the subject analyte sensor from the second mode to the first mode, taking a first sensor signal sample; generating a first estimated concentration of the first analyte using the baseline sensor characteristic and the first sensor signal sample; and determining an error of the subject analyte sensor based at least in part on the first estimated concentration of the first analyte and the first concentration of the first analyte.

[0042] In Example 34, the subject matter of Example 33 optionally includes prior to transitioning the subject analyte sensor from the second mode to the first mode, taking a reference sensor signal sample, the error of the subject analyte sensor also being based at least in part on the reference sensor signal sample.

[0043] In Example 35, the subject matter of Example 34 optionally includes determining a first rebased sensor signal sample based at least in part on the first sensor signal sample and the reference sensor signal sample, the generating of the first estimated concentration of the first analyte being based at least in part on the first rebased sensor signal sample.

[0044] In Example 36, the subject matter of any one or more of Examples 33-35 optionally includes exposing the subject analyte sensor to a second concentration of the first analyte; while the subject analyte sensor is exposed to the second concentration of the first analyte, transitioning the subject analyte sensor from the second mode to the first mode; after transitioning the subject analyte sensor from the second mode to the first mode, taking a second sensor signal sample; and generating a second estimated concentration of the first analyte using the baseline sensor characteristic and the second sensor signal sample, the error of the subject analyte sensor also being based at least in part on the second estimated concentration of the first analyte and the second concentration of the first analyte.

[0045] In Example 37, the subject matter of any one or more of Examples 33-36 optionally includes the exposing of the subject analyte sensor to the first concentration of the first analyte comprising exposing the subject analyte sensor to a buffer solution, the method further comprising using a reference first analyte sensor to determine that the buffer solution is at the first concentration of the first analyte.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0046] In Example 38, the subject matter of any one or more of Examples 33-37 optionally includes the baseline sensor characteristic describing a sensitivity’ of the first analyte sensor to the first analyte when operated in the first mode.

[0047] In Example 39, the subject matter of any one or more of Examples 33-38 optionally includes modifying the baseline sensor characteristic based at least in part on the error.

[0048] In Example 40, the subject matter of any one or more of Examples 33-39 optionally includes while operating the baseline set of analyte sensors in the first mode, exposing the baseline set of analyte sensors to a first test concentration of the first analyte; and while operating the baseline set of analyte sensors in the first mode, exposing the baseline set of analyte sensors to a second test concentration of the first analyte, the second test concentration being different than the first concentration, the determining of the baseline sensor characteristic being based at least in part on a response of the baseline set of analyte sensors to the first test concentration of the first analyte and a response of the baseline set of analyte sensors to the second test concentration of the first analyte.

[0049] In Example 41, the subject matter of any one or more of Examples 33-40 optionally includes determining to redetermine the baseline sensor characteristic, the determining based at least in part on the error of the subject analyte sensor.

[0050] Example 42 is a system comprising: at least one processor and at least one memory storing instructions, which when executed by the at least one processor perform operations comprising the method of any one of Examples 33 to 41.

[0051] Example 43 is a multi-analyte sensor system operable in a first mode to measure a first analyte and in a second mode to measure a second analyte, the multi-analyte sensor system comprising: an analyte sensor; and sensor electronics configured to perform operations comprising: operating the multianalyte sensor system in the first mode; while the multi-analyte sensor system is operating in the first mode, measuring a concentration of the first analyte; after operating the multi-analyte sensor system in the first mode, operating the multianalyte sensor system in the second mode; while the multi-analyte sensor system is operating in the second mode, measuring a concentration of the second analyte; detecting a fault in the multi-analyte sensor system, the detecting being based at least in part on the concentration of the second analyte to detect a faultAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01in the multi-analyte sensor system; and executing a responsive action in response to the detected fault.

[0052] In Example 44, the subject matter of Example 43 optionally includes the first analyte being glucose and the second analyte being oxygen.

[0053] In Example 45, the subject matter of Example 44 optionally includes the detecting of the fault in the multi-analyte sensor system comprising determining that the concentration of the second analyte is less than a threshold concentration.

[0054] In Example 46, the subject matter of Example 45 optionally includes the threshold concentration being between about 0.1 ppm and about 0.5 ppm.

[0055] In Example 47, the subject matter of any one or more of Examples 45-46 optionally includes the threshold concentration being between about 0.25 ppm and about 0.3 ppm.

[0056] In Example 48, the subject matter of any one or more of Examples 44-47 optionally includes the detecting of the fault in the multi-analyte sensor system comprising determining that the concentration of the second analyte is less than a previously -measured concentration of the second analyte by more than a threshold.

[0057] In Example 49, the subject matter of Example 48 optionally includes the threshold being between about 10% and about 50% of the previously-measured concentration.

[0058] In Example 50, the subject matter of any one or more of Examples 44-49 optionally includes the detecting of the fault in the multi-analyte sensor system comprising executing a trained computerized model, the concentration of the second analyte being at least one input to the trained computerized model and an output of the trained computerized model indicating the fault in the multi-analyte sensor system.

[0059] Example 51 is a method of using a multi-analyte sensor system operable in a first mode to measure a first analyte and in a second mode to measure a second analyte, the method comprising: operating the multi-analyte sensor system in the first mode; while the multi-analyte sensor system is operating in the first mode, measuring a concentration of the first analyte; after operating the multi-analyte sensor system in the first mode, operating the multi-analyte sensor system in the second mode; while the multi-analyte sensor system is operating inAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01the second mode, measuring a concentration of the second analyte; detecting a fault in the multi-analyte sensor system, the detecting being based at least in part on the concentration of the second analyte to detect a fault in the multi-analyte sensor system; and executing a responsive action in response to the detected fault.

[0060] In Example 52, the subject matter of Example 51 optionally includes the first analyte being glucose and the second analyte being oxygen.

[0061] In Example 53, the subject matter of Example 52 optionally includes the detecting of the fault in the multi-analyte sensor system comprising determining that the concentration of the second analyte is less than a threshold concentration.

[0062] In Example 54, the subject matter of Example 53 optionally includes the threshold concentration being between about 0.1 ppm and about 0.5 ppm.

[0063] In Example 55, the subject matter of Example 54 optionally includes the threshold concentration being between about 0.25 ppm and about 0.3 ppm.

[0064] In Example 56, the subject matter of any one or more of Examples 52-55 optionally includes the detecting of the fault in the multi-analyte sensor system comprising determining that the concentration of the second analyte is less than a previously -measured concentration of the second analyte by more than a threshold.

[0065] In Example 57, the subject matter of Example 56 optionally includes the threshold being between about 10% and about 50% of the previously-measured concentration.

[0066] In Example 58, the subject matter of any one or more of Examples 52-57 optionally includes the detecting of the fault in the multi-analyte sensor system comprising executing a trained computerized model, the concentration of the second analyte being at least one input to the trained computerized model and an output of the trained computerized model indicating the fault in the multi-analyte sensor system.

[0067] Example 59 is a method comprising: determining, based at least on in vitro data, first calibration information for an analyte sensor configured to generate at least an oxygen signal; accessing in vivo oxygen data, wherein the in vivo oxygen data is generated by the analyte sensor when the in vivo oxygen sensor is inserted into a host; determining, second calibration information for theAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01analyte sensor based at least on the in vivo oxygen data; and determining an estimated oxygen concentration based at least on the second calibration information.

[0068] In Example 60, the subject matter of Example 59 optionally includes generating the in vivo oxygen data based at least on an oxygen signal generated by the analyte sensor when the analyte sensor is inserted into the host.

[0069] In Example 61, the subject matter of any one or more of Examples 59-60 optionally includes wherein the in vitro data is determined by at least: while operating the analyte sensor in a first mode, exposing the analyte sensor to a first concentration of oxygen; while operating the analyte sensor in the first mode, exposing the analyte sensor to a second concentration of oxygen, the second concentration being different than the first concentration.

[0070] In Example 62, the subject matter of any one or more of Examples 59-61 optionally includes wherein the analyte sensor configured to sense a first analyte when operated in a first mode and to sense a second analyte when operated in a second mode.

[0071] In Example 63, the subject matter of any one or more of Examples 59-62 optionally includes wherein the analyte sensor is further configured to generate a glucose signal.

[0072] In Example 64, the subject matter of Example 63 optionally includes determining an estimated glucose concentration.

[0073] In Example 65, the subject matter of Example 64 optionally includes wherein the estimated glucose concentration is determined based at least on the estimated oxygen concentration.

[0074] In Example 66, the subject matter of any one or more of Examples 59-65 optionally includes wherein the estimated oxygen concentration is determined based at least on the first calibration information.

[0075] In Example 67, the subject matter of any one or more of Examples 59-66 optionally includes modifying the first calibration information based at least on the second calibration information.

[0076] In Example 68, the subject matter of Example 67 optionally includes wherein the estimated oxygen concentration is determined based at least on the modified first calibration information.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0077] In Example 69, the subject matter of any one or more of Examples 59-68 optionally includes wherein the analyte sensor operates in a first mode for measuring a first analyte when a first bias condition is applied to the analyte sensor and in a second mode for measuring a second analyte when a second bias condition is applied to the analyte sensor.

[0078] Example 70 is a system comprising at least one data processor; and at least one memory storing instructions which when executed by the at least one data processor perform operations comprising the method of any one of Examples 59 to 69.

[0079] Example 71 is a method, apparatus, and / or system comprising any combination of Examples 1 to 70.

[0080] This summary is intended to provide an overview of subject matter of the present patent application. It is not intended to provide an exclusive or exhaustive explanation of the disclosure. The detailed description is included to provide further information about the present patent application. Other aspects of the disclosure will be apparent to persons skilled in the art upon reading and understanding the following detailed description and viewing the drawings that form a part thereof, each of which are not to be taken in a limiting sense.BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. Like numerals having different letter suffixes may represent different instances of similar components. The drawings illustrate generally, by way of example, but not by way of limitation, various embodiments described in the present document.

[0082] FIG. 1 is a diagram showing one example of an environment including an analyte sensor system.

[0083] FIG. 2 is a schematic illustration of an example analyte sensor system, which may for example, be the system shown in FIG. 1.

[0084] FIG. 3 is a diagram showing one example of a medical device system including the analyte sensor system of FIG. 1.

[0085] FIG. 4 is an illustration of an example analyte sensor.

[0086] FIG. 5 is an illustration of another example analyte sensor.

[0087] FIG. 6 is an enlarged view of an example analyte sensor portion.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0088] FIG. 7 is a cross-sectional view of the analyte sensor of FIGS. 3 and 4.

[0089] FIG. 8 is a schematic illustration of a circuit that represents the behavior of an example analyte sensor.

[0090] FIG. 9 is a diagram showing one example of a workflow that may be executed by sensor electronics of an analyte sensor system to generate an estimated analyte concentration.

[0091] FIG. 10 is a diagram showing one example of a workflow that may be executed by sensor electronics of an analyte sensor system to generate an estimated analyte concentration with temperature compensation performed prior to the determination of a desired sensor signal.

[0092] FIG. 11 is a flowchart showing one example of a process flow that may be executed in the analyte sensor system of FIG. 10 to measure a first analyte and a second analyte.

[0093] FIG. 12 shows a plot illustrating bias conditions provided by a bias circuit to an analyte sensor in one example implementation of the process flow of FIG. 11.

[0094] FIG. 13 is a diagram showing one example of a workflow that may be executed by the sensor electronics of FIG. 10 to generate an estimated analyte concentration of the first analyte and an estimated analyte concentration of the second analyte.

[0095] FIG. 14 is a diagram showing another example of an analyte sensor system configured to measure a first analyte and a second analyte using a first analyte sensor and a second analyte sensor.

[0096] FIG. 15 is a diagram showing one example of a workflow that may be executed by the sensor electronics of FIG. 14 to generate an estimated analyte concentration of the first analyte and an estimated analyte concentration of the second analyte.

[0097] FIG. 16 is a flowchart showing one example of a process flow that may be performed to determine a sensor characteristic of an analyte sensor configured to sense a first analyte and a second analyte.

[0098] FIG. 17 is a plot showing an example series of analyte concentrations to which an analyte sensor may be exposed to generate an analyte sensor characteristic.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0099] FIG. 18 is a plot showing an example series of analyte concentrations to which an analyte sensor may be exposed to generate an analyte sensor characteristic.

[0100] FIG. 19 is a flowchart showing one example of a process flow that may be performed utilizing a baseline set of analyte sensors to generate sensor-specific sensor characteristics for a subject analyte sensor.

[0101] FIG. 20 is a plot illustrating an example of generating a baseline sensor characteristic at a time during the baseline test time period.

[0102] FIG. 21 is a plot illustrating an example of generating a baseline sensor characteristic from characteristics of a baseline set of analyte sensors determined during a baseline test.

[0103] FIG. 22 is a plot illustrating an example of generating a subject sensor characteristic by modifying the baseline sensor characteristic.

[0104] FIG. 23 is a flowchart showing one example of a process flow that may be performed utilizing a baseline set of analyte sensors to generate sensor-specific sensor characteristics for a subject analyte sensor.

[0105] FIG. 24 illustrates an example calibration process that can use one or more of pre-implant information, internal diagnostic information, and external reference information as inputs to form or modify a transformation function.

[0106] FIG. 25 is a flowchart showing one example of a process flow that may be executed to determine and utilize a temperature compensation for an analyte sensor.

[0107] FIG. 26 is a plot showing an example relationship between an analyte sensor characteristic (e.g., oxygen sensitivity) and temperature.

[0108] FIG. 27 is a plot showing another example relationship between an analyte sensor characteristic (e.g., oxygen sensitivity) and temperature.

[0109] FIG. 28 is a flowchart showing one example of a process flow that may be executed in an analyte sensor system to compensate for temperature.

[0110] FIG. 29 shows two example plots showing concentrations of a first analyte.

[0111] FIG. 30 is a flowchart showing one example of a process flow that may be performed to determine an error in an estimated analyte concentration generated by analyte sensors.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0112] FIG. 31 is a flowchart showing one example of a process flow for generating a first analyte rebased sensor signal.

[0113] FIG. 32 is a plot showing one example correlation between a raw sensor signal generated by a multi-analyte sensor configured in an oxygen mode and know n concentrations of oxygen.

[0114] FIG. 33 is a plot showing one example correlation between a raw sensor signal generated by a multi-analyte sensor configured in an oxygen mode and kno Ti concentrations of oxygen.

[0115] FIG. 34 is a plot showing example correlations between estimated oxygen concentration and an analyte sensor fault condition.

[0116] FIG. 35 is a plot showing additional example correlations between estimated oxygen concentration and analyte sensor fault conditions.

[0117] FIG. 36 is a flowchart show ing one example of a process How that may be executed, for example, by sensor electronics of a multi-analyte sensor system, to detect a sensor fault using estimated oxygen concentrations.

[0118] FIG. 37 is a flowchart showing one example of a process flow that may be executed, for example, by sensor electronics of a multi-analyte sensor system, to detect a sensor fault using estimated oxygen concentrations.

[0119] FIG. 38 illustrates a simplified block diagram showing an example process flow that may be executed by a sensor electronics of an analyte sensor system to detect an end-of-life state of the analyte sensor.

[0120] FIG. 39 is a block diagram illustrating a computing device hardware architecture, within w hich a set or sequence of instructions can be executed to cause a machine to perform examples of any one of the methodologies discussed herein.DETAILED DESCRIPTION

[0121] Various examples described herein are directed to analyte sensor systems and methods for using analyte sensor systems. An analyte sensor system includes an analyte sensor placed in contact with a host's bodily fluid to measure an analyte's concentration, such as glucose, in the bodily fluid. In some examples, the analyte sensor is inserted into the host to contact the bodily fluid in vivo. In some examples, the analyte sensor is inserted subcutaneously to contact interstitial fluid below the host’s skin.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0122] When the analyte sensor is exposed to analyte in the host’s bodily fluid, an electrochemical reaction between the analyte sensor and the analyte causes the analyte sensor to generate a raw sensor signal that is indicative of the analyte concentration in the bodily fluid. For example, the analyte sensor may include two or more electrodes. The analyte sensor system may comprise sensor electronics that apply a bias condition to the electrodes. The bias condition may be, for example, a potential difference applied between a working electrode of the analyte sensor and a reference electrode of the analyte sensor. The bias condition promotes the electrochemical reaction between the analyte and the analyte sensor, resulting in a current between the working electrode and at least one other analyte sensor electrode. The raw sensor signal may be and / or may be based on the current.

[0123] The sensor electronics use the raw sensor signal to determine an estimated analyte concentration. In some examples, the sensor electronics are also programmed to output result data, which may include the estimated analyte concentration or other data. In some examples, the control circuit communicates result data to one or more other external devices.

[0124] An analyte sensor system may be arranged to measure multiple different analytes. For example, an analyte sensor system may be arranged to measure both glucose and oxygen. In some examples, an analyte sensor system arranged to measure multiple different analytes may comprise a single analyte sensor. The analyte sensor may be arranged to generate a raw sensor signal indicative of a first analyte when exposed to a first bias condition and to generate a raw sensor signal indicative of a second analyte when exposed to a second bias condition different than the first bias condition. For example, an analyte sensor, as described herein, may generate a raw sensor signal indicative of glucose when exposed to a bias condition that includes a positive potential difference between the working electrode and the reference electrode. The same analyte sensor may generate a raw sensor signal indicative of oxygen when exposed to a bias condition that includes a negative potential difference between the working electrode and the reference electrode.

[0125] Also, in some examples, an analyte sensor system for measuring multiple different analytes may comprise multiple analyte sensors. For example, the analyte sensor system may comprise a first analyte sensor arranged toAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01generate a raw sensor signal indicative of a first analyte and a second analyte sensor arranged to generate a raw sensor signal indicative of a second analyte. The sensor electronics may provide bias conditions to the first analyte sensor and the second analyte sensor. In some examples, the analyte sensor system comprises multiple analyte sensors provided on multiple different substrates, such as, for example, multiple planar substrates and / or multiple elongated conductive bodies, as described herein. Also, in some examples, the analyte sensor system comprises more than one analyte sensor implemented on a common substrate. For example, the common substrate may comprise electrodes for implementing a first analyte sensor and also comprise electrodes for implementing a second analyte sensor.

[0126] In some examples, it can be challenging to interpret a raw sensor signal indicative of an analyte, such as oxygen, for which it is difficult to obtain another in vivo sensor to provide a reference. Consider an example analyte sensor system that can be configured to generate a raw sensor signal indicative of oxygen. Because alternative in vivo oxygen sensors may not be readily available, it may not be practical to obtain a baseline in vivo oxygen concentration during the operation of the analyte sensor system. As a result, it may be challenging to calibrate the analyte sensor system by deriving one or more analyte sensor characteristics relating the raw sensor signal to the oxygen concentration.

[0127] In some examples, these and other challenges may be addressed by generating sensor-specific characteristic data describing an analyte sensor. In some examples, the analyte sensor is a multi-analyte sensor that is operable in a first mode to measure a first analyte, such as oxygen, and in a second mode to measure a second analyte.

[0128] A baseline set of such analyte sensors may be used to generate baseline characteristic data. The baseline set of analyte sensors may be repeatedly exposed to a baseline series of known concentrations of the first analyte while operating in the first mode. For example, the baseline set of analyte sensors may be exposed to a first concentration of the baseline series of known concentrations for a baseline exposure time period, then exposed to the second concentration of the baseline series of known concentrations for the baseline exposure time period, and so on. The baseline set of analyte sensorsAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01may be repeatedly exposed to the baseline series of known concentrations of the first analyte for a baseline test time period.

[0129] The raw sensor signals generated by the baseline set of analyte sensors while exposed to the concentrations of the first analyte (or samples thereof) may be compared to the know n concentrations of the first analy te to generate baseline characteristic data describing the baseline set of analyte sensors. In some examples, the baseline characteristic data describes a relationship between the known concentrations of the first analyte and the raw sensor signals. In some examples, the baseline characteristic data describes a sensitivity of the baseline set of analyte sensors to the first analyte. Also, in some examples, the baseline characteristic data describes an offset of the sensitivity.

[0130] The baseline characteristic data may be derived to describe the best fit between the raw sensor signals generated by all or a subset of the baseline set of analyte sensors and the known concentrations of the first analyte to which the set of analyte sensors were exposed. Accordingly, the baseline characteristic data may not accurately capture individual sensor-to-sensor variations.

[0131] In some examples, sensor-to-sensor variations may be accounted for by performing a pre-session analysis of a subject analyte sensor. The presession analysis may include exposing the subject analyte to a pre-session series of known concentrations of the first analyte while operating in the first mode. The subject analyte sensor may be repeatedly exposed to the pre-session series of known concentrations for a pre-session time period. The pre-session time period may be shorter than the baseline test time period. For example, the baseline test time period may be or approximate the length of a sensor session for the analyte sensors such as, for example, about 15 days. The pre-session time period, on the other hand, may be between about one and four hours. The raw sensor signal generated by the subject analyte sensor during the pre-session analysis may be used to make modifications to the baseline characteristic data considering the properties of the subject analyte sensor. This may result in subject sensor characteristic data. The subject analyte sensor may later be inserted into a host. Raw sensor signals generated by the subject analyte sensor while operating in the first mode may be used to generate a corresponding first analyte concentration using the subject analyte sensor's characteristic data.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0132] In various examples, an analyte sensor configured to generate a raw sensor signal indicative of oxygen concentration may exhibit a temperature dependency. For example, the magnitude of the raw sensor signal may depend on oxygen concentration and also on temperature at or near the analyte sensor. Various examples may address these and other challenges by applying temperature compensation to the raw sensor signal indicative of oxygen. For example, a set of baseline sensors may be exposed to a series of known concentrations of the first analyte over a range of temperatures. Oxygen temperature compensation data may be derived from the responses of the set of baseline sensors. In some examples, the oxygen temperature compensation data may indicate parameters of an Arrhenius relationship between temperature and a characteristic of the analyte sensor, such as, for example, sensitivity and / or offset.

[0133] In various examples, it can be challenging to measure an accuracy or error of an analyte sensor configured to measure an analyte, such as oxygen, for which in vivo reference sensors are not readily available. For example, a set of baseline analyte sensors may be used to generate a baseline sensor characteristic, such as a sensitivity, a sensitivity drift, and / or the like. Because it may be challenging to determine an in vivo analyte concentration, it may be similarly challenging to determine the accuracy of the baseline sensor characteristic and / or to correct the baseline sensor characteristic if desirable.

[0134] Various examples address these and other challenges by utilizing a set of baseline analyte sensors to generate a baseline sensor characteristic. The set of baseline analyte sensors may be configured to sense a first analyte when operated in a first mode and to sense a second analyte when operated in a second mode. In some examples, the first analyte may be oxygen, and the second analyte may be glucose, but other suitable analytes may be used.

[0135] A test analyte sensor may be exposed to a first concentration of the analyte. While the test analyte sensor is exposed to the first concentration of the analyte, the test analyte sensor may be transitioned from the second mode to the first mode. After the test analyte sensor is transitioned from the second mode to the first mode, a sample of a raw sensor signal generated by the test analyte sensor may be taken. The baseline sensor characteristic may be used to generate a first estimated concentration of the first analyte. An error of the test analyteAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01sensor may be determined by comparing the first estimated concentration of the first analyte to the first concentration of the analyte, which may be known. For example, the concentration of the analyte may be measured using a reference analyte sensor. In an example where the analyte is oxygen, the reference analyte sensor may be an optical oxygen probe. If the error between the first estimated concentration of the first analyte and the first concentration of the analyte is greater than a threshold, the baseline sensor characteristic may be redetermined using, for example, a different set of baseline analyte sensors and / or different techniques for determining the baseline sensor characteristic.

[0136] In some examples, the analyte sensor systems configured to measure glucose experience various faults. An example of such a fault is a compression low. During a compression low, physical pressure on the sensor insertion point results in an artificial change in the raw sensor signal. As a result, the raw sensor signal may indicate an estimated glucose concentration that is lower than the host's actual glucose concentration. Another example of such a fault is progressive sensor decline (PSD). PSD occurs at the end of an analyte sensor’s useful life. When an analyte sensor experiences PSD, the accuracy of estimated glucose concentrations generated using the analyte sensor may decline. In some examples, the raw sensor signal generated by an analyte sensor may not provide a clear indication that the analyte sensor is experiencing a fault, such as, for example, a compression low, PSD, or another sensor fault.

[0137] Various examples address these and other challenges using a multi-analyte sensor system that is operable in a first mode to measure a first analyte and in a second mode to measure a second analyte. In some examples, the first analyte may be glucose, and the second analyte may be oxygen. The multi-analyte sensor system may be configured to periodically transition from the first mode to the second mode. While in the second mode, the multi-analyte sensor system may determine an estimated concentration of the second analyte. The multi-analyte sensor system may detect a fault if the concentration of the second analyte meets a threshold condition.

[0138] The examples described herein may result in improved accuracy for analyte sensors, including, for example, analyte sensors configured to measure glucose, oxygen, and / or the like. Increased accuracy for analyte sensors may result in improved care for hosts using analyte sensors, such as, forAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01example, improved accuracy in the dosing of insulin and other medicaments, improved accuracy in the management of the host’s activities, and other improved health outcomes.

[0139] FIG. 1 is a diagram showing one example of an environment 100 including an analyte sensor system 102. The analyte sensor system 102 is coupled to a host 101, which may be a human patient. In some examples, the host is subject to a temporary or permanent diabetes condition or other health condition that makes analyte monitoring useful.

[0140] The analyte sensor system 102 includes an analyte sensor 104. In some examples, the analyte sensor 104 is or includes a glucose sensor configured to measure a glucose concentration in the host 101. The analyte sensor 104 can be exposed to an analyte at the host 101 in any suitable way. In some examples, the analyte sensor 104 is fully implantable under the skin of the host 101. In other examples, the analyte sensor 104 is wearable on the body of the host 101 (e.g., on the body but not under the skin). Also, in some examples, the analyte sensor 104 is a transcutaneous device (e.g., with a sensor residing at least partially under or in the skin of a host). It should be understood that the devices and methods described herein can be applied to any device capable of detecting a concentration of an analyte, such as glucose, and providing an output signal that represents the concentration of the analyte.

[0141] In the example of FIG. 1, the analyte sensor system 102 also includes sensor electronics 106. In some examples, the sensor electronics 106 and analyte sensor 104 are provided in a single integrated enclosure (See FIG.4). In other examples, the analyte sensor 104 and sensor electronics 106 are provided as separate components or modules (See FIG. 5). For example, the analyte sensor system 102 may include a disposable (e.g., single-use) sensor mounting unit that may include the analyte sensor 104, a component for attaching the sensor 104 to a host (e.g.. an adhesive pad), and / or a mounting structure configured to receive a sensor electronics unit including some or all of the sensor electronics 106 shown in FIGS. 2 and 3. The sensor electronics 106 may be reusable.

[0142] The analyte sensor 104 may use any know n method, including invasive, minimally invasive, or non-invasive sensing techniques (e.g., optically excited fluorescence, microneedle, transdermal monitoring of glucose), toAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01provide a raw sensor signal indicative of the concentration of the analyte in the host 101. The raw sensor signal may be converted into calibrated and / or filtered analyte concentration data used to provide a useful value of the analyte concentration (e.g., estimated blood glucose concentration level) to a user, such as the host or a caretaker (e.g., a parent, a relative, a guardian, a teacher, a doctor, a nurse, or any other individual that has an interest in the wellbeing of the host 101).

[0143] In some examples, the analyte sensor 104 is or includes a continuous glucose sensor. A continuous glucose sensor can be or include a subcutaneous, transdermal (e g., transcutaneous), and / or intravascular device. In some embodiments, such a sensor or device may recurrently (e.g.. periodically or intermittently) analyze sensor data. The glucose sensor may use any method of glucose measurement, including enzymatic, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, iontophoretic, radiometric, immunochemical, and the like. In various examples, the analyte sensor system 102 may be or include a continuous glucose monitor sensor available from DexCom™, (e.g., the DexCom G5™ sensor, Dexcom G6™ sensor, the DexCom G7™ sensor, or any variation thereof), from Abbott™ (e.g., the Libre™ sensor), or from Medtronic™ (e.g., the Enlite™ sensor).

[0144] In some examples, analyte sensor 104 includes an implantable glucose sensor, such as described with reference to U.S. Patent 6,001,067 and U.S. Patent Publication No. US-2005-0027463-A1, which are incorporated by reference. In some examples, analyte sensor 104 includes a transcutaneous glucose sensor, such as described with reference to U.S. Patent Publication No. US-2006-0020187-A1, which is incorporated by reference. In some examples, analyte sensor 104 may be configured to be implanted in a host vessel or extracorporeally, such as is described in U.S. Patent Publication No. US-2007-0027385-A1, co-pending U.S. Patent Publication No. US-2008-0119703-Al filed October 4, 2006, U.S. Patent Publication No. US-2008-0108942-A1 filed on March 26, 2007, and U.S. Patent Application No. US-2007-0197890-Al filed on February 14, 2007, all of which are incorporated by reference. In some examples, the continuous glucose sensor may include a transcutaneous sensor such as described in U.S. Patent 6,565.509 to Say et al., which is incorporated by reference. In some examples, analyte sensor 104 may include a continuousAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01glucose sensor that includes a subcutaneous sensor such as described with reference to U.S. Patent 6,579,690 to Bonnecaze et al. or U.S. Patent 6,484,046 to Say et al., which are incorporated by reference. In some examples, the continuous glucose sensor may include a refillable subcutaneous sensor, such as described with reference to U.S. Patent 6,512,939 to Colvin et al., which is incorporated by reference. The continuous glucose sensor may include an intravascular sensor such as described with reference to U.S. Patent 6,477,395 to Schulman et al., which is incorporated by reference. The continuous glucose sensor may include an intravascular sensor such as described with reference to U.S. Patent 6,424,847 to Mastrototaro et al., which is incorporated by reference.

[0145] The environment 100 may also include various other external devices, including, for example, a medical device 108. The medical device 108 may be or include a drug del i \ erx device, such as an insulin pump or an insulin pen. In some examples, the medical device 108 includes one or more sensors, such as another analyte sensor, a heart rate sensor, a respiration sensor, a motion sensor (e.g.. accelerometer), a posture sensor (e.g., 3-axis accelerometer), acoustic sensor (e.g., to capture ambient sound or sounds inside the body). The medical device 108 may be wearable, e.g., on a watch, glasses, contact lens, patch, wristband, ankle band, or another wearable item, or may be incorporated into a handheld device (e.g., a smartphone). In some examples, the medical device 108 includes a multi-sensor patch that may, for example, detect one or more of analyte levels (e.g., glucose, oxygen, lactate, insulin, or other substance), heart rate, respiration (e.g., using impedance), activity (e.g., using an accelerometer), posture (e.g., using an accelerometer), galvanic skin response, tissue fluid levels (e.g., using impedance or pressure).

[0146] In some examples, the analyte sensor system 102 and the medical device 108 communicate with one another. Communication between the analyte sensor system 102 and medical device 108 may occur over any suitable wired connection and / or via a wireless communication signal 110. For example, the analyte sensor system 102 (e.g., the sensor electronics 106 thereof) may be configured to establish a communication connection with the medical device 108 using a suitable short-range communications medium such as, for example, a radio frequency medium (e.g., Bluetooth, Medical Implant Communication System (MICS), Wi-Fi, near field communication (NFC), radio frequencyAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01identification (RFID), Zigbee, Z-Wave or other communication protocols), an optical medium (e.g., infrared), a sonic medium (e.g., ultrasonic), a cellular protocol-based medium (e.g., Code Division Multiple Access (CDMA) or Global System for Mobiles (GSM)), and / or the like.

[0147] In some examples, the environment 100 also includes other external devices, such as, for example, a wearable sensor 130. The wearable sensor 130 can include a sensor circuit (e.g., a sensor circuit configured to detect a glucose concentration or other analyte concentration) and a communication circuit, which may, for example, be an NFC circuit. In some examples, information from the wearable sensor 130 may be retrieved from the wearable sensor 130 using a user computing device 132. such as a smartphone, that is configured to communicate with the wearable sensor 130 via the wearable sensor’s communication circuit, for example, when the user device 132 is placed near the wearable sensor 130. For example, swiping the user device 132 over the sensor 130 may retrieve sensor data from the wearable sensor 130 using NFC or other suitable wireless communication.

[0148] The use of NFC communication may reduce power consumption by the wearable sensor 130, which may reduce the size of a power source (e.g., battery or capacitor) in the wearable sensor 130 or extend the usable life of the power source. In some examples, the wearable sensor 130 may be wearable on an upper arm as shown. In some examples, awearable sensor 130 may additionally or alternatively be on the upper torso of the patient (e.g., over the heart or over a lung), which may, for example, facilitate detecting heart rate, respiration, or posture. A wearable sensor 136 may also be on the lower body (e.g., on a leg) or other part of the body (e.g., on the abdomen).

[0149] In some examples, an array or network of sensors may be associated w ith the patient. For example, one or more of the analyte sensor system 102. and / or external devices, such as the medical device 108, wearable device 120 such as a watch, an additional wearable sensor 130 and / or the like, may communicate with one another via a short-range communication medium (e.g., Bluetooth, MICS, NFC, or any of the other options described above,). The additional w earable sensor 130 may be any of the examples described above with respect to medical device 108. The analyte sensor system 102, medicalAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01device 108, and additional sensor 130 on the host 101 are provided for illustration and description and are not necessarily drawn to scale.

[0150] The environment 100 may also include one or more other external devices such as a hand-held smart device (e.g., smart phone) 112, tablet 114, smart pen 116 (e.g., insulin deliver}7pen with processing and communication capability), computer 118, a wearable device 120 such as a watch, or peripheral medical device 122 (which may be a proprietary device such as a proprietary user device available from DexCom1M), any of which may communicate with the analyte sensor system 102 via a short-range communication medium, such as indicated by wireless communication signal 110. and may also communicate over a network 124 with a server system (e.g., remote data center) or with a remote terminal 128 to facilitate communication with a remote user (not shown) such as a technical support staff member or a clinician.

[0151] The wearable device 120 may include an activity sensor, a heart rate monitor (e.g., light-based sensor or electrode-based sensor), a respiration sensor (e.g.. acoustic- or electrode-based), a location sensor (e.g., GPS), or other sensors.

[0152] In some examples, the environment 100 includes a server system 126. The server system 126 can include one or more computing devices, such as one or more server computing devices. In some examples, the server system 126 is used to collect analyte data from the analyte sensor system 102 and / or analyte or other data from the plurality of other devices, and to perform analytics on collected data, generate, or apply universal or individualized models for glucose levels, and communicate such analytics, models, or information based thereon back to one or more of the devices in the environment 100. In some examples, the server system 126 gathers inter-host and / or intra-host break-in data to generate one or more break-in characteristics, as described herein.

[0153] The environment 100 may also include a wireless access point (WAP) 138 used to communicatively couple one or more of the analyte sensor system 102, network 124, server system 126, medical device 108, or any of the peripheral devices described above. For example, WAP 138 may provide Wi-Fi and / or cellular connectivity within environment 100. Other communication protocols, such as NFC or Bluetooth, may also be used among devices of the environment 100.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0154] FIG. 2 is a schematic illustration of an example analyte sensor system 200. which may for example, be the system 102 shown in FIG. 1. The analyte sensor system may include an analyte sensor 202. The analyte sensor 202 may be configured to measure glucose or another suitable analyte. The analyte sensor system 200 may also comprise one or more temperature sensors 204, a processor 210, and a memory 206. The processor 210 may access a signal indicative of an analyte concentration level from the analyte sensor 202 and access a temperature signal indicative of a temperature parameter (e.g., absolute or relative temperature or a temperature gradient) from the temperature sensor 204. Accessing the respective signals may comprise accessing directly from a source (e.g., the analyte sensor 202, the temperature sensor 204) and / or accessing from an intermediate component, such as, for example, a memory where the signal is stored. The signal indicative of the analyte concentration may be a raw sensor signal or a processed sensor signal. The sensor system 200 may also include one or more additional sensors 208, which may include, for example, a heart rate sensor, an activity sensor (e.g., accelerometer), or a pressure gauge (e g., to measure compression of the sensor against a host).

[0155] The processor 210 may determine a temperature-compensated analyte concentration level based on the temperature sensor signal and optionally also based on one or more signals from additional sensor(s) 208. The processor 210 may determine a specific temperature-compensated sensitivity value (e.g., an analyte sensor sensitivity value based on the temperature) or may determine a compensated estimated glucose value.

[0156] The temperature sensor 204 may be remote from the analyte sensor 202. For example, the temperature sensor may be positioned at or near the sensor electronics and at or near the surface of the host’s skin. Accordingly, the temperature sensor 204 may measure the temperature at the analyte sensor 202 indirectly. The signal from the temperature sensor 204 may be used as an approximation of a temperature at an analyte sensor 202, or the signal from the temperature sensor 204 may be processed (e.g., using methods described in detail below) to determine an estimated analyte temperature sensor based on the signal from the temperature sensor 204.

[0157] In some examples, the processor 210 may retrieve instructions or information from a memory 206 to determine a temperature-compensatedAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01analyte concentration level. For example, the processor may access a look-up table, or apply an algorithm based on the signal indicative of analyte concentration and temperature sensor signal, or apply the signal indicative of analyte concentration and temperature signal to a model (e.g., use a state model or neural network).

[0158] In some examples, the processor may retrieve executable instructions from the memory 206 (or a separate memory that may be operatively coupled to or integrated into the processor.) In some examples, the processor may include, or be part of, an application-specific integrated circuit (ASIC) that may be configured to determine a temperature-compensated glucose concentration level. In various examples, any one or more of the methods described herein may be executed by the processor 210 or temperature-compensated glucose sensor, either alone or in combination with other processors or devices.

[0159] FIG. 3 is a diagram showing one example of a medical device system 300 including the analyte sensor system 102 of FIG. 1. In the example of FIG. 3, the analyte sensor system 102 includes sensor electronics 106 and an example sensor mounting unit 390, although in some examples, it will be appreciated that the analyte sensor 104 and sensor electronics 106 may be included in a common enclosure. While a specific example of division of components between the sensor mounting unit 390 and sensor electronics 106 is shown, it is understood that some examples may include additional components in the sensor mounting unit 390 or in the sensor electronics 106, and that some of the components (e.g., a battery or supercapacitor) that are shown in the sensor electronics 106 may be alternatively or additionally (e.g., redundantly) provided in the sensor mounting unit 390.

[0160] In the example shown in FIG. 3, the sensor mounting unit 390 includes the analyte sensor 104 and a battery 392. In some examples, the sensor mounting unit 390 may be replaceable, and the sensor electronics 106 may include a debouncing circuit (e.g., gate with hysteresis or delay) to avoid, for example, recurrent execution of a power-up or powder down process when a batten' is repeatedly connected and disconnected or avoid processing of noise signal associated with removal or replacement of a battery.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0161] The sensor electronics 106 may include electronics components that are configured to process sensor information, such as raw sensor signals, and generate corresponding analyte concentration values. The sensor electronics 106 may, for example, include electronic circuitry associated with measuring, processing, storing, or communicating continuous analyte sensor data, including prospective algorithms associated with processing and calibration of the raw sensor signal. The sensor electronics 106 may include hardware, firmware, and / or software that enables measurement of levels of the analyte via a glucose sensor. Electronic components may be affixed to a printed circuit board (PCB) or the like and can take a variety of forms. For example, the electronic components may take the form of an integrated circuit (IC), such as an Application-Specific Integrated Circuit (ASIC), a microcontroller, and / or a processor.

[0162] In the example of FIG. 3, the sensor electronics 106 include a measurement circuit 302 (e.g., potentiostat) coupled to the analyte sensor 104 and configured to recurrently obtain analyte sensor readings using the analyte sensor 104. For example, the measurement circuit 302 may continuously or recurrently sample a raw sensor signal indicating a current flow at the analyte sensor 104 between a working electrode and a reference electrode. The sensor electronics 106 may include a gate circuit 394, which may be used to gate the connection between the measurement circuit 302 and the analyte sensor 104. For example, the analyte sensor 104 may accumulate charge over an accumulation period. After the accumulation period, the gate circuit 394 is opened so that the measurement circuit 302 can sample the accumulated charge. Gating the analyte sensor 104 may improve the performance of the sensor system 102 by creating a larger signal-to-noise or interference ratio (e.g., because charge accumulates from an analyte reaction, but sources of interference, such as the presence of acetaminophen near a glucose sensor, do not accumulate, or accumulate less than the charge from the analyte reaction).

[0163] The sensor electronics 106 may also include a processor 304. The processor 304 is configured to retrieve instructions 306 from memory 308 and execute the instructions 306 to control various operations in the analyte sensor system 102. For example, the processor 304 may be programmed to control application of bias potentials to the analyte sensor 104 via a potentiostat at theAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01measurement circuit 302, interpret raw sensor signals from the analyte sensor 104, and / or compensate for environmental factors.

[0164] The processor 304 may also save information in data storage memory 310 or retrieve information from data storage memory 310. In various examples, data storage memory 310 may be integrated with memory 308, or may be a separate memory circuit, such as a non-volatile memoiy’ circuit (e.g., flash RAM). Examples of systems and methods for processing sensor analyte data are described in more detail herein and in U.S. Patent Nos. 7,310,544 and 6,931,327.

[0165] The sensor electronics 106 may also include one or more sensors, such as the sensor 312, which may be coupled to the processor 304. The sensor 312 may be a temperature sensor, accelerometer, or another suitable sensor. The sensor electronics 106 may also include a power source such as a capacitor or battery 314, which may be integrated into the sensor electronics 106, or may be removable, or part of a separate electronics unit. The battery 314 (or other power storage component, e g., capacitor) may optionally be rechargeable via a wired or wireless (e.g., inductive or ultrasound) recharging system 316. The recharging system 316 may harvest energy' or may receive energy^ from an external source or on-board source. In various examples, the recharge circuit may include a triboelectric charging circuit, a piezoelectric charging circuit, an RF charging circuit, a light charging circuit, an ultrasonic charging circuit, a heat charging circuit, a heat harvesting circuit, or a circuit that harvests energy^ from the communication circuit. In some examples, the recharging circuit may recharge the rechargeable battery' using power supplied from a replaceable battery' (e.g., a battery supplied with a base component).

[0166] The sensor electronics 106 may also include one or more supercapacitors in the sensor electronics unit (as shown), or in the sensor mounting unit 390. For example, the supercapacitor may allow' energy to be drawn from the battery 314 in a highly consistent manner to extend the life of the battery 314. The battery 314 may recharge the supercapacitor after the supercapacitor delivers energy to the communication circuit or to the processor 304, so that the supercapacitor is prepared for delivery' of energy' during a subsequent high-load period. In some examples, the supercapacitor may be configured in parallel with the battery 314. A device may be configured to preferentially draw energy from the supercapacitor, as opposed to the batteryAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01314. In some examples, a supercapacitor may be configured to receive energy from a rechargeable battery for short-term storage and transfer energy to the rechargeable battery for long-term storage.

[0167] The supercapacitor may extend an operational life of the battery 314 by reducing the strain on the battery 314 during the high-load period. In some examples, a supercapacitor removes at least 10% of the strain off the battery during high-load events. In some examples, a supercapacitor removes at least 30% of the strain off the battery during high-load events. In some examples, a supercapacitor removes at least 30% of the strain off the battery' during high-load events. In some examples, a supercapacitor removes at least 50% of the strain off the battery during high-load events.

[0168] The sensor electronics 106 may also include a wireless communication circuit 318, which may, for example, include a wireless transceiver operatively coupled to an antenna. The wireless communication circuit 318 may be operatively coupled to the processor 304 and may be configured to wirelessly communicate with one or more peripheral devices or other medical devices, such as an insulin pump or smart insulin pen.

[0169] In the example of FIG. 3, the medical device system 300 also includes optional external devices including, for example, a peripheral device 350. The peripheral device 350 may be any suitable user computing device such as, for example, awearable device (e.g., activity monitor), such as awearable device 120. In other examples, the peripheral device 350 may be a hand-held smart device (e.g., smartphone or other device such as a proprietary handheld device available from Dexcom), a tablet 114, a smart pen 116. or a specialpurpose computer 118 shown in FIG. 1.

[0170] The peripheral device 350 may include a UI 352, a memory circuit 354, a processor 356, a wireless communication circuit 358, a sensor 360, or any combination thereof. The peripheral device 350 may not necessarily include all the components shown in FIG. 3. The peripheral device 350 may also include a power source, such as a battery.

[0171] The UI 352 may, for example, be provided using any suitable input / output device or devices of the peripheral device 350 such as, for example, a touch-screen interface, a microphone (e.g., to receive voice commands), or a speaker, a vibration circuit, or any combination thereof. The UI 352 may receiveAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01information from the host or another user (e.g., instructions, glucose values). The UI 352 may also deliver information to the host or other user, for example, by displaying UI elements at the UI 352. For example, UI elements can indicate glucose or other analyte concentration values, glucose or other analyte trends, glucose or other analyte alerts, etc. Trends can be indicated by UI elements such as arrows, graphs, charts, etc.

[0172] The processor 356 may be configured to present information to a user, or receive input from a user, via the UI 352. The processor 356 may also be configured to store and retrieve information, such as communication information (e g., pairing information or data center access information), user information, sensor data or trends, or other information in the memory circuit 354. The wireless communication circuit 358 may include a transceiver and antenna configured to communicate via a wireless protocol, such as any of the wireless protocols described herein. The sensor 360 may, for example, include an accelerometer, a temperature sensor, a location sensor, a biometric sensor, a blood glucose sensor, a blood pressure sensor, a heart rate sensor, a respiration sensor, or another physiologic sensor.

[0173] The peripheral device 350 may be configured to receive and display sensor information that may be transmitted by sensor electronics 106 (e.g., in a customized data package that is transmitted to the display devices based on their respective preferences). Sensor information (e.g., blood glucose concentration level) or an alert or notification (e.g., ‘'high glucose level”, “low glucose level”, or “fall rate alert” may be communicated via the UI 352 (e.g., via visual display, sound, or vibration). In some examples, the peripheral device 350 may be configured to display or otherwise communicate the sensor information as it is communicated from the sensor electronics 106 (e.g., in a data package that is transmitted to respective display devices). For example, the peripheral device 350 may transmit data that has been processed (e.g., an estimated analyte concentration level that may be determined by processing raw sensor data), so that a device that receives the data may not be required to further process the data to determine usable information (such as the estimated analyte concentration level). In other examples, the peripheral device 350 may process or interpret the received information (e.g., to declare an alert based on glucose values or a glucose trend). In various examples, the peripheral device 350 mayAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01receive information directly from sensor electronics 106, or over a network (e.g., via a cellular or Wi-Fi network that receives information from the sensor electronics 106 or from a device that is communicatively coupled to the sensor electronics 106).

[0174] In the example of FIG. 3, the medical device sy stem 300 includes an optional medical device 370. For example, the medical device 370 may be an external device used in addition to or instead of the peripheral device 350. The medical device 370 may be or include any suitable type of medical or other computing device including, for example, the medical device 108, peripheral medical device 122, wearable device 120, wearable sensor 130, or wearable sensor 136 shown in FIG. 1. The medical device 370 may include a UI 372. a memory circuit 374, a processor 376, a wireless communication circuit 378, a sensor 380, a therapy circuit 382, or any combination thereof.

[0175] Similar to the UI 352, the UI 372 may be provided using any suitable input / output device or devices of the medical device 370 such as, for example, a touch-screen interface, a microphone, or a speaker, a vibration circuit, or any combination thereof. The UI 372 may receive information from the host or another user (e.g., glucose values, alert preferences, calibration coding). The UI 372 may also deliver information to the host or other user, for example, by displaying UI elements at the UI 352. For example. UI elements can indicate glucose or other analyte concentration values, glucose or other analyte trends, glucose or other analyte alerts, etc. Trends can be indicated by UI elements such as arrows, graphs, charts, etc.

[0176] The processor 376 may be configured to present information to a user or receive input from a user via the UI 372. The processor 376 may also be configured to store and retrieve information, such as communication information (e.g., pairing information or data center access information), user information, sensor data or trends, or other information in the memory circuit 374. The wireless communication circuit 378 may include a transceiver and antenna configured to communicate via a wireless protocol, such as any of the wireless protocols described herein.

[0177] The sensor 380 may, for example, include an accelerometer, a temperature sensor, a location sensor, a biometric sensor, a blood glucose sensor, a blood pressure sensor, a heart rate sensor, a respiration sensor, or anotherAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01physiologic sensor. The medical device 370 may include two or more sensors (or memories or other components), even though only one sensor 380 is shown in the example in FIG. 3. In various examples, the medical device 370 may be a smart handheld glucose sensor (e.g., blood glucose meter), drug pump (e.g., insulin pump), or other physiologic sensor device, therapy device, or combination thereof.

[0178] In examples where medical device 370 is or includes an insulin pump, the pump and analyte sensor system 102 may be in two-way communication (e.g., so the pump can request a change to an analyte transmission protocol, e.g., request a data point or request data on a more frequent schedule), or the pump and analyte sensor system 102 may communicate using one-way communication (e.g., the pump may receive analyte concentration level information from the analyte sensor system). In one-way communication, a glucose value may be incorporated in an advertisement message, which may be encrypted with a previously shared key. In a two-way communication, a pump may request a value, which the analyte sensor system 102 may share, or obtain and share, in response to the request from the pump, and any or all of these communications may be encry pted using one or more previously shared keys. An insulin pump may receive and track analyte (e.g., glucose) values transmitted from analyte sensor system 102 using one-way communication to the pump for one or more of a variety of reasons. For example, an insulin pump may suspend or activate insulin administration based on a glucose value being below or above a threshold value.

[0179] In some examples, the medical device system 300 includes two or more peripheral devices and / or medical devices that each receive information directly or indirectly from the analyte sensor system 102. Because different display devices provide many different user interfaces, the content of the data packages (e.g., amount, format, and / or type of data to be displayed, alarms, and the like) may be customized (e.g., programmed differently by the manufacturer and / or by an end user) for each device. For example, referring now to the example of FIG. 1 , a plurality of different peripheral devices may be in direct wireless communication with sensor electronics 106 (e.g., such as an sensor electronics 106 that are on-skin and physically connected to the continuous analyte sensor 104) during a sensor session to enable a plurality of differentAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01types and / or levels of display and / or functionality associated w ith the displayable sensor information, or, to save battery power in the sensor system 102, one or more specified devices may communicate with the analyte sensor system 102 and relay (i.e., share) information to other devices directly or through a server system (e.g., a network-connected data center) 126.

[0180] FIG. 4 is a side view of an example analyte sensor 434 that may be implanted into a host. An enclosure 402 may be adhered to the host’s skin using an adhesive pad 408. The adhesive pad 408 may be formed from an extensible material, which may be removably attached to the skin using an adhesive. Sensor electronics may be positioned within the enclosure 402. The sensor 434 may extend from the enclosure 402 and under the skin of a host, as shown.

[0181] FIG. 5 is a side view of another example analyte sensor 534 in an arrangement including a mounting unit 514 and an electronics unit 518. The mounting unit 514 may be adhered to the host’s skin using an adhesive pad 508, which may be like the adhesive pad 408 described herein. The electronics unit 518 comprises an enclosure 502 that may have sensor electronics positioned thereon. In some examples, the electronics unit 518 and mounting unit 514 are arranged in a manner like the sensor electronics 106 and sensor mounting unit 390 shown in FIGS. 1 and 4. For example, the sensor 534 may extend from the enclosure 502 via the mounting unit 514.

[0182] FIG. 6 is an enlarged view of a distal portion of an analyte sensor 634. The analyte sensor 634 illustrates one example arrangement that may be used to implement the analyte sensors described herein, such as. for example, the analyte sensors 104, 434, 534. The analyte sensor 634 may be adapted for insertion under the host’s skin and may be mechanically coupled to an enclosure, such as the enclosures 502, and / or to a mounting unit 514, such as the mounting unit 514. The analyte sensor 634 may be electrically coupled to sensor electronics, which may be positioned within the enclosure 402, 502.

[0183] The example analyte sensor 634 shown in FIG. 6 includes an elongated conductive body 641. The elongated conductive body 641 can include a core with various layers positioned thereon. A first layer 638 that at least partially surrounds the core and includes a working electrode, for example, located in window 639. In some examples, the core and the first layer 638 areAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01made of a single material (such as, for example, platinum). In some examples, the elongated conductive body 641 is a composite of two conductive materials, or a composite of at least one conductive material and at least one non-conductive material. A membrane system 632 is located over the working electrode and may cover other layers and / or electrodes of the sensor 634, as described herein.

[0184] The first layer 638 may be formed of a conductive material. The working electrode (at window 639) is an exposed portion of the surface of the first layer 638. Accordingly, the first layer 638 is formed of a material configured to provide a suitable electroactive surface for the working electrode. Examples of suitable materials include, but are not limited to, platinum, platinum-iridium, gold, palladium, iridium, graphite, carbon, a conductive polymer, an alloy, and / or the like.

[0185] A second layer 640 surrounds at least a portion of the first layer 638, thereby defining boundaries of the working electrode. In some examples, the second layer 640 serves as an insulator and is formed of an insulating material, such as polyimide, polyurethane, parylene, or any other suitable insulating material or materials. In some examples, the second layer 640 is configured such that the working electrode (of the layer 638) is exposed via the window 639.

[0186] In some examples, the sensor 634 further includes a third layer 643 comprising a conductive material. The third layer 643 may comprise a reference electrode. In some examples, the third layer 643, including the reference electrode, is formed of a silver-containing material that is applied onto the second layer 640 (e.g., an insulator). The silver-containing material may include various materials and be in various forms, such as, for example, Ag / AgCl-polymer pasts, paints, polymer-based conducting mixtures, inks, etc.

[0187] The analyte sensor 634 may include two (or more) electrodes, e.g., a working electrode at the layer 638 and exposed at window 639, and at least one additional electrode, such as a reference electrode of the layer 643. In the example arrangement of FIGS. 6-7, the reference electrode also functions as a counter electrode, although other arrangements can include a separate counter electrode. While the analyte sensor 634 may be used with a mounting unit in some examples, in other examples, the analyte sensor 634 may be used withAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01other types of sensor systems. For example, the analyte sensor 634 may be part of a system that includes a battery and sensor in a single package, and may optionally include, for example, anear-field communication (NFC) circuit.

[0188] FIG. 7 is a cross-sectional view through the sensor 634 of FIG. 6 on plane 2-2 illustrating a membrane system 632. The membrane system 632 may include a number of domains (e.g., layers). In an example, the membrane system 632 may include an enzyme domain 642, a diffusion resistance domain 644, and a bioprotective domain 646 located around the working electrode. In some examples, a unitary diffusion resistance domain and bioprotective domain may be included in the membrane system 632 (e g., wherein the functionality of both the diffusion resistance domain and bioprotective domain are incorporated into one domain).

[0189] The membrane system 632, in some examples, also includes an electrode layer 647. The electrode layer 647 may be arranged to provide an environment between the surfaces of the working electrode and the reference electrode that facilitates the electrochemical reaction between the electrodes. For example, the electrode layer 647 may include a coating that maintains a layer of water at the electrochemically reactive surfaces of the sensor 634.

[0190] In some examples, the sensor 634 may be configured for shortterm implantation (e.g.. from about 1 to 30 days). However, it is understood that the membrane system 632 can be modified for use in other devices, for example, by including only one or more of the domains, or additional domains. For example, a membrane system 632 may include a plurality of resistance layers or a plurality of enzyme layers. In some examples, the resistance domain 644 may include a lurality of resistance layers, or the enzyme domain 642 may include a plurality of enzyme layers.

[0191] The diffusion resistance domain 644 may include a semipermeable membrane that controls the flux of oxygen and glucose to the underlying enzyme domain 642. As a result, the upper limit of linearity of glucose measurement is extended to a much higher value than that which is achieved without the diffusion resistance domain 644.

[0192] In some examples, the membrane system 632 may include a bioprotective domain 646, also referred to as a domain or biointerface domain, comprising a base polymer. However, the membrane system 632 of someAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01examples can also include a plurality of domains or layers, including, for example, an electrode domain, an interference domain, or a cell-disruptive domain, such as described in more detail elsewhere herein and in U.S. Patent Nos. 7,494,465, 8,682,608, and 9,044,199, which are incorporated herein by reference in their entirety.

[0193] It is to be understood that sensing membranes modified for other sensors, for example, may include fewer or additional layers. For example, in some examples, the membrane system 632 may comprise one electrode layer, one enzyme layer, and two bioprotective layers, but in other examples, the membrane system 632 may comprise one electrode layer, two enzyme layers, and one bioprotective layer. In some examples, the bioprotective layer may be configured to function as the diffusion resistance domain 644 and control the flux of the analyte (e.g., glucose) to the underlying membrane layers.

[0194] In some examples, one or more domains of the sensing membranes may be formed from materials such as silicone, polytetrafluoroethylene, polyethylene-co-tetrafluoroethylene, polyolefin, polyester, polycarbonate, biostable polytetrafluoroethylene, homopolymers, copolymers, terpolymers of polyurethanes, polypropylene (PP),poly viny lchloride (PVC), polyvinylidene fluoride (PVDF), polybutylene terephthalate (PBT), polymethylmethacrylate (PMMA), polyether ether ketone (PEEK), polyurethanes, cellulosic polymers, poly(ethylene oxide), polypropylene oxide) and copolymers and blends thereof, polysulfones and block copolymers thereof including, for example, di-block, tri-block, alternating, random and graft copolymers.

[0195] In some examples, the sensing membrane can be deposited on the electroactive surfaces of the electrode material using known thin or thick film techniques (for example, spraying, electro-depositing, dipping, or the like). The sensing membrane located over the working electrode does not have to have the same structure as the sensing membrane located over the reference electrode; for example, the enzy me domain 642 deposited over the working electrode does not necessarily need to be deposited over the reference or counter electrodes.

[0196] Although the examples illustrated in FIGS. 6-7 involve circumferentially extending membrane systems, the membranes described herein may be applied to any planar or non-planar surface, for example, the substrate-Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01based sensor structure of U.S. Pat. No. 6.565,509 to Say et al., which is incorporated by reference.

[0197] In an example in which the analyte sensor 634 is a glucose sensor, glucose analyte can be detected utilizing glucose oxidase. Glucose oxidase reacts with glucose to produce hydrogen peroxide (H2O2). The hydrogen peroxide reacts with the surface of the working electrode, producing two protons (2H+), two electrons (2e") and one molecule of oxygen (O2). This produces an electronic current that may be detected by the sensor electronics 106. The amount of current is a function of the glucose concentration level. A calibration cur e may be used to provide an estimated glucose concentration level based on a measured current. The amount of current is also a function of the diffusivity of glucose through the sensor membrane. The glucose diffusivity may change over time, which may cause the sensor glucose sensitivity7to change over time, or “drift.”

[0198] FIG. 8 is a schematic illustration of a circuit 800 that represents the behavior of an example analyte sensor, such as the analyte sensor 634 shown in FIGS. 6-7. As described above, the interaction of hydrogen peroxide (generated from the interaction between glucose analyte and glucose oxidase) and working electrode (WE) 804 produces a voltage differential between the working electrode (WE) 804 and reference electrode (RE) 806 which drives a current. The current may make up all or part of a raw sensor signal that is measured by sensor electronics, such as the sensor electronics 106 of FIGS. 1-2, and used to estimate an analyte concentration (e.g., glucose concentration).

[0199] The circuit 800 also includes a double-layer capacitance (Cdl) 808, which occurs at an interface between the working electrode (WE) 804 and the adjacent membrane (not shown in FIG. 8, see, e.g., FIGS. 6-7 above). The double-layer capacitance (Cdl) may occur at an interface between the working electrode 804 and the adjacent membrane due to the presence of two layers of ions with opposing polarity, as may occur during application of an applied voltage between the working electrode 804 and the reference electrode 806. The equivalent circuit 800 may also include a polarization resistance (Rpol) 810, which may be relatively large, and may be modeled, for example, as a static value (e.g., 100 mega-Ohms), or as a variable quantity that varies as a function of glucose concentration level.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0200] An estimated analyte concentration may be determined from a raw sensor signal based upon a measured current (or charge flow) through the analyte sensor membrane 812 when a bias potential is applied to the sensor circuit 800. For example, sensor electronics or another suitable computing device can use the raw sensor signal and a sensitivity of the sensor, which correlates a detected current flow to a glucose concentration level, to generate the estimated analyte concentration. In some examples, the device also uses a break-in characteristic, as described herein.

[0201] With reference to the equivalent circuit 800, when a voltage is applied across the working and reference electrodes 804 and 806, a current may be considered to flow (forward or backward depending on polarity ) through the internal electronics of transmitter (represented by R_Tx_intemal) 811; through the reference electrode (RE) 806 and working electrode (WE) 804, which may be designed to have a relatively low resistance; and through the sensor membrane 812 (Rmembr, which is relatively small). Depending on the state of the circuit, current may also flow through, or into, the relatively large polarization resistance 810 (which is indicated as a fixed resistance but may also be a variable resistance that varies with the body’s glucose level, where a higher glucose level provides a smaller polarization resistance), or into the double-layer capacitance 808 (i.e., to charge the double-layer membrane capacitor formed at the working electrode 804), or both.

[0202] The impedance (or conductance) of the membrane (Rmembr) 812 is related to electrolyte mobility in the membrane, which is in turn related to glucose diffusivity in the membrane. As the impedance goes down (i.e.. conductance goes up, as electrolyte mobility in the membrane 812 goes up), the glucose sensitivity goes up (i.e., a higher glucose sensitivity means that a particular glucose concentration will produce a larger signal in the form of more current or charge flow). Impedance, glucose diffusivity, and glucose sensitivity are further described in U.S. Patent Publication No. US2012 / 0262298, which is incorporated by reference in its entirety.

[0203] FIG. 9 is a diagram showing one example of a workflow 900 that may be executed by sensor electronics 106 to generate an estimated analyte concentration 926. A raw sensor signal 916 is sampled, with samples of the raw sensor signal 916 being provided to a Kalman filter 902. The Kalman filter 902Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01may generate a desired sensor signal 920. The desired sensor signal 920 is an estimated true value of the sensor signal 920, absent noise and other potentially confounding factors. Although FIG. 9 shows a Kalman fdter 902, it will be appreciated that other filters and / or other processing algorithms may be used to generate the desired sensor signal 920. The outputs of the Kalman filter 902 may include the desired sensor signal 920 and a rate-of-change of the raw sensor signal 916, represented in FIG. 9 as the rate-of-change (ROC) 927. The rate-of-change 927 may indicate a current rate-of-change of the raw sensor signal 916 associated w ith the most recent sample of the raw sensor signal 916 provided to the Kalman filter 902. In some examples, each sample of the raw sensor signal 916 is converted to a corresponding desired sensor signal 920 value that depends on the most recent sample of the raw sensor signal 916 as well as on previous samples of the raw sensor signal 916.

[0204] The desired sensor signal 920 generated by the Kalman filter 902 is provided to a temperature compensation block 910. At the temperature compensation block 910, the sensor electronics 106 may apply a temperature compensation to the desired sensor signal 920. The temperature compensation applied at block 910 may correct the sensor signal (e.g., the desired sensor signal 920) for changes in the behavior of the analyte sensor due to temperature. For example, temperature can influence the catalytic rate of the immobilized enzyme at the membrane as well as, for example, because structural and / or morphological changes to the analyte sensor membrane. Execution of the temperature compensation block 910 may be based on the desired sensor signal 920, the rate-of-change 927 generated by the Kalman fdter 902, and an analyte sensor temperature 922.

[0205] In some examples, the analyte sensor temperature 922 may be measured at the analyte sensor, such as at the w orking electrode of the analyte sensor. In other examples, the analyte sensor temperature 922 is derived from a temperature sensor at another location. In the example of FIG. 9, a raw temperature signal 918 is generated by a temperature sensor positioned remote from the analyte sensor, such as described herein with respect to the temperature sensor 204. For example, the temperature sensor generating the raw temperature signal 918 may be positioned at or near the sensor electronics and, for example, at the surface of the host’s skin. Samples of the transmitter temperature signalAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01may be provided to a linear time invariant (LTI) filter 904. The LTI filter 904 may output an approximation of the analyte sensor temperature 922 based on the raw temperature signal 918.

[0206] The output of the temperature compensation block 910 may be a temperature-compensated signal 928, which is provided to an estimated analyte concentration conversion block 912. At the estimated analyte concentration conversion block 912, the sensor electronics 106 may convert the temperature-corrected sensor signal to an analyte concentration, for example, utilizing the sensitivity of the analyte sensor. The result may be an interstitial analyte concentration 930 describing the analyte concentration at the host’s interstitial region, which is directly measured by the analyte sensor.

[0207] The interstitial analyte concentration is input to a time lag compensation (TLC) block 914. At block 914, the sensor electronics 106 may convert the interstitial analyte concentration to a blood analy te concentration 926. In some examples, the sensor electronics may apply the TLC block as indicated by Equation [1] below:EAC = EAC[ntersi ia+ ROC * PH * Trend Computed[1] In Equation [1], EAVB is the estimated blood analyte concentration.EACin ersti iaiis the estimated interstitial analyte concentration (e.g., the input to the block 914 ). ROC is the rate-of-change 924. The rate-of-change 924 may be provided to the TLC by the temperature compensation block 910, and / or may be received directly from the Kalman filter 902. For example, the rate-of-change 927 may be provided directly to the block 914. PH may stand for prediction horizon and represents a time lag between blood analyte concentration and interstitial analyte concentration. Trend computed is a direction or sign of the rate of change describing the estimated interstitial analyte concentration.

[0208] As described herein, the Kalman filter 902, or other suitable filter for generating a desired sensor signal 920, may introduce a time lag between the raw sensor signal 916 and the desired sensor signal 920. As a result, fast changes in the raw sensor signal 916, such as changes due to sudden changes in temperature, may not be quickly reflected in the desired sensor signal 920. As a result, temperature compensation applied at the temperature compensation blockAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01910 may overcorrect for temperature effects in the raw sensor signal 916 that are attenuated by the Kalman filter 902.

[0209] Also, in some examples, applying the sensor sensitivity to the temperature-compensated sensor signal 928 may include considering changes to the sensitivity resulting from temperature effects. For example, the temperature compensation block 910 may also output a temperature correction factor.Example Equation [2] is an illustration of how temperature sensitivity may be taken into account at the estimated analyte concentration conversion block 912:In Equation [2], AC is analyte concentration. S is the temperature-compensated signal 928. The value mt is the sensitivity of the analyte sensor and tempCorrection is the temperature correction factor. In some examples, the rate-of-change 927 is also temperature-compensated, for example, according to Equation [3] below:>In Equation [3], is the rate-of-change determined by the Kalman filter 902. In various implementations, however, it may be computationally burdensome to calculate the temperature-compensated rate-of-change according to Equation [3], Accordingly, in some examples, the temperature-compensated rate-of-change may be simplified as given by Equation [4] below:dSTC Rate of Change = — - - — - mt{ 1 + tempCorrection)[4]

[0210] FIG. 10 is a diagram showing one example of an analyte sensor system 1000 configured to measure a first analyte and a second analyte. The analyte sensor system 1000, in various examples, may be arranged in a manner similar to that of the analyte sensor system 102 described herein. In this example, the analyte sensor system 1000 comprises an analyte sensor 1004 thatAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01is configured to generate a raw sensor signal indicative of the first analyte when exposed to a first bias condition and indicative of the second analyte when exposed to a second bias condition.

[0211] The analyte sensor system 1000 comprises the analyte sensor 1004 and sensor electronics 1002. The analyte sensor 1004 may be arranged, for example, as described herein with respect to FIGS. 6 and 7. The sensor electronics 1002 may include a bias circuit 1010, an analyte concentration module 3806, and a fault module 1008.

[0212] The bias circuit 1010 may comprise various power supplies, regulators, and / or the like to provide a bias condition to the analyte sensor 1004. The bias condition provided to the analyte sensor 1004 may configure the analyte sensor to measure the first analyte or the second analyte. A bias condition of about 600 mV between a working electrode and a reference electrode of the analyte sensor 1004 may configure the analyte sensor 1004 to generate a raw sensor signal indicative of glucose. In some examples, the bias condition to configure the analyte sensor 1004 to measure glucose may be between about 400 mV and about 800 mV. A bias condition of about -200 mV between the working electrode and the reference electrode of the analyte sensor 1004 configures the analyte sensor 1004 to generate a raw sensor signal indicative of oxygen. In some examples, the bias condition to configure the analyte sensor 1004 to measure oxygen may be between about -400 mV and 0 mV.

[0213] Sensor data generated by the analyte sensor 1004 may be provided to the analyte concentration module 1006 and the fault module 1008. The sensor data may include, for example, samples of the raw sensor signal generated by the analyte sensor 1004. The analyte concentration module 3806 and fault module 1008 may be or include an executable code that may be executed by a processor of the sensor electronics 1002. For example, the analyte concentration module 3806 may be executed to receive the sensor data and generate corresponding estimated analyte concentration data 1018. The analyte concentration data 1018 may comprise one or more estimated concentrations of the first analyte and / or one or more estimated concentrations of the second analyte. The display 1012 may be any suitable device having a wired and / or wireless connection to the sensor electronics 1002. In some examples, theAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01display 1012 is or includes one or more of the devices 132, 114, 118, 112, 122, 128 described herein.

[0214] The fault module 1008 may be executed to access sensor data generated by the analyte sensor 1004 such as, for example, samples of the raw sensor signal indicating a first analyte, samples of the raw sensor signal indicative a second analyte, estimated first analyte concentrations generated from the raw sensor signal, estimated second analyte concentrations generated from the raw sensor signal, and / or any suitable intermediate processing between the raw sensor signal and the estimated glucose concentration. Using the sensor data, the fault module 1008 may determine a performance decline metric describing the analyte sensor 1004. The performance decline metric may describe a likelihood that the analyte sensor 1004 is experiencing performance decline, for example, due to PSD or another end-of4ife state. In some examples, the performance decline metric may describe a severity' of performance decline due to PSD or another end-of-life state. In some examples, the fault module 1008 executes a trained computerized model 1014. The trained computerized model 1014 may receive some or all of the sensor data as input and may generate a value for the sensor performance metric, where the value of the sensor performance metric is indicative of whether the analyte sensor 1004 is currently experiencing a fault. The fault may be a temporary fault and / or may be associated with an end-ol-lile state.

[0215] If the sensor performance decline metric generated by the trained computerized model 1014 indicates PSD or another fault condition at the analyte sensor 1004, the sensor electronics 1002 may execute a responsive action. The fault module 1008 may be configured to initiate a responsive action that is responsive to the determined value of the performance decline metric.

[0216] In some examples, the responsive action includes providing a fault alert 1016 to the display 1012. The display 1012 may provide an output indicative of the fault alert 1016 to alert a host or other user of the existence of the end-of-life state or other fault condition. In some examples, the responsive action includes disabling the display 1012 and / or preventing the display from indicating the estimated analyte concentration data 1018. This may prevent the host or other user from relying on estimated analyte concentration data 1018 generated by an analyte sensor 1004 that is in an end-of-life state or other faultAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01condition. Also, in some examples, the responsive action may include ending a sensor session for the analyte sensor 1004. This may include, for example, ceasing to provide the bias signal to the analyte sensor 1004.

[0217] The trained computerized model 1014 may be any suitable trained computerized model, such as a neural network. A neural network is a type of computerized model that is constructed from interconnected cells divided into layers. Each cell is implemented as a function executed by the sensor electronics or other processor or computing system. The sensor electronics or other system implements a cell by processing cell input data and generating corresponding cell output data according to the cell's function. In some examples, the cells of the neural network are arranged in layers. An input layer of cells receives an input to the neural network. The inputs to cells making up the intermediate layers may include outputs of cells in the input layer and / or outputs of other cells in the intermediate layers. Cells in an output layer receive inputs generated, for example, by cells in the intermediate layers and generate outputs. The outputs of the cells of the output layer may represent the sensor decline metric or other output of the neural network.

[0218] A neural network may be trained utilizing labeled training data to generate an indication of whether the analyte sensor 1004 is in an end-of-life state or other fault condition. Labeled training data comprises instances of input data and a corresponding output. In the context of a continuous analyte sensor, for example, labeled training data may comprise sensor signal data describing the sensor signal over a time period and a corresponding label indicating a state of the analyte sensor, such as whether the sensor signal data describes an analyte sensor that is in and end-of4ife state or otherwise experiencing a fault.

[0219] Training may be conducted in a series of training epochs. In each training epoch, some or all of the training data may be provided as input to the neural network. The output of the neural network may be compared to the corresponding label or labels to determine a loss of the neural network. The loss describes a difference between the output of the neural network and the label or labels indicating the correct output for the corresponding input. The loss is used to modify the neural network, for example, by modifying weights or other portions of the functions implemented by the individual cells. In some examples, a predetermined number of training epochs are performed. Also, in someAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01examples, training epochs are performed until the loss of the neural network meets a threshold. Various different techniques may be used to determine the loss and to determine updates to neural network cells during a training epoch. Example techniques include, gradient descent and back propagation.

[0220] In some examples, the trained computerized model 1014 is or includes a Recurrent Neural Network (RNN). An RNN is a bidirectional neural network. For example, some cells of the RNN may receive input that is based on the outputs of previous cells and may, additionally or alternatively, receive input that depends on a previous output of the same cell. An example RNN is an LSTM or LSTM layer. Also, in some examples, the trained computerized model 1014 is or includes a Convolutional Neural Network (CNN). A CNN is a unidirectional or feed-forward neural network in which the inputs to the cells are based on the outputs of cells from prior layers.

[0221] FIG. 11 is a flowchart showing one example of a process flow 1100 that may be executed in the analyte sensor system 1000, for example, by the sensor electronics 1002, to measure a first analyte and a second analyte. In the example of FIG. 11 , the analyte sensor system 1000 is configured to measure the concentration of the first analyte for a first analyte period and then measure the concentration of the second analyte for a second analyte period.

[0222] At operation 1102, the sensor electronics 1002 may configure the analyte sensor system 1000 to measure the first analyte. This may include the bias circuit 1010 biasing the analyte sensor 1004 to a first bias condition as described herein. At operation 1104, the sensor electronics 1002 may sample the raw sensor signal generated by the analyte sensor 1004 while biased with the first bias condition. This raw sensor signal may be indicative of the first analyte. At operation 1106, the sensor electronics 1002 processes the sample of the raw sensor signal taken at operation 1104. This may include, for example, determining a first analyte concentration based on the sample of the raw sensor signal. The first analyte concentration may be an estimated concentration of the first analyte. Processing the sample of the raw sensor signal may also include, for example, storing the sample of the raw sensor signal for later processing.

[0223] At operation 1108, the sensor electronics 1002 may determine if the first analyte period has ended. If the first analyte period has not ended, the sensor electronics 1002 may return to operation 1104 and take another sample ofAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01the raw sensor signal. In some examples, the operations 1104, 1106, and 1108 may be performed during the first analyte time period at a first analyte sample rate. This may result in samples of the raw sensor signal being taken at operation 1104 according to the first analyte sample rate. The first analyte sample rate may be any suitable rate such as, for example, between about 1 Hz and 1 / 60 Hz. In some examples, the sample rate is about 1 / 30 Hz.

[0224] If the first analyte period has ended, then the sensor electronics 1002 may, at operation 1110 configure the analyte sensor system 1000 to measure the second analyte. This may include the bias circuit 1010 biasing the analyte sensor 1004 to a second bias condition, for example, as described herein. At operation 1112, the sensor electronics 1002 may take a sample of the raw sensor signal. At operation 1114, the sensor electronics 1002 may process the sample of the raw sensor signal taken at operation 1112. This may include, in some examples, determining a second analyte concentration value using the sample of the raw sensor signal from operation 1112. For example, the sensor electronics 1002 may determine a concentration of the second analyte.Processing the sample of the raw sensor signal taken at operation 1112 may also include, for example, storing the sample for later processing.

[0225] At operation 1116, the sensor electronics 1002 may determine if the second analyte period has ended. If the second analyte period has not ended, then the sensor electronics 1002 may return to operation 1 112 and take another sample of the raw sensor signal. The operations 1112, 1114, and 1116 may be performed during the second analyte period at a second analyte sample rate. The second analyte sample rate may be the same as the first analyte sample rate, or different than the first analyte sample rate. In some examples, the second analyte sample rate is between about 10 Hz and about 1 / 30 Hz. In some examples, the second analyte sample rate is about 1 / 18 Hz.

[0226] If the second analyte period has ended, then the sensor electronics 1002 may return to operation 1102 and configure the analyte sensor system 1000 to measure the first analyte. In some examples, the process flow 1100 may execute repeatedly during a sensor session of the analyte sensor system 1000. The sensor session may be of any suitable time such as, for example, between about 5 days and about 20 days. In some examples, the sensor session may be about 15 days.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0227] FIG. 12 shows a plot 1200 illustrating bias conditions provided by the bias circuit 1010 to the analyte sensor 1004 of FIG. 10 in one example implementation of the process flow 1100 of FIG. 11. In this example, the first analyte is oxygen. The first bias condition includes the provision of a -200 mV potential difference between the working electrode and the reference electrode of the analyte sensor 1004. Also, in this example, the second analyte is glucose, and the second bias condition includes the provision of a 600 mV potential difference between the working electrode and the reference electrode of the analyte sensor 1004.

[0228] As shown, the first analyte time period is about 3 minutes, and the first analyte sample rate is 1 / 18 Hz. Accordingly, 10 samples of the raw sensor signal are taken during the 3-minute first analyte time period while the analyte sensor 1004 is biased to measure the first analyte. The second analyte time period is 57 minutes, and the second analyte sample rate is 1 / 30 Hz.Accordingly, 114 samples of the raw sensor signal were collected during the 57-minute second analyte time period, while the analyte sensor 1004 is biased to measure the second analyte.

[0229] FIG. 13 is a diagram showing one example of a workflow 1300 that may be executed by the sensor electronics 1002 of FIG. 10 to generate an estimated analyte concentration 1334 of the first analyte and an estimated analyte concentration 1326 of the second analyte. In the workflow 1300, a raw sensor signal 1316 is sampled. Samples of the raw sensor signal are provided to a Kalman filter 1302, which may operate similarly to the Kalman filter 902. The outputs of the Kalman filter 1302 may include the desired sensor signal 1320 and a ROC of the raw sensor signal 1316.

[0230] An analyte sensor temperature 1322 may also be determined. In some examples, the analyte sensor temperature 1322 may be measured at the analyte sensor 1004, such as at the working electrode of the analyte sensor. In other examples, the analyte sensor temperature 1322 is derived from a temperature sensor at another location. In the example of FIG. 13, a raw temperature signal 1318 is generated by a temperature sensor positioned remote from the analyte sensor, such as described herein with respect to the temperature sensor 204. For example, the temperature sensor generating the raw temperature signal 1318 may be positioned at or near the sensor electronics 1002 and, forAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01example, at the surface of the host's skin. Samples of the transmitter temperature signal may be provided to LTI filter 1304, which may output an approximation of the analyte sensor temperature 1322 based on the raw temperature signal 1318.

[0231] The desired sensor signal 1320, the ROC 1327 and / or the analyte sensor temperature 1322 may be provided to a first analyte workflow branch 1301 and / or to a second analyte workflow branch 1303.

[0232] The first analyte workflow branch 1301 may be executed when the analyte sensor system 1000 is configured to generate the raw sensor signal 1316 indicative of the first analyte. In some examples, the first analyte workflow branch 1301 is performed as all or part of the operation 1106 of the process flow 1100. The first analyte workflow branch 1301 may utilize one or more of the desired sensor signal 1320, ROC 1327, and / or analyte sensor temperature 1322 to generate the estimated concentration 1334 of the first analyte. For example, the desired sensor signal 1320 and analyte sensor temperature 1322 may be provided to a temperature compensation block 1328. The temperature compensation block 1328 may generate a temperature-compensated signal 1330. The temperature-compensated signal 1330 may be provided to a first estimated analyte concentration conversion block 1332. The first estimated analyte concentration conversion block 1332 may apply one or more sensor characteristics to the temperature-compensated signal 1330 to generate the first analyte estimated concentration 1334. In some examples, the first estimated analyte concentration conversion block 1332 may apply a first analy te sensitivity and / or a first analyte offset.

[0233] The second analyte workflow branch 1303 may be executed when the analyte sensor system 1000 is configured to generate the raw sensor signal 1316 indicative of the second analyte. In some examples, the second analyte workflow branch 1303 is performed as all or part of the operation 1114 of the process flow 1100. The second analyte workflow branch 1303 may use the desired sensor signal 1320, the ROC 1327, and / or the analyte sensor temperature 1322 to generate an estimated analyte concentration 1326 of the second analyte.

[0234] The desired sensor signal 1320 generated by the Kalman filter 1302 is provided to a temperature compensation block 1310. At the temperature compensation block 1310, the sensor electronics 106 may apply a temperatureAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01compensation to the desired sensor signal 1320. The temperature compensation applied at block 1310 may correct the sensor signal (e.g., the desired sensor signal 1320) for changes in the behavior of the analyte sensor due to temperature. In some examples, the temperature compensation block 1310 operates in a manner similar to that described herein with respect to the temperature compensation block 910.

[0235] The output of the temperature compensation block 1310 may be a temperature-compensated signal 1330, which is provided to an estimated analyte concentration conversion block 1312. At the estimated analyte concentration conversion block 1312, the sensor electronics 1002 may convert the temperature-corrected sensor signal to an analyte concentration, for example, utilizing the sensitivity of the analyte sensor. The result may be an interstitial analyte concentration 1313 describing the concentration of the second analyte at the host’s interstitial region, which is directly measured by the analyte sensor.

[0236] The interstitial analyte concentration 1313 may be input to a TLC block 1314 that may operate in a manner similar to the 914 described herein. The TLC block 1314 may operate on the interstitial analyte concentration 1313 and the rate-of-change 1324 to generate the estimated concentration 1326 of the second analyte.

[0237] FIG. 14 is a diagram showing another example of an analyte sensor system 1400 configured to measure a first analyte and a second analyte using a first analyte sensor 1404 and a second analyte sensor 1405. The analyte sensor system 1400, in various examples, may be arranged in a manner similar to that of the analyte sensor system 102 described herein. In this example, the analyte sensor 1404 is configured to generate a raw sensor signal indicative of the first analyte, and the analyte sensor 1405 is configured to generate a raw sensor signal indicative of the second analyte. In some examples, the analyte sensors 1404, 1405 may be provided on distinct substrates, such as distinct planar substrates or distinct elongated conductive bodies similar to those described with respect to FIGS. 6 and 7. For example, the analyte sensor system 1400 may comprise two different substrates. A first substrate may be arranged to comprise electrodes for implementing the analyte sensor 1404. A second elongated conductive body may be arranged to comprise electrodes for implementing the analyte sensor 1405. The distinct substrates may both beAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01inserted into the host, for example, in a common wound pocket. In other examples, the analyte sensor system 1400 may comprise a single substrate (e.g., a planar substrate, an elongated conductive body, and / or the like) that is arranged to include electrodes for implementing both the analyte sensor 1404 and the analyte sensor 1405. For example, a first set of electrodes on the single substrate may implement the analyte sensor 1404, and a second set of electrodes on the single substrate may implement the analyte sensor 1405. In addition to the analyte sensor 1404 and the analyte sensor 1405, the analyte sensor system 1400 comprises sensor electronics 1402. The sensor electronics 1402 may include a bias circuit 1410, an analyte concentration module 1406, and a fault module 1408.

[0238] The bias circuit 1410 may comprise various power supplies, regulators, and / or the like to provide bias conditions to the analyte sensors 1404, 1405. The bias condition provided to the analyte sensor 1404 may configure the analyte sensor 1404 to measure the first analyte, while the bias condition provided to the analyte sensor 1405 may configure the analyte sensor 1405 to measure the second analyte.

[0239] Sensor data generated by the respective analyte sensors 1404, 1405 may be provided to the analyte concentration module 1406 and the fault module 1408. which may operate in a manner similar to the analyte concentration module 1406 and the fault module 1008. For example, the analyte concentration module 1406 may be executed to receive the sensor data and generate corresponding estimated analyte concentration data 1418. The analyte concentration data 1418 may comprise one or more estimated concentrations of the first analyte and / or one or more estimated concentrations of the second analyte. The display 1412 may be any suitable device having a wired and / or wireless connection to the sensor electronics 1402. In some examples, the display 1412 is or includes one or more of the devices 132, 114, 118, 112. 122, 128 described herein. The fault module 1408 may operate in a manner similar to the fault module 1008, for example, by executing a trained computerized model 1414 to detect a fault in a raw sensor signal generated by the analyte sensor 1404 and / or by the analyte sensor 1405. If a fault is detected, the fault module 1408 may perform a responsive action, such as, for example, providing a fault alert 1416 to the display 1412.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0240] FIG. 15 is a diagram showing one example of a workflow' 1500 that may be executed by the sensor electronics 1402 of FIG. 14 to generate an estimated analyte concentration 1534 of the first analyte and an estimated analyte concentration 1526 of the second analyte. In the w orkflow- 1500, a raw sensor signal 1516 is generated by the analyte sensor 1404, and a raw sensor signal 1550 is generated by the analyte sensor 1405. Accordingly, the raw sensor signal 1516 is indicative of the first analyte, and the raw sensor signal 1550 is indicative of the second analyte.

[0241] The raw sensor signal 1516 may be sampled, with samples provided to a Kalman filter block 1502 that generates a desired sensor signal 1520. The raw sensor signal 1550 may also be sampled, with samples provided to another Kalman filter block 1551 that generates a desired sensor signal 1554 and a rate-of-change 1556. A raw' temperature signal 1518 is generated by a temperature sensor, for example, as described herein. The raw temperature signal 1518 may be processed by an LTI block 1504, which may operate in a manner similar to the LTI filters 1304, 904, to generate an analyte sensor temperature 1522.

[0242] The desired sensor signal 1520 and analyte sensor temperature 1522 are provided to a first analyte orkflow branch 1501 which may generate an estimated concentration 1534 of the first analyte. The first analyte workflow branch may utilize a temperature compensation block 1528 to generate a temperature-compensated signal 1531. The temperature-compensated signal 1531 may be provided to a first estimated analyte concentration conversion block 1532. The temperature compensation block 1528 and the first estimated analyte concentration conversion block 1532 may operate in a manner similar to the temperature compensation block 1328 and the first estimated analyte concentration conversion block 1332 described herein.

[0243] The desired sensor signal 1550, rate-of-change 1556, and analyte sensor temperature 1522 may be provided to a second analyte workflow branch 1503. The second analyte workflow branch 1503 may use the desired sensor signal 1520, the ROC 1524, and / or the analyte sensor temperature 1522 to generate an estimated analyte concentration 1526 of the second analyte.

[0244] The desired sensor signal 1520 generated by the Kalman filter block 1502 is provided to a temperature compensation block 1510. At theAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01temperature compensation block 1510, the sensor electronics 106 may apply a temperature compensation to the desired sensor signal 1520. The temperature compensation applied at block 1510 may correct the sensor signal (e.g., the desired sensor signal 1520) for changes in the behavior of the analyte sensor due to temperature. In some examples, the temperature compensation block 1510 operates in a manner similar to that described herein with respect to the temperature compensation blocks 1310, 910.

[0245] The output of the temperature compensation block 1510 may be a temperature-compensated signal 1529 which is provided to an estimated analyte concentration conversion block 1512. At the estimated analyte concentration conversion block 1512. the sensor electronics 1002 may convert the temperature-corrected sensor signal to an analyte concentration, for example, utilizing the sensitivity’ of the analyte sensor. The result may be an interstitial analyte concentration 1530 describing the concentration of the second analyte at the host’s interstitial region, which is directly measured by the analyte sensor.

[0246] The interstitial analyte concentration 1530 may be input to a TLC block 1514 that may operate in a manner similar to the TLC blocks 1314, 914 described herein. The TLC block 1514 may operate on the interstitial analyte concentration 1530 and the rate-of-change 1524 to generate the estimated concentration 1526 of the second analyte.

[0247] FIG. 16 is a flowchart showing one example of a process flow 1600 that may be performed to determine a sensor characteristic of an analyte sensor configured to sense a first analyte and a second analyte. In the example of FIG. 16, the analyte sensor is exposed to a series of known concentrations of the first analyte. The analyte sensor may be configured to measure the first analyte for at least some of the time that it is exposed to each of the series of known concentrations of the first analyte. While the analyte sensor is exposed to each of the series of known concentrations of the first analyte and configured to measure the first analyte, samples of the raw sensor signal may be taken.

[0248] For example, at operation 1602, the analyte sensor may be exposed to a first concentration of the first analyte for an exposure time period. In some examples, the analyte sensor may be immersed in a bath of a buffer material having the first concentration of the first analyte. An ex vivo analyte sensor may be used to set and / or verify that the buffer material exhibits the firstAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01concentration of the first analyte. In some examples, the analyte sensor may be exposed to a concentration of the second analyte concurrent with the first concentration of the first analyte. For example, the bath of buffer material having the first concentration of the first analyte may also have a concentration of the second analyte.

[0249] While the analyte sensor is exposed to the first concentration of the first analyte, the analyte sensor may be biased to generate a raw sensor signal indicative of the first analyte. For example, the analyte sensor, and / or an analyte sensor system including the analyte sensor, may cycle through different modes while exposed to the first concentration of the first analyte. This may include cycling the analyte sensor between a first mode in which the raw sensor signal is indicative of the first analyte and a second mode in which the raw sensor signal is indicative of the second analyte, for example, as described herein with respect to FIGS. 11 and 12. The raw sensor signal generated by the analyte sensor while it is exposed to the first concentration of the first analyte and operating in the first mode may be sampled and stored. In some examples, the analyte sensor may be configured to measure the first analyte throughout the exposure time period. For example, analyte sensor systems having multiple analyte sensors, such the analyte sensor system 1400 of FIG. 14 may maintain an analyte sensor for measuring the first analyte in a condition for measuring the first analyte throughout the exposure time period.

[0250] When the analyte sensor has been exposed to the first concentration of the first analyte for the exposure time period, the analyte sensor may be exposed to a second concentration of the first analyte for the exposure time period at operation 1604. In some examples, the analyte sensor is removed from a bath of buffer material having the first concentration of the first analyte and placed in a second bath of buffer material having the second concentration of the first analyte. Also, in some examples, the analyte sensor remains in the bath of buffer material having the first concentration of the first analyte while the concentration of the analyte in that buffer material is modified to the second concentration. While the analyte sensor is exposed to the second concentration of the first analyte, it may be biased to generate the raw sensor signal indicative of the concentration of the first analyte. The raw sensor signal generated while the analyte sensor is biased to generate a raw sensor signal indicative of theAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01concentration of the first analyte may be sampled, and the samples stored. In some examples, the analyte sensor may be cycled between modes for sensing the first analyte and the second analyte, as described with respect to operation 1602.

[0251] The analyte sensor may be exposed to a series of known concentrations of the first analyte in this manner. For example, the process flow 1600 illustrates exposing the analyte sensor to a series of known concentrations of the first analyte, including the first concentration and the second concentration. In some examples, a series of known concentrations of the first analyte may include more concentrations than the two show n in FIG. 16. Also, in some examples, the exposure time period for different concentrations in a series of known concentrations may be different. For example, the analyte sensor may be exposed to a first concentration for a first exposure time period and to a second concentration for a second exposure time period different than the first exposure time period, and so on.

[0252] The analyte sensor may be exposed to a series of known concentrations of the first analyte a single time, or multiple times. For example, operations 1602 and 1604 may be executed a single time or multiple times. In some examples, the analyte sensor is exposed to the series of known concentrations of the first analyte repeatedly during a test time period. The test time period may be between about one hour and about 20 days. In some examples, the test time period is about four hours. Also, in some examples, the test time period is about 15 days.

[0253] In some examples, the exposure of the analyte sensor to the second analyte may be held constant while the analyte sensor is exposed to the series of known concentrations of the first analyte. Consider an example in which the analyte sensor is exposed to the series of known concentrations of the first analyte by exposing the analyte sensor to baths of buffer solution having the respective concentrations of the first analyte. In this example, each bath may have a common concentration of the second analyte.

[0254] Also, in some examples, the exposure of the analyte sensor to the second analyte may vary, for example, between different exposure time periods and / or between different applications of the series of know n concentrations of the first analyte. For example, the analyte sensor may be exposed to one concentration of the second analyte while executing the operation 1602 and to aAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01different concentration of the second analyte while executing the operation 1604. Also, for example, the analyte sensor may be exposed to the series of known concentrations of the first analyte a first time at a first constant concentration of the second analyte. During a second exposure of the analyte sensor to the series of known concentrations of the first analyte, the second analyte may be held at a second constant concentration different than the first constant concentration.

[0255] At operation 1606, a sensor characteristic for the analyte sensor may be determined based on the response of the analyte sensor to the series of analyte concentrations. For example, the raw sensor signal generated by the analyte sensor in response to each concentration of the series of known concentrations may be compared to the known concentrations. The comparison may be used to generate a sensitivity and / or an offset. The sensitivity and / or offset may be used for the analyte sensor and / or for other analyte sensors to convert raw sensor signals to concentrations of the first analyte.

[0256] FIG. 17 is a plot 1700 showing an example series of analyte concentrations to which an analyte sensor may be exposed to generate an analyte sensor characteristic. In the example of FIG. 17, the analyte sensor is configurable to a mode for measuring a first analyte and / or a second mode for measuring a second analyte. In the example of FIG. 17, the first analyte measured by the analyte sensor is oxygen (02) and the second analyte measured by the analyte sensor is glucose. In plot 1700, a horizontal axis 1702 indicates time. A vertical axis 1704 indicates the oxygen concentration of a buffer material. A dashed vertical axis 1706 indicates the glucose concentration of the buffer material.

[0257] In the example of FIG. 17, the analyte sensor is exposed to an oxygen concentration of 1.8 ppm for an exposure time period 1710 of one hour. In some examples, the oxygen concentration during the time period 1710 may be between about 1 ppm and 3 ppm. Then, the analyte sensor is exposed to an oxygen concentration of 0.8 ppm for an exposure time period 1712 of one hour. In some examples, the oxygen concentration at the time period 1712 may be between about 0.4 ppm and 1.2 ppm. Subsequently, the analyte sensor is exposed to an oxygen concentration of 0.25 ppm for an exposure time period 1714 of one hour. In some examples, the oxygen concentration during the time period 1714 may be between about 0.05 ppm and 0.4 ppm. The analyte sensorAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01may then be exposed to an oxygen concentration of 0.1 ppm for an exposure time period 1716 of one hour. In some examples, the oxygen concentration at the time period 1716 may be between about 0 ppm and 0.2 ppm. In this example, the oxygen concentrations of 1.8 ppm, 0.8 ppm, 0.25 ppm, and 0.1 ppm are an example series of known concentrations to which the analyte sensor may be exposed. At the conclusion of the exposure time period 1716, the series of known concentrations may be repeated, with the analyte sensor being exposed to an oxygen concentration of 1.8 ppm at the exposure time period 1718, and so on for a test time period. In the example of FIG. 17, the glucose concentration 1708 to which the analyte sensor is exposed remains constant at 100 mg / dL.

[0258] In some examples, the analyte sensor may be switched between a first mode for measuring the first analyte and a second mode for measuring the second analyte (e.g., as described herein with respect to FIGS. 11 and 12) while being exposed to the series of known concentrations of oxygen.

[0259] FIG. 18 is a plot 1800 showing an example series of analyte concentrations to which an analyte sensor may be exposed to generate an analyte sensor characteristic. In the example of FIG. 18, the analyte sensor is configurable to measure a first analyte and / or a second analyte. In the example of FIG. 18, the first analyte measured by the analyte sensor is oxygen (02), and the second analyte measured by the analyte sensor is glucose. In plot 1800, a horizontal axis 1802 indicates time. A vertical axis 1804 indicates the oxygen concentration of a buffer material. A dashed vertical axis 1806 indicates glucose concentration of the buffer material.

[0260] In the example of FIG. 18, the analyte sensor is exposed to an oxygen concentration of 1.8 ppm for an exposure time period 1810 of one hour. Then the analyte sensor is exposed to an oxygen concentration of 0.8 ppm for an exposure time period 1812 of one hour. Subsequently, the analyte sensor is exposed to an oxygen concentration of 0.1 ppm for an exposure time period 1814 of one hour. The analyte sensor may then be exposed to an oxygen concentration of 1.0 ppm for an exposure time period 1816 of one hour.Subsequently, the analyte sensor may be exposed to an oxygen concentration of 1.2 ppm for an exposure time period 1818 of one hour.

[0261] In this example, the oxygen concentrations of 1.8 ppm. 0.8 ppm.0.1 ppm, 1.0 ppm, and 1.2 ppm are an example series of known concentrations toAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01which the analyte sensor may be exposed. At the conclusion of the exposure time period 1818. the series of known concentrations may be repeated. In the example of FIG. 18, the glucose concentration 1808 to which the analyte sensor is exposed remains constant at 100 mg / dL during the first exposure to the series of known concentrations. For the second exposure to the series of know n concentrations, the glucose concentration 1808 may rise to 250 mg / dL.

[0262] FIG. 19 is a flowchart showing one example of a process flow 1900 that may be performed utilizing a baseline set of analyte sensors to generate sensor-specific sensor characteristics for a subject analyte sensor. The subject analyte sensor may be a multi-analyte sensor operable in a first mode to measure a first analyte and in a second mode to measure a second analyte different than the first analyte. In some examples, the first analyte is oxygen, and the second analyte is glucose.

[0263] At operation 1902, a baseline test may be performed using the baseline set of analyte sensors. Performing the baseline test may include, for example, executing the process flow 1600 using the baseline set of analyte sensors. For example, the baseline set of analyte sensors may be exposed to a baseline series of know n concentrations of the first analyte. The baseline set of analyte sensors may be exposed to each known concentration of the series of known concentrations of the first analyte for a baseline exposure time period. In some examples, the same baseline exposure time period may be used for each of the baseline series of know n concentrations of the first analyte, or different baseline exposure time periods may be used for different known concentrations of the first analyte. The baseline set of analyte sensors may be repeatedly exposed to the baseline series of known concentrations of the first analyte for a baseline test time period. In some examples, the baseline set of analyte sensors may be exposed to a constant concentration of the second analyte and / or the concentration of the second analyte may be varied, for example, as described herein.

[0264] At operation 1904, a baseline sensor characteristic may be determined using responses of the baseline set of analyte sensors to the testing performed at operation 1902. For example, raw- sensor signals generated by the baseline set of analyte sensors in response to exposure to the known concentrations of the first analyte may be compared to the known concentrationsAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01to generate the characteristic. In some examples, the analyte sensor characteristic is or includes a sensitivity and / or an offset of the baseline set of analyte sensors to the first analyte.

[0265] FIG. 20 is a plot 2000 illustrating an example of generating a baseline sensor characteristic at a time during the baseline test time period. In the example of FIG. 20, the first analyte is oxygen. Oxygen concentration is indicated on the horizontal axis 2004, and the raw sensor signal is indicated on the vertical axis 2002. The raw sensor signal may be indicated by any suitable measure, such as, for example, counts, amps (including subdivisions thereof), and / or the like. In this example, the baseline series of known concentrations of the first analyte includes a concentration 2008. a concentration 2010, a concentration 2012, and a concentration 2014. Clusters of points around the concentrations 2008, 2010, 2012, 2014 indicate the raw sensor signal generated by the baseline set of analyte sensors when exposed to the baseline series of known concentrations of the first analyte. A linear fitting may be performed to generate the line 2006. A slope of the line 2006 may indicate a baseline sensitivity of the baseline set of analyte sensors to oxygen. An offset of the line 2006 may indicate a baseline offset of the baseline set of analyte sensors to oxygen.

[0266] FIG. 21 is a plot 2100 illustrating an example of generating a baseline sensor characteristic from characteristics of a baseline set of analyte sensors determined during a baseline test. For example, FIG. 21 illustrates example results of performing the operation 1902 and demonstrates one example way for performing the operation 1904. In the example of FIG. 21, the first analyte is oxygen, and the analyte sensor characteristic is an oxygen sensitivity, indicated on a vertical axis 2102. The horizontal axis 2104 indicates time. As illustrated, the oxygen sensitivity changes over time during the baseline test time period. The plot 2100 indicates a sensitivity curve 2106 resulting from a first analyte sensor of the baseline set of analyte sensors and a sensitivity curve 2108 resulting from a second analyte sensor of the baseline set of analyte sensors. The baseline characteristic is illustrated by the sensitivity curve 2110. The baseline characteristic may be determined based on an aggregation of the determined characteristics of the baseline set of analyte sensors. The aggregation may be based on, for example, an average, and / or the like. Although sensitivity curvesAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01for two of the baseline set of analyte sensors are shown in plot 2100. It will be appreciated that the baseline set of analyte sensors may include more than two sensors.

[0267] Referring back to the process flow 1900, an in vivo sensor session may be performed utilizing a subject analyte sensor at operation 1906. In some examples, the subject analyte sensor may be from the same lot as the baseline set of analyte sensors or otherwise manufactured in the same or similar manner and / or under the same or similar conditions. During the in vivo session, the raw sensor signal generated by the subject analyte sensor may be sampled and the samples stored. The in vivo session may last any suitable time period, such as, for example, between about one day and about 30 days. In some examples, the in vivo session may last about 15 days.

[0268] At operation 1908, an explant test may be performed using the subject analyte sensor. The explant test may be conducted after the in vivo session. Performing the explant test may include, for example, executing the process flow 1600 using the subject analyte sensor. For example, the subject analyte sensor may be exposed to an explant series of known concentrations of the first analyte. The subject analyte sensor may be exposed to each known concentration of the series of known concentrations of the first analyte for an explant exposure time period. In some examples, the same explant exposure time period may be used for each of the explant series of known concentrations of the first analyte, or different explant exposure time periods may be used for different known concentrations of the first analyte. The subject analyte sensor may be repeatedly exposed to the explant series of known concentrations of the first analyte for an explant test time period. The explant time period may be between about two hours and about 72 hours. In some examples, the explant time period is about 48 hours. The raw sensor signal generated by the subject analyte sensor during the explant test may be sampled and stored.

[0269] At operation 1910, a subject sensor characteristic may be determined by modifying the baseline characteristic using the explant data to generate a subject sensor characteristic. In some examples, this may include determining a modification to the baseline characteristic using samples of the raw sensor signal generated by the subject analyte sensor during the explant test.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0270] FIG. 22 is a plot 2200 illustrating an example of generating a subject sensor characteristic by modifying the baseline sensor characteristic. For example, the plot 2200 illustrates one example way of performing the operation 1910. In the example of FIG. 22, the first analyte is oxygen, and the analyte sensor characteristic is the sensitivity of the subject analyte sensor to oxygen, also referred to as an oxygen sensitivity. In FIG. 22, oxygen sensitivity is indicated on a vertical axis 2202. The horizontal axis 2204 indicates time. The baseline sensor characteristic is indicated by curve 2206. A subject sensor characteristic determined during the explant test is indicated by curve 2208. In some examples, the subject sensor characteristic may be determined by finding a ratio of the subject sensor characteristic (curve 2208) to the baseline characteristic (curve 2206).

[0271] The process flow 1900, at operation 1912, the in vivo analyte concentrations from the in vivo session of the subject analyte sensor may be determined using the subject sensor characteristic determined at 1910. For example, the subject sensor characteristic may be applied to the raw sensor signal samples generated during the in vivo session. In this way, analyte concentrations detected by the subject analyte sensor during the in vivo session may be measured.

[0272] FIG. 23 is a flowchart showing one example of a process flow 2300 that may be performed utilizing a baseline set of analyte sensors to generate sensor-specific sensor characteristics for a subject analyte sensor. At operation 2302, a baseline test may be performed using the baseline set of analyte sensors. Performing the baseline test may include, for example, executing the process flow 1600 using the baseline set of analyte sensors. For example, the baseline set of analyte sensors may be exposed to a baseline series of known concentrations of the analyte. The baseline set of analyte sensors may¬ be exposed to each known concentration of the series of known concentrations of the analyte for a baseline exposure time period. In some examples, the same baseline exposure time period may be used for each of the baseline series of know n concentrations of the analyte, or different baseline exposure time periods may be used for different known concentrations of the analyte. The baseline set of analyte sensors may be repeatedly exposed to the baseline series of known concentrations for a baseline test time period.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0273] At operation 2304, a baseline sensor characteristic may be determined using responses of the baseline set of analyte sensors to the testing performed at operation 2302. For example, raw sensor signals generated by the baseline set of analyte sensors in response to exposure to the known concentrations of the first analyte may be compared to the known concentrations to generate the characteristic. In some examples, the analyte sensor characteristic is or includes a sensitivity' and / or an offset. The baseline sensor characteristic may be determined, for example, as described herein with respect to FIGS. 19-21.

[0274] At operation 2306, a pre-session test may be conducted using a subject analyte sensor. The subject analyte sensor, in some examples, is from the same lot as the baseline set of analyte sensors and / or may be manufactured using similar processes and / or under similar conditions. The pre-session test may include executing the process flow 1600 using the subject analyte sensor. For example, the subject analyte sensor may be exposed to a subject series of known concentrations of the first analyte. The subject analyte sensor may be exposed to each known concentration of the series of known concentrations of the first analyte for a subject exposure time period. In some examples, the same subject exposure time period may be used for each of the subject series of known concentrations of the first analyte, or different subject exposure time periods may be used for different known concentrations of the first analyte. The subject analyte sensor may be repeatedly exposed to the subject series of known concentrations for a pre-session test time period. In some examples, the presession test time period may be less than a full sensor session. For example, the pre-session test time period may be between about one hour and about eight hours. In some examples, the pre-session test time period may be between about two hours and about four hours.

[0275] At operation 2308, a subject analyte sensor characteristic may be determined, for example, by modifying the baseline characteristic using the presession data generated during the pre-session test. In some examples, the subject analyte sensor characteristic may be determined in a manner similar to that described herein with respect to FIGS. 19 and 22.

[0276] At operation 2310, an in vivo session may be conducted using the subject analyte sensor. For example, the subject analyte sensor may be sterilizedAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01and then used for the in vivo session. In some examples, prior to the in vivo session, sensor electronics of an analyte sensor system, including the subject analyte sensor, may be programmed with sensor characteristic data describing the subject analyte sensor characteristic determined at operation 2308. The in vivo session may last any suitable time, such as, for example, between about five days and about 20 days. In some examples, the in vivo session lasts about 15 days. At operation 2312, and in vivo concentration of the analyte may be determined using the subject analyte sensor characteristic determined at operation 2308. In some examples, the in vivo concentration may be determined by the sensor electronics of an analyte sensor system, including the subject analyte sensor.

[0277] The process flow 2300 shows one example way that a multianalyte sensor may be calibrated for a first analyte. The multi-analyte sensor may also be calibrated for the second analyte.

[0278] An example calibration process will now be described with reference to FIG. 24. The calibration process described by FIG. 24 may be used to calibrate a multi-analyte sensor for the second analyte, such as glucose. It will be appreciated, however, that aspects of the calibration process described by FIG. 24 may also be used to calibrate a multi-analyte sensor for a first analyte, such as oxygen. For example, aspects of the calibration process described by FIG. 24 may be used in addition to or instead of other disclosure herein.

[0279] FIG. 24 illustrates an example calibration process 2400 that can use one or more of pre-implant information 2402, internal diagnostic information 2404, and external reference information 2406 as inputs to form or modify’ a transformation function 2408. Transformation function 2408 can be used to convert sensor data (e.g., in units of current or counts) into estimated analyte values 2410 (e.g., in units of analyte concentration). Information representative of the estimated analyte values can then be outputted 2412, such as displayed on a user display, transmitted to an external device (e.g., an insulin pump, PC computer, mobile computing device, etc.) and / or otherw ise processed further. The analyte can be glucose, for example.

[0280] In process 2400. pre-implant information 2402 can mean information that was generated prior to implantation of the sensor(s) presentlyAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01being calibrated. Pre-implant information 2402 can include any of the following types of information:• A priori information, including sensitivity values and ranges from in vitro or in vivo testing; predetermined sensitivity profile(s) associated with the currently used (e g., implanted) sensor, such as a predicted profile of sensitivity change over time of a sensor;• previously determined relationships between particular stimulus signal output (e.g., output indicative of an impedance, capacitance, or other electrical or chemical property of the analyte sensor) to sensor sensitivity (e.g., determined from prior in vivo and / or ex vivo studies), such as described in US Patent Publication 2012-0265035, which is incorporated herein by reference in its entirety;• previously determined relationships between particular stimulus signal output (e.g., output indicative of an impedance, capacitance, or other electrical or chemical property7of the sensor) to sensor temperature (e.g., determined from prior in vivo and / or ex vivo studies);• sensor data obtained from previously implanted analyte concentration sensors, such as sensors of the same lot of the sensor being calibrated and / or sensors from one or more different lots;• calibration code(s) associated with a sensor being calibrated;• patient-specific relationships between sensor and sensitivity, baseline, drift, impedance, impedance / temperature relationship (e.g., determined from prior studies of the patient or other patients having common characteristics with the patient);• site of sensor implantation (abdomen, arm, etc.), specific relationships (different sites may have different vascular density);Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01• time since sensor manufacture (e.g., time sensor on shelf, date when sensor was manufactured and or shipped, time between when the sensor was manufactured and / or shipped and when the sensor is implanted); and• exposure of the sensor to temperature, humidity, external factors, and on- shelf factors.

[0281] In process 2400, internal diagnostic information 2404 can mean information generated by the sensor system in which the implanted analyte sensor (the data of which is being calibrated) is being used. Internal diagnostic information 2404 can include any of the following types of information:• stimulus signal output (e.g., the output of which can be indicative of the sensor’s impedance) of a sensor using any of the stimulus signal techniques described herein (the stimulus signal output can be obtained and processed in real time);• sensor data measured by the implanted sensor indicative of an analyte concentration (real-time data and / or previously generated sensor data using the currently implanted sensor);• temperature measurements using the implanted sensor or an auxiliary sensor (such as a thermistor) co-located with the implanted analyte sensor or separately from the implanted analyte sensor;• sensor data from multi-electrode sensors; for example, where one electrode of the sensor is designed to determine a baseline signal;• sensor data generated by redundant sensors, where one or more of the redundant sensors is designed to be substantially the same as at least some (e.g., have the same sensor membrane type), if not all, of the other redundant sensors;sensor data generated by one or more auxiliary sensors, where the auxiliary sensor has a different modality, such as optical, thermal,Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01capacitive, etc., co-located with the analyte sensor or located apart from the analyte sensor;• time since sensor was implanted and / or connected (e.g., physically or electronically) to a sensor electronics of a sensor system;• data representative of a pressure on a sensor / sensor system generated by, for example, a pressure sensor (e.g., to detect compression artifact);• data generated by an accelerometer (e.g.. indicative of exercise / movement / activity of a host);• measure of moisture ingress (e.g., indicative of an integrity of a moisture seal of the sensor system); and• a measure of noise in an analyte concentration signal (which can be referred to as a residual between raw and filtered signals in some embodiments).

[0282] In process 2400, external reference information 2406 can mean information generated from sources while the implanted analyte sensor (the data of which is being calibrated) is being used. External reference information 2406 can include any of the following types of information:• real-time and / or prior analyte concentration information obtained from a reference monitor (e.g.. an analyte concentration value obtained from a separate sensor, such as a finger stick glucose meter);• type / brand of reference meter (different meters can have different bias / precision);information indicative of carbohydrates consumed by the patient;Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01information from a medicament pen / pump, such as insulin on board, insulin sensitivity, and glucagon on board;• glucagon sensitivity7information; and• information gathered from population-based data (e.g., based on data collected from sensors having similar characteristics, such as sensors from the same lot).

[0283] FIG. 25 is a flowchart showing one example of a process flow 2500 that may’ be executed to determine and utilize a temperature compensation for an analyte sensor. For example, the process flow 2500 may be utilized to determine a temperature compensation for a first analyte of a multi-analyte sensor, as described herein. In some examples, the process flow 2500 may be used to determine a temperature compensation for a multi-analyte sensor configured to measure oxygen and glucose.

[0284] At operation 2502, an analyte sensor or set of analyte sensors may be exposed to a series of known concentrations of a first analyte at a first temperature. In some examples, the analyte sensor may be immersed in a bath or baths of a buffer material having the series of concentrations of the first analyte, as described herein. An ex vivo analyte sensor may be used to set and / or verify that the buffer material exhibits the series of concentrations of the first analyte. In some examples, the analyte sensor may be exposed to one or more concentrations of the second analyte concurrent with the series of concentrations of the first analyte, for example, as described herein with respect to FIG. 18. For example, the bath or baths of buffer material having the series concentration of the first analyte may also have a concentration of the second analy te. The temperature of the bath of buffer material may be set and monitored in any suitable manner.

[0285] The analyte sensor may be repeatedly7exposed to the series of analyte concentrations at the first temperature for a first temperature time period. The first temperature time period may be, for example, between about 2 hours and about 72 hours. While the analyte sensor is exposed to the series of concentrations of the first analyte, the analyte sensor may be biased to generate a raw sensor signal indicative of the first analyte. For example, the analyte sensorAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01and / or an analyte sensor system including the analyte sensor may cycle through different modes while exposed to the series of concentrations of the first analyte. This may include cycling the analyte sensor between a first mode in which the raw sensor signal is indicative of the first analyte and a second mode in which the raw sensor signal is indicative of the second analyte, for example, as described herein with respect to FIGS. 11 and 12. The raw sensor signal generated by the analyte sensor while it is exposed to the first concentration of the first analyte and operating in the first mode may be sampled and stored. In some examples, the analyte sensor may be configured to measure the first analyte throughout the exposure time period. For example, analyte sensor systems having multiple analyte sensors, such as the analyte sensor system 1400 of FIG. 14, may maintain an analyte sensor for measuring the first analyte in a condition for measuring the first analyte throughout the exposure to the first temperature.

[0286] At operation 2504, the analyte sensor or set of analyte sensors may be exposed to the series of concentrations of the first analyte at a second temperature different than the first temperature. The exposure at operation 2504 may be similar to the exposure at operation 250 to accept at the second temperature. At operation 2506, the analyte sensor or set of analyte sensors may¬ be exposed to the series of concentrations of the first analyte at an Nth temperature. Although exposures at three temperatures are shown in FIG. 25, it will be appreciated that the analyte sensor or set of analyte sensors may be exposed to more or fewer than three temperatures.

[0287] At operation 2508, a sensor characteristic temperature dependency may be determined based on the stored samples of the raw sensor signal taken during the exposure of the analyte sensor to the series of concentrations of the first analyte at operations 2502, 2504, 2506. For example, the sensitivity of the analyte sensor or sensors to the first analyte, an offset of the response of the analyte sensor or sensors to the first analyte, and / or the like mayexhibit a dependency on temperature.

[0288] FIG. 26 is a plot 2600 showing an example relationship between an analyte sensor characteristic (oxygen sensitivity) and temperature. In the example of FIG. 26, a horizontal axis 2602 indicates temperature, and a vertical axis 2604 indicates oxygen sensitivity. In this example, oxygen sensitivity isAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01indicated as a ratio of the raw sensor signal generated by an analyte sensor configured to measure oxygen (02 current in pA) over known oxygen concentration (02 concentration in ppm). Points on the plot correspond to measurements taken, for example, during execution of the process flow 2500. In this example, a linear fitting produces line 2606. In this example, a temperature compensation characteristic may be described by a slope and an offset of line 2606.

[0289] FIG. 27 is a plot 2700 showing another example relationship between an analyte sensor characteristic (oxygen sensitivity) and temperature. In the example of FIG. 27, a horizontal axis 2702 indicates temperature, and a vertical axis 2704 indicates oxygen sensitivity. In this example, oxygen sensitivity’ is indicated as a ratio of the raw sensor signal generated by an analyte sensor configured to measure oxygen (02 current in pA) over known oxygen concentration (02 concentration in ppm). Points on the plot correspond to measurements taken, for example, during execution of the process flow 2500. In this example, a fitting is performed using the Arrhenius equation, resulting in curve 2706. The curv e 2706 may be described by the Arrhenius equation, given by Equation [5] below:-Eak = AekBT[5]In Equation [5], k is a rate constant, T is the temperature (in Kelvin), fo is the Boltzman constant, A is a pre-exponential factor, and Eais the activation energy. From the plotted curve 2706, values may be found for k, A, and Ea. These values may describe the temperature compensation characteristic of the analyte sensor.

[0290] FIG. 28 is a flowchart showing one example of a process flow 2800 that may be executed in an analyte sensor system to compensate for temperature. In some examples, the process flow 2800 may be executed in a multi-analyte sensor system to compensate for temperature with respect to a first analyte. The process flow 2800 shows one example way of implementing the temperature compensation block 1328 of FIG. 13 and the temperature compensation block 1528 of FIG. 15.

[0291] At operation 2802, sensor electronics may access a first raw sensor signal sample. The first raw sensor signal sample may have been taken byAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01an analyte sensor arranged to measure the first analyte. This may be, for example, from an analyte sensor that is dedicated to measuring the first analyte, such as, for example, analyte sensor 1404 of FIG. 14, and / or an analyte sensor that is configurable to measure multiple analytes such as, for example, the analyte sensor 1004 of FIG. 10. In some examples, the raw sensor signal sample may have been processed by a Kalman filter or other suitable filter or filters prior to access at operation 2802.

[0292] At operation 2804, the sensor electronics may access a temperature value. The temperature value may have been determined by a temperature sensor at the analyte sensor and / or by a temperature sensor are remote from the analyte sensor. For example, a temperature value from a temperature sensor remote from the analyte sensor may be processed by an LTI or other suitable processing prior to access at operation 2804.

[0293] At operation 2806, the sensor electronics may access a temperature compensation characteristic of the analyte sensor with respect to the first analyte. This may have been determined, for example, as described herein with respect to FIGS. 25-27. At operation 2808, the sensor electronics may determine an estimated analyte concentration of the first analyte using the raw sensor signal accessed at operation 2802. the temperature value accessed at operation 2804. and the temperature compensation characteristic accessed at operation 2806. For example, the sensor electronics may apply the temperature compensation characteristic to other sensor characteristics, such as, for example, the sensitivity of the sensor to the first analyte and / or an offset of the first sensor response to the first analyte. This may result in a temperature-compensated sensor characteristic. The sensor electronics may utilize the temperature-compensated sensor characteristic to determine the estimated concentration of the first analyte.

[0294] FIG. 29 shows two example plots, 2902, 2904, showing concentrations of a first analyte. In this example, the first analyte is oxygen. The plot 2902 is shown with a horizontal axis 2906 indicating temperature and a vertical axis 2910 indicating oxygen concentration. The plot 2904 is shown on the horizontal axis 2906, indicating temperature, and a different vertical axis 2908, indicating oxygen concentration. The plot 2902 includes data points gathered from analyte sensors and determined without using temperatureAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01compensation as described herein with respect to FIGS. 25-28. The plot 2904 includes data points gathered from analyte sensors and determined using temperature compensation as described herein with respect to FIGS. 25-28. A mean of the plot 2902 is given by line 2912. The mean of the plot 2904 is given by the line 2914. As shown, the line 2912 is not horizontal, indicating a dependence of the estimated oxygen concentrations on temperature. On the other hand, the line 2914 is closer to horizontal, indicating a lesser dependence of the estimated oxygen concentrations on temperature.

[0295] FIG. 30 is a flowchart showing one example of a process flow 3000 that may be performed to determine an error in an estimated analyte concentration generated by analyte sensors. In some examples, the process flow 3000 may be used in circumstances in which a suitable reference analyte sensor is not available. For example, the process flow 3000 may be used to determine the accuracy of a sensor characteristic determined as described herein.

[0296] At operation 3002, a baseline test may be performed using the baseline set of analyte sensors. Performing the baseline test may include, for example, executing the process flow 1600 using the baseline set of analyte sensors. For example, the baseline set of analyte sensors may be exposed to a baseline series of known concentrations of the first analyte. The baseline set of analyte sensors may be exposed to each known concentration of the series of known concentrations of the first analyte for a baseline exposure time period. In some examples, the same baseline exposure time period may be used for each of the baseline series of known concentrations of the first analyte, or different baseline exposure time periods may be used for different known concentrations of the first analyte. The baseline set of analyte sensors may be repeatedly exposed to the baseline series of known concentrations of the first analyte for a baseline test time period. In some examples, the baseline set of analyte sensors may be exposed to a constant concentration of the second analyte and / or the concentration of the second analyte may be varied, for example, as described herein.

[0297] At operation 3004, a baseline sensor characteristic may be determined using responses of the baseline set of analyte sensors to the testing performed at operation 3002. For example, raw sensor signals generated by the baseline set of analyte sensors in response to exposure to the knownAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01concentrations of the first analyte may be compared to the known concentrations to generate the characteristic. In some examples, the analyte sensor characteristic is or includes a sensitivity and / or an offset of the baseline set of analyte sensors to the first analyte.

[0298] At operation 3006, a trial test may be performed using a trial set of analyte sensors. Performing the trial test may include, for example, executing the process flow 1600 using the trial set of analyte sensors. For example, the trial set of analyte sensors may be exposed to atrial series o I' known concentrations of the first analyte. The trial series of know n concentrations of the first analyte may be the same as or different than the baseline series of known concentrations of the first analyte. The trial set of analyte sensors may be exposed to each known concentration of the series of known concentrations of the first analyte for a trial exposure time period. In some examples, the same trial exposure time period may be used for each of the trial series of known concentrations of the first analyte, or different trial exposure time periods may be used for different known concentrations of the first analyte. The trial set of analyte sensors may be repeatedly exposed to the trial series of known concentrations of the first analyte for a trial test time period. In some examples, the trial set of analyte sensors may be exposed to a constant concentration of the second analyte and / or the concentration of the second analyte may be varied, for example, as described herein.

[0299] At operation 3008, one or more estimated analyte concentrations for the first analyte may be generated using raw sensor signal samples from the trial test and the baseline sensor characteristic determined from the baseline test at operation 3004. At operation 3010, the one or more estimated analyte concentrations for the first analyte may be compared to the known concentrations of the first analyte from the series of known concentrations of the first analyte. The difference may indicate an accuracy or error of the trial set of analyte sensors and the baseline sensor characteristic.

[0300] Optionally, at operation 3012, the baseline sensor characteristic may be redetermined. In some examples, the baseline sensor characteristic may be redetermined if the error determined at operation 3010 is greater than a threshold. Redetermining the baseline sensor characteristic may comprise reconducting the baseline test at operation 3002 and / or redetermining the baselineAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01sensor characteristic at operation 3004. Redetermining the baseline sensor characteristic may be performed, for example, with a different set of baseline analyte sensors, a different number of baseline analyte sensors, a different baseline series of known concentrations of the first analyte, a different baseline exposure time period, a different baseline test time period, and / or the like.

[0301] FIG. 31 is a flowchart showing one example of a process flow 3100 for generating a first analyte rebased sensor signal. At operation 3102, sensor electronics may configure and operate the analyte sensor in a second analyte mode. In some examples, the second analyte mode causes the analyte sensor to generate a raw7sensor signal indicative of a second analyte, such as glucose. While the analyte sensor is operating in the second analyte mode, the sensor electronics may periodically sample the raw sensor signal, which may be indicative of the second analyte. The sensor electronics may, as described herein, generate estimated concentrations of the second analyte and provide those estimated concentrations for display or storage, for example, as described herein.

[0302] At operation 3104, the sensor electronics may store a reference sample of the raw- sensor signal sampled while the analyte sensor system is executing in the second analyte mode. After storing the reference sample of the raw sensor signal, the sensor electronics may, at operation 3106, configure the analyte sensor to operate in a first analyte mode. In the first analyte mode, the analyte sensor may be configured to generate a raw sensor signal indicative of the first analyte, such as oxygen. At operation 3108, the sensor electronics may sample the raw sensor signal while the analyte sensor is operated in the first analyte mode. One or more samples may be taken.

[0303] At operation 3110, a rebased raw sensor signal for the first analyte mode may be generated. This may include, for example, subtracting the magnitude of the reference sample from each raw7sensor signal sample taken at operation 3108. The result may be a rebased raw sensor signal and / or a set of rebased raw sensor signal samples. The operation 3110, in some examples, is performed by sensor electronics during a sensor session using the analyte sensor. In other examples, the operation 3110 may be performed by another computing device, for example, using data generated during a bench test or in vivo session.

[0304] In various examples, determining a rebased sensor signal (or rebased sensor signal samples as described with respect to FIG. 31) for the firstAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01analyte mode may result in increased accuracy and better correlation between the rebased sensor signal and the first analyte concentration. Consider an example in which the first analyte is oxygen and the second analyte is glucose. Recall that an analyte sensor for measuring glucose and oxygen may be configured to measure glucose by providing a positive bias condition between the working electrode and the reference electrode and configured to measure oxygen by providing a negative bias condition between the working electrode and the reference electrode. In some examples, when the analyte sensor is transitioned from the glucose mode to the oxygen mode, current artifacts related to the glucose mode may remain during the oxygen mode. The last raw sensor signal sample taken before the switch from glucose mode to oxygen mode may be an accurate estimate of the current artifacts from the glucose mode that remain in the oxygen mode. Accordingly, subtracting the last raw sensor signal sample in the glucose mode from raw sensor signal samples in the oxygen mode may increase the accuracy of the analyte sensor while operating in the oxygen mode.

[0305] FIG. 32 is a plot 3200 showing one example correlation between a raw sensor signal generated by a multi-analyte sensor configured in an oxygen mode and known concentrations of oxygen. A horizontal axis 3204 indicates oxygen. A vertical axis 3202 indicates a raw sensor signal, in nA. The points on the plot 3200 illustrate points gathered using an analyte sensor subject to known concentrations of oxygen, for example, as described herein. Line 3206 shows a linear fitting of the points.

[0306] FIG. 33 is a plot 3300 showing one example correlation between a raw sensor signal generated by a multi-analyte sensor configured in an oxygen mode and know n concentrations of oxygen. A horizontal axis 3304 indicates oxygen. A vertical axis 3302 indicates a rebased sensor signal, in nA. A rebased sensor signal may be determined, for example, as described herein with respect to FIG. 31. The points on the plot 3300 illustrate points gathered using an analyte sensor subject to known concentrations of oxygen, for example, as described herein. Line 3306 shows a linear fitting of the points. As shown, the line 3306 using rebased sensor signal samples demonstrates a superior fit to the line 3206 using the raw sensor signal samples. It will be appreciated that a rebased sensor signal sample or rebased sensor signal samples may be utilized in place of a raw' sensor signal in any of the implementations described herein.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0307] In some examples, a multi-analyte sensor system may be used to detect and / or correct fault conditions. Consider an example multi-analyte sensor system configured to measure oxygen and glucose. Such an analyte sensor system may be arranged with a single analyte sensor, similar to analyte sensor system 1000, or with multiple analyte sensors, similar to analyte sensor system 1400. In some examples, estimated concentrations of oxygen generated by the analyte sensor system may be used to detect false in the measurement of glucose.

[0308] Consider an example fault condition in a glucose sensor referred to as a compression low. During a compression low, the host places pressure on the insertion location for an analyte sensor, for example, by lying on the insertion location, leaning on the insertion location, and / or the like. Pressure or compression on the insertion location may tend to evacuate the insertion location of interstitial fluid. This may artificially reduce the concentration of glucose at the insertion location, thus breaking the correlation between glucose concentration in the interstitial fluid at the insertion location and blood glucose concentration. This may reduce the raw sensor signal generated by the analyte sensor in the glucose mode may be reduced, resulting in an erroneous reduction in estimated glucose concentrations.

[0309] Consider another example fault condition in a glucose sensor referred to as PSD or an end-of-life state. As the analyte sensor is used over a session, the host’s body may tend to encapsulate the analyte sensor to isolate it from the host’s tissue. This bioencapsulation may reduce the amount of glucose in contact with the analyte sensor, also resulting in a reduction in the raw sensor signal in the glucose mode and in erroneous reduction in estimated glucose concentrations generated by the analyte sensor system.

[0310] In some examples, a reduction in oxygen concentration may be an indicator of a fault condition, such as, for example, a compression low or an end-of-life state. For example, as pressure on the insertion location drives interstitial fluid away from the insertion location, it may also reduce the oxygen available to the analyte sensor. Accordingly, a compression low may be accompanied by a reduction in estimated oxygen concentration. Also, bioencapsulation may also tend to reduce the access of the sensor to interstitial oxygen. Accordingly, an end-of-life state may also be accompanied by a reduction in estimated oxygen concentration.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0311] FIG. 34 is a plot 3400 showing example correlations between estimated oxygen concentration and an analyte sensor fault condition. The plot 3400 includes a plot of a sensor signal 3402 generated by an analyte sensor in glucose mode, an estimated glucose value (EGV) 3404 generated from the sensor signal, and an estimated oxygen value (EOV) 3406. The dashed circle 3408 illustrates a change in the EGV 3404 corresponding to an end-of-life state. The dashed circle 3410 illustrates that the end-of-life state was accompanied by a corresponding reduction in EOV 3406.

[0312] FIG. 35 is a plot 3500 showing additional example correlations between estimated oxygen concentration and analyte sensor fault conditions. The plot 3500 includes a plot of an EGV 3502 generated by an analyte sensor system and a plot of an EOV 3504 generated by that analyte sensor system. A low glucose condition is illustrated at 3506. As shown, the low glucose condition corresponds to a low oxygen condition 3508 occurring at the same time. In this example, the coincidence of the low glucose condition 3506 and the low oxygen condition 3508 may indicate a fault condition at the sensor, such as, for example, a compression low and / or an end-of-life state. FIG. 35 also shows a low' glucose condition 3510 that coincides with a low oxygen condition 3512. This, too, may indicate that the low glucose condition 3510 is due to a sensor fault such as. for example, a compression low and / or an end-of-life state.

[0313] FIG. 36 is a flowchart showing one example of a process flow 3600 that may be executed, for example, by sensor electronics of a multi-analyte sensor system, to detect a sensor fault using estimated oxygen concentrations. The process flow 3600 may be executed using an analyte sensor system having a single analyte sensor that is configurable to measure glucose and oxygen, for example, similar to the analyte sensor system 1000 of FIG. 10. The process flow 3600 may also describe the operation of a multi-analyte sensor system comprising multiple analyte sensors, such as the analyte sensor system 1400 of FIG. 14.

[0314] At operation 3602, the sensor electronics may configure an analyte sensor system to a mode for measuring glucose. At operation 3604, the sensor electronics may use the analyte sensor to measure a glucose concentration. For example, the sensor electronics may sample a raw sensor signal generated while the analyte sensor is in the glucose mode. The sensorAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01electronics may process the raw sensor signal sample, for example, as described herein, to generate an estimated glucose value. At operation 3606. the sensor electronics may determine if a glucose period for the analyte sensor system has ended. If the glucose period has not ended, the sensor electronics may continue using the analyte sensor to measure the glucose concentration at operation 3604.

[0315] If the glucose period has ended, then the sensor electronics may configure the analyte sensor to an oxygen mode at operation 3608. While the analyte sensor is configured to the oxygen mode, the sensor electronics may use the analyte sensor to measure an oxygen concentration at operation 3610. In some examples, the sensor electronics is configured to use the analyte sensor to measure the oxygen concentration for a predetermined number of samples over a predetermined time period.

[0316] At operation 3612, the sensor electronics may determine if one or more of the oxygen concentrations from operation 3610 indicate a fault condition at the analyte sensor. In some examples, oxygen concentrations may indicate a fault condition if the oxygen concentrations, or a mean or other aggregation thereof, is lower than a threshold concentration. In some examples, the threshold concentration may be between about 0.1 ppm and about 0.5 ppm. In some examples, the threshold concentration may be between about 0.25 ppm and 0.3 ppm. In some examples, the sensor electronics may determine that one or more of the oxygen concentrations indicate a fault if the oxygen concentrations are declining. For example, the sensor electronics may determine that one or more of the oxygen concentrations indicate a fault of the analyte sensor if one oxygen concentration is less than a previously measured oxygen concentration by more than a threshold amount. The threshold amount may be between about 10% and about 50% of the previously measured oxygen concentration.

[0317] If the oxygen concentration or concentrations do not indicate a sensor fault, then the sensor electronics may determine, at operation 3614, whether the oxygen period of the analyte sensor system has completed. If the oxygen period has not completed, then the sensor electronics may return to operation 3610 and continue using the analyte sensor to measure oxygen. If the oxygen period has completed, then the sensor electronics may return to operation 3602 and configure the analyte sensor to the glucose mode.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0318] If the oxygen concentration does indicate a sensor fault at operation 3612. then the sensor electronics may perform a responsive action at operation 3616. Example responsive actions may include ending a current sensor session and / or suspending display of estimated glucose values to the host or other user. In some examples, the display of the estimated glucose value may be suspended for a predetermined time and / or until subsequent measures of oxygen concentration fail to indicate a fault.

[0319] FIG. 37 is a flowchart showing one example of a process flow 3700 that may be executed, for example, by sensor electronics of a multi-analyte sensor system, to detect a sensor fault using estimated oxygen concentrations. The process flow 3700 may be executed using an analyte sensor system that concurrently measures oxygen concentration and glucose concentration. For example, the analyte sensor system 1400 of FIG. 14, having two analyte sensors 1404, 1405, may be operated in this manner.

[0320] At operation 3702, the sensor electronics may use a glucose sensor to measure a glucose concentration. The glucose sensor may be an analyte sensor configured to measure glucose. For example, the sensor electronics may sample a raw sensor signal generated by the glucose sensor. The sensor electronics may process the raw sensor signal sample, for example, as described herein, to generate an estimated glucose value.

[0321] Concurrent with operation 3702, the sensor electronics may, at operation 3704, use an oxygen sensor to measure an oxygen concentration. The oxygen sensor may be an analyte sensor configured to measure oxygen. At operation 3706. the sensor electronics may determine if the oxygen concentration determined at operation 3704 indicates a sensor fault. If no fault is indicated, then the sensor electronics may continue to measure glucose at operation 3702 and oxygen at operation 3704. If a fault is detected at operation 3706, then a responsive action may be undertaken at operation 3708.

[0322] In some examples, the sensor electronics of an analyte sensor system or other device is programmed to detect an end-of-life state of an analyte sensor using various factors and techniques. An oxygen concentration measured by the analyte sensor system may be considered along with various other factors to detect the end-of-life state of the analyte sensor.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0323] FIG. 38 illustrates a simplified block diagram showing an example process flow 3800 that may be executed by a sensor electronics of an analyte sensor system to detect an end-of-life state of the analyte sensor. In some examples, the arrangement of FIG. 38 may allow real-time detection of PSD without sacrificing the lifetime of healthy sensors.

[0324] The process flow 3800 may include a feature generation module 3802, a classifier model 3804, and an end-of-life state generation module 3806. The feature generation module 3802 may receive raw input associated with one or more risk factor metrics, as described herein, and may generate expressions of the one or more and of life risk factor metrics as features in a feature space. The features generated by the feature module 3802, collectively referred to as a feature set, may be provided to the classifier model 3804. The classifier model 3804 may be a trained machine learning model. The classifier model 3804 may receive the feature set and generate an output that represents an end-of-life likelihood, or a likelihood that the continuous analyte sensor is in an end-of-life state. An end-of-life state generation module 3806 may receive the end-of-life likelihood and determine whether the continuous analyte sensor is in the end-of-life state.

[0325] In some examples, the feature generation module 3802 may¬ receive some or all of a raw data stream generated by the continuous analyte sensor. In some examples, the feature generation module 3802 may also receive noise state data describing a noise state of the continuous analyte sensor. In some examples, the feature generation module 3802 may derive noise state data for the continuous analyte sensor from the raw data stream. The feature generation module 3802 may also receive other inputs indicative of other risk factor metrics. In some examples, the feature generation module 3802 may receive sensor signals generated by an analyte sensor system indicating glucose concentrations and sensor signals indicating corresponding oxygen concentrations. The feature generation module 3802 processes its various inputs to produce features making up a feature set that is provided to the classifier model 3804. The features of the feature set may correlate to progressive sensor decline (PSD) or otherwise indicate whether the continuous analyte sensor is in an end-of-life state.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0326] The processor circuit may apply the classifier model 3804 to the generated feature set to determine an end-of-life likelihood. The end-of-life likelihood may indicate a probability that the continuous analyte sensor is in an end-of-life state based on the feature set of based on the risk factor metrics. In some examples, the end-of-life likelihood is a number between zero and one. The end-of-life state generation module 3806 may be executed to produce an end-of-life state or flag using the current end-of-life likelihood.

[0327] The feature generation module 3802, in some examples, may generate features from end-of-life risk factor metrics from four example categories: (i) Noise Features, (ii) Downward Drift Features, (iii) secondary features, and (iv) oxygen concentration. Noise Features may be designed to capture characteristics of noise that is often present when there is a non-recoverable PSD event, indicating an end-of-life state for the continuous glucose sensor. Downward Drift features are correlated with accumulated sensor model deviations (i.e. sensitivity / drift decline). Secondary’ features may be determined based on the noise features, downward drift features and their interactions, as described herein. Oxygen concentration features may include one or more estimated oxygen concentrations or values generated by the analyte sensor system. The generated feature set may capture different aspects of PSD as it is manifested on the raw glucose signal from clinical data.

[0328] Other example features that may be considered include: accelerometer artifacts detected by an accelerometer of the analyte sensor system (e g. indicating drops of the analyte sensor), temperature exposure of the analyte sensor measured by a temperature sensor of the analyte sensor system, elapsed time since a sensor session has begun, elapsed time since the sensor was manufactured, whether an expiration date for the sensor has passed, a number of samples of the raw sensor signal taken since the beginning of a sensor session, and / or the like.

[0329] In some examples, the classifier model 3804 is a logistic regression model that linearly combines all the features with pre-determined coefficients and applies anon-linear function (e.g., sigmoid) to transform it to an end-of-life likelihood between zero and one. It will be appreciated that other types of classifier models may also be used, such as. for example, a gradient boosting decision tree model, a random forest model, and / or the like. Prior toAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01use, the classifier model 3804 may be trained using suitable training data.Training data, in some examples, may be based on the behavior of the continuous analyte sensor associated with processor circuit and / or based on the behavior of other continuous analyte sensors. The classifier model 3804, in some examples, is trained by the processor circuit associated with the continuous analyte sensor. In other examples, the classifier model 3804 is pre-trained and provided to the processor circuit associated with the continuous analyte sensor already trained.

[0330] The end-of-life state generation module 3806 may apply a threshold to the end-of-life likelihood value generated by the classifier model 3804 and may derive an end-of-life status for the continuous analyte sensor (e.g., an indication of whether the continuous analyte sensor is in an end-of-life state). If the end-of-life likelihood is greater than or equal to a threshold (i.e., 0.5 in probability7space), the end-of-life status may be “1”, indicating that the continuous analyte sensor is in an end-of-life state. If computed end-of-life likelihood is less than the threshold, the end-of-life status may be “0” , indicating that the continuous analyte sensor is not in an end-of-life state.

[0331] In some examples, if the feature generation module 3802 and / or classifier model 3804 has not generated an end-of-life likelihood value, the end-of-life state generation module 3806 may set the end-of-life state for the continuous analyte sensor to“-l”, indicating “not computed / ’ The feature generation module 3802 and / or classifier model 3804 may not have generated an end-of-life likelihood value, for example, if one or more of those components is initializing. In some examples, the end-of-life state generation module 3806 sets an end-of-life flag if the end-of-life likelihood indicates that the continuous analyte sensor is in an end-of-life state. The end-of-life flag, when said, may trigger various responsive actions by the processor circuit, for example, as described herein.

[0332] FIG. 39 is a block diagram illustrating a computing device hardw are architecture 3900, within w hich a set or sequence of instructions can be executed to cause a machine to perform examples of any one of the methodologies discussed herein. The hardware architecture 3900 can describe various computing devices, including, for example, the sensor electronics 106, the peripheral medical device 122, the smart device 112, the tablet 114, etc.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01

[0333] The architecture 3900 may operate as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the architecture 3900 may operate in the capacity of either a server or a client machine in server-client network environments, or it may act as a peer machine in peer-to-peer (or distributed) network environments. The architecture 3900 can be implemented in a personal computer (PC), a tablet PC, a hybrid tablet, a set-top box (STB), a personal digital assistant (PDA), a mobile telephone, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing instructions (sequential or otherwise) that specify operations to be taken by that machine.

[0334] The example architecture 3900 includes a processor unit 3902 comprising at least one processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both, processor cores, compute nodes). The architecture 3900 may further comprise a main memory 3904 and a static memory 3906, which communicate with each other via a link 3908 (e.g., bus). The architecture 3900 can further include a video display unit 3910, an input device 3912 (e g., a keyboard), and a UI navigation device 3914 (e.g., a mouse). In some examples, the video display unit 3910, input device 3912, and UI navigation device 3914 are incorporated into a touchscreen display. The architecture 3900 may additionally include a storage device 3916 (e.g., a drive unit), a signal generation device 3918 (e.g., a speaker), a network interface device 3920, and one or more sensors (not show n), such as a Global Positioning System (GPS) sensor, a compass, an accelerometer, or another sensor.

[0335] In some examples, the processor unit 3902 or another suitable hardware component may support a hardware interrupt. In response to a hardware interrupt, the processor unit 3902 may pause its processing and execute an ISR, for example, as described herein.

[0336] The storage device 3916 includes a machine-readable medium 3922 on which is stored one or more sets of data structures and instructions 3924 (e.g., software) embodying or used by any one or more of the methodologies or functions described herein. The instructions 3924 can also reside, completely or at least partially, within the main memory 3904, within the static memory 3906. and / or within the processor unit 3902 during execution thereof by theAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01architecture 3900, with the main memory 3904, the static memory 3906, and the processor unit 3902 also constituting machine-readable media.EXECUTABLE INSTRUCTIONS AND MACHINE-STORAGE MEDIUM

[0337] The various memories (i.e., 3904. 3906, and / or memory of the processor unit(s) 3902) and / or storage device 3916 may store one or more sets of instructions and data structures (e.g., instructions) 3924 embodying or used by any one or more of the methodologies or functions described herein. These instructions, when executed by processor unit(s) 3902 cause various operations to implement the disclosed examples.

[0338] As used herein, the terms '‘machine-storage medium,” “devicestorage medium,” “computer-storage medium” (referred to collectively as “machine-storage medium 3922”) mean the same thing and may be used interchangeably in this disclosure. The terms refer to a single or multiple storage devices and / or media (e.g., a centralized or distributed database, and / or associated caches and servers) that store executable instructions and / or data, as well as cloud-based storage systems or storage networks that include multiple storage apparatus or devices. The terms shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media, and / or device-storage media 3922 include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), FPGA, and flash memory' devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms machine-storage media, computer-storage media, and device-storage media 3922 specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium” discussed below.SIGNAL MEDIUM

[0339] The term “signal medium” or “transmission medium” shall be taken to include any form of modulated data signal, carrier wave, and so forth.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01The term ‘‘modulated data signal'’ means a signal that has one or more of its characteristics set or changed in such a matter as to encode information in the signal.COMPUTER-READABLE MEDIUM

[0340] The terms “machine-readable medium,” “computer-readable medium” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure. The terms are defined to include both machine-storage media and signal media. Thus, the terms include both storage devices / media and carrier waves / modulated data signals.

[0341] The instructions 3924 can further be transmitted or received over a communications network 3926 using a transmission medium via the network interface device 3920 using any one of a number of well-known transfer protocols (e.g., HTTP). Examples of communication networks include a LAN, a WAN, the Internet, mobile telephone networks, plain old telephone service (POTS) networks, and wireless data networks (e.g., Wi-Fi, 3G, 4G LTE / LTE-A, 5G or WiMAX networks). The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carry ing instructions for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such software.

[0342] Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.

[0343] Various components are described in the present disclosure as being configured in a particular way. A component may be configured in anyAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01suitable mariner. For example, a component that is or that includes a computing device may be configured with suitable software instructions that program the computing device. A component may also be configured by virtue of its hardware arrangement or in any other suitable manner.

[0344] The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) can be used in combination with others. Other examples can be used, such as by one of ordinary skill in the art upon reviewing the above description. The Abstract is to allow the reader to quickly ascertain the nature of the technical disclosure, for example, to comply with 37 C.F.R. § 1.72(b) in the United States of America. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

[0345] Also, in the above Detailed Description, various features can be grouped together to streamline the disclosure. However, the claims cannot set forth every feature disclosed herein, as examples can feature a subset of said features. Further, examples can include fewer features than those disclosed in a particular example. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate example. The scope of the examples disclosed herein is to be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

[0346] Each of these non-limiting examples in any portion of the above description may stand on its own or may be combined in various permutations or combinations with one or more of the other examples.

[0347] The above detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific embodiments in which the subject matter can be practiced. These embodiments are also referred to herein as ‘"examples.’7Such examples can include elements in addition to those shown or described. However, the present inventors also contemplate examples in which only those elements shown or described are provided. Moreover, the present inventors also contemplate examples using any combination or permutation of those elements shown or described (or one or more aspects thereof), either withAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01respect to a particular example (or one or more aspects thereof), or with respect to other examples (or one or more aspects thereof) shown or described herein.

[0348] In the event of inconsistent usages between this document and any documents so incorporated by reference, the usage in this document controls.

[0349] In this document, the terms “a” or ‘“an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one’’ or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B.” unless otherwise indicated. In this document, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended, that is, a system, device, article, composition, formulation, or process that includes elements in addition to those listed after such a term in a claim are still deemed to fall within the scope of that claim. Moreover, in the following claims, the terms “first,” “second,” “third,” etc., are used merely as labels, and are not intended to impose numerical requirements on their objects.

[0350] Geometric terms, such as “parallel”, “perpendicular”, “round”, or “square” are not intended to require absolute mathematical precision, unless the context indicates otherwise. Instead, such geometric terms allow for variations due to manufacturing or equivalent functions. For example, if an element is described as “round” or “generally round”, a component that is not precisely circular (e.g., one that is slightly oblong or is a many-sided polygon) is still encompassed by this description.

[0351] Method examples described herein can be machine or computer-implemented at least in part. Some examples can include a computer-readable medium or machine-readable medium encoded with instructions operable to configure an electronic device to perform methods as descnbed in the above examples. An implementation of such methods can include code, such as microcode, assembly language code, a higher-level language code, or the like. Such code can include computer readable instructions for performing various methods. The code may form portions of computer program products. Further, in an example, the code can be tangibly stored on one or more volatile, non-Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01transitory, or non-volatile tangible computer-readable media, such as during execution or at other times. Examples of these tangible computer-readable media can include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, random access memories (RAMs), read only memories (ROMs), and the like.

[0352] The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other embodiments can be used, such as by one of ordinary skill in the art upon reviewing the above description. The Abstract is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be interpreted as intending that an unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that such embodiments can be combined with each other in various combinations or permutations. The scope of the subject matter should be determined with reference to the claims, along with the full scope of equivalents to which such claims are entitled.

Claims

Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01CLAIMS1. A method for operating a subject analyte sensor configured to sense a first analyte when operated in a first mode and to sense a second analyte when operated in a second mode, the method comprising:while operating the subject analyte sensor in the first mode, exposing the subject analyte sensor to a first concentration of the first analyte;while operating the subject analyte sensor in the first mode, exposing the subject analyte sensor to a second concentration of the first analyte, the second concentration being different than the first concentration;accessing baseline sensitivity data describing a sensitivity of a baseline set of analyte sensors to the first analyte, the baseline set of analyte sensors not including the subject analyte sensor;generating subject analyte sensor sensitivity' data describing a sensitivity of the subject analyte sensor to the first analyte, the generating of the subject analyte sensor sensitivity data being based at least in part on a response of the subject analyte sensor to the first analyte at the first concentration and at least in part on a response of the subject analyte sensor to the first analyte at the second concentration;accessing first mode in vivo sensor data, wherein the first mode in vivo sensor data is generated by the subject analyte sensor while operating in the first mode and the subject analyte sensor is inserted into a host; andgenerating a first analyte concentration value using the first mode in vivo sensor data and the subject analyte sensor sensitivity data.

2. The method of claim 1, further comprising:accessing second mode in vivo sensor data generated by the subject analyte sensor while operating in the second mode and inserted into the host; and generating a second analyte concentration value using the second mode in vivo sensor data.

3. The method of any of claims 1-2, further comprising:while operating the subject analyte sensor in the first mode, exposing the subject analyte sensor to a third concentration of the first analyte; andAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01while operating the subject analyte sensor in the first mode, exposing the subject analyte sensor to a fourth concentration of the first analyte, the generating of the subject analyte sensor sensitivity data also being based at least in part on a response of the subject analyte sensor to the first analyte at the third concentration and at least in part on a response of the subject analyte sensor to the first analyte at the fourth concentration.

4. The method of claim 3, the first analyte being oxygen, the first concentration being between about 1 ppm and 3 ppm, the second concentration being between about 0.4 ppm and 1.2 ppm, the third concentration being between about 0.05 ppm and 0.4 ppm, and the fourth concentration being between about 0 ppm and 0.2 ppm.

5. The method of any of claims 3-4, wherein:the exposing of the subject analyte sensor to the first concentration of the first analyte is before the exposing of the subject analyte sensor to the second concentration of the first analyte;the exposing of the subject analyte sensor to the second concentration of the first analyte is before the exposing of the subject analyte sensor to the third concentration of the first analyte; andthe exposing of the subject analyte sensor to the third concentration of the first analyte is before the exposing of the subject analyte sensor to the fourth concentration of the first analyte.

6. The method of any of claims 1-5, further comprising:repeatedly exposing the baseline set of analyte sensors to a first sequence of concentrations of the first analyte for a baseline time period, the baseline sensitivity data being based at least in part on a response of the baseline set of analyte sensors to the first sequence of concentrations of the first analyte; and exposing the subject analyte sensor to a second sequence of concentrations of the first analyte for a pre-session time period, the sequence of concentrations of the first analyte comprising the first concentration of the first analyte and the second concentration of the first analyte.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT017. The method of claim 6, the exposing of the subject analyte sensor to the second sequence of concentrations of the first analyte comprising repeatedly exposing the subject analyte sensor to the second sequence of concentrations of the first analyte.

8. The method of any of claims 6-7, the pre-session time period being shorter than the baseline time period.

9. The method of any of claims 6-8, the pre-session time period being between about one hour and about eight hours.

10. The method of any of claims 6-9, the pre-session time period being between about two hours and about four hours.

11. The method of any of claims 6- 10, the baseline time period being between about five days and about twenty days.

12. The method of any of claims 6-11, the baseline time period being between about fifteen days.

13. The method of any of claims 1-12, further comprising:while exposing the subject analyte sensor to the first concentration of the first analyte, exposing the analyte sensor to a first concentration of the second analyte; andwhile exposing the subject analyte sensor to the second concentration of the first analyte, exposing the analyte sensor to the first concentration of the second analyte.

14. The method of any of claims 1-13, further comprising storing the subject analyte sensor sensitivity data to subject sensor electronics associated with the subject analyte sensor, the generating of the first analyte concentration value being performed by the subject sensor electronics.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT0115. The method of claim 14, further comprising:receiving, by the subject sensor electronics, an indication that the subject analyte sensor has been inserted into a host: andoperating, by the subject sensor electronics, the subject analyte sensor in the first mode to generate the first mode in vivo sensor data.

16. The method of any of claims 1-15, further comprising: generating a modified baseline characteristic data based at least in part on the first mode in vivo sensor data.

17. A system comprising: at least one processor and at least one memory storing instructions, which when executed by the at least one processor perform operations comprising the method of any of claims 1 to 16.

18. An analyte sensor system configurable to a first mode for generating a sensor signal indicative of a first analyte and to a second mode for generating a sensor signal indicative of a second analyte, the analyte sensor system comprising:a first analyte sensor;a temperature sensor; andsensor electronics configured to perform operations comprising:accessing a first sensor signal sample of a first sensor signal indicative of the first analyte;accessing a temperature value generated by the temperature sensor;accessing first analyte temperature compensation data describing a relationship between temperature and a characteristic of the first analyte sensor; andgenerating an estimated first analyte concentration using the first sensor signal sample, the temperature value, and the first analyte temperature compensation data.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT0119. The analyte sensor system of claim 18, the sensor electronics further configured to perform operations comprising:accessing a second sensor signal sample indicative of the second analyte: andgenerating an estimated second analyte concentration using the second sensor signal sample.

20. The analyte sensor system of claim 19, the sensor electronics further configured to perform operations comprising:biasing the first analyte sensor to a first bias condition;while the first analyte sensor is biased to the first bias condition, generating the first sensor signal sample from the first sensor signal;biasing the first analyte sensor to a second bias condition different than the first bias condition; andwhile the first analyte sensor is biased to the second bias condition, generating the second sensor signal sample from the second sensor signal sample.

21. The analyte sensor system of any of claims 19-20, further comprising a second analyte sensor, the first sensor signal being generated by the first analyte sensor and the second sensor signal sample being sampled from a second sensor signal generated by the second analyte sensor.

22. The analyte sensor system of any of claims 18-21, the first analyte temperature compensation data describing a relationship between temperature and a first analyte sensitivity of the first analyte sensor.

23. The analyte sensor system of claim 22, the relationship being a linear relationship over a first temperature range.

24. The analyte sensor system of any of claims 18-23, the relationship being an Arrhenius relationship.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT0125. A method for using an analyte sensor configured to generate a first sensor signal describing a concentration of a first analyte and a second sensor signal describing a concentration of a second analyte, the method comprising:accessing a first sensor signal sample of a first sensor signal indicative of the first analyte generated by a first analyte sensor;accessing a temperature value generated by a temperature sensor; accessing first analyte temperature compensation data describing a relationship between temperature and a characteristic of the first analyte sensor; andgenerating an estimated first analyte concentration using the first sensor signal sample, the temperature value, and the first analyte temperature compensation data.

26. The method of claim 25, further comprising:accessing a second sensor signal sample indicative of the second analyte; andgenerating an estimated second analyte concentration using the second sensor signal sample.

27. The method of claim 26, further comprising:biasing the first analyte sensor to a first bias condition;while the first analyte sensor is biased to the first bias condition, generating the first sensor signal sample from the first sensor signal;biasing the first analyte sensor to a second bias condition different than the first bias condition; andwhile the first analyte sensor is biased to the second bias condition, generating the second sensor signal sample from the second sensor signal.

28. The method of any of claims 26-27, further comprising a second analyte sensor, the first sensor signal being generated by the first analyte sensor and the sensor signal sample being sampled from a second sensor signal generated by the second analyte sensor.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT0129. The method of any of claims 25-28, the first analyte temperature compensation data describing a relationship between temperature and a first analyte sensitivity of the first analyte sensor.

30. The method of claim 29, the relationship being a linear relationship over a first temperature range.

31. The method of any of claims 29-30, the relationship being an Arrhenius relationship.

32. The method of any of claims 25-31, further comprising:while operating the analyte sensor in a first mode, exposing the analyte sensor to a series of concentrations of the first analyte a first temperature;while operating the analyte sensor in a first mode, exposing the analyte sensor to the series of concentrations of the first analyte at a second temperature different than the first temperature; anddetermining analyte sensor temperature compensation data describing a relationship betw een temperature and a characteristic of the analyte sensor based at least in part on a response of the analyte sensor to the series of concentrations of the first analyte at the first temperature and the response of the analyte sensor to the series of concentrations of the first analyte at the second temperature.

33. A method for testing analyte sensors configured to sense a first analyte when operated in a first mode and to sense a second analyte when operated in a second mode, the method comprising:generating a baseline sensor characteristic using a baseline set of analyte sensors;exposing a subject analyte sensor to a first concentration of the first analyte;while the subject analyte sensor is exposed to the first concentration of the first analyte, transitioning the subject analyte sensor from the second mode to the first mode;after transitioning the subject analyte sensor from the second mode to the first mode, taking a first sensor signal sample;Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01generating a first estimated concentration of the first analyte using the baseline sensor characteristic and the first sensor signal sample; and determining an error of the subject analyte sensor based at least in part on the first estimated concentration of the first analyte and the first concentration of the first analyte.

34. The method of claim 33, further comprising, prior to transitioning the subject analyte sensor from the second mode to the first mode, taking a reference sensor signal sample, the error of the subject analyte sensor also being based at least in part on the reference sensor signal sample.

35. The method of claim 34, determining a first rebased sensor signal sample based at least in part on the first sensor signal sample and the reference sensor signal sample, the generating of the first estimated concentration of the first analyte being based at least in part on the first rebased sensor signal sample.

36. The method of any of claims 33-35, further comprising:exposing the subject analyte sensor to a second concentration of the first analyte;while the subject analyte sensor is exposed to the second concentration of the first analyte, transitioning the subject analyte sensor from the second mode to the first mode;after transitioning the subject analyte sensor from the second mode to the first mode, taking a second sensor signal sample; andgenerating a second estimated concentration of the first analyte using the baseline sensor characteristic and the second sensor signal sample, the error of the subject analyte sensor also being based at least in part on the second estimated concentration of the first analyte and the second concentration of the first analyte.

37. The method of any of claims 33-36, the exposing of the subject analyte sensor to the first concentration of the first analyte comprising exposing the subject analyte sensor to a buffer solution, the method further comprising using aAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01reference first analyte sensor to determine that the buffer solution is at the first concentration of the first analyte.

38. The method of any of claims 33-37, the baseline sensor characteristic describing a sensitivity of the first analyte sensor to the first analyte when operated in the first mode.

39. The method of any of claims 33-38, further comprising modifying the baseline sensor characteristic based at least in part on the error.

40. The method of any of claims 33-39, further comprising:while operating the baseline set of analyte sensors in the first mode, exposing the baseline set of analyte sensors to a first test concentration of the first analyte; andwhile operating the baseline set of analyte sensors in the first mode, exposing the baseline set of analyte sensors to a second test concentration of the first analyte, the second test concentration being different than the first concentration, the determining of the baseline sensor characteristic being based at least in part on a response of the baseline set of analyte sensors to the first test concentration of the first analyte and a response of the baseline set of analyte sensors to the second test concentration of the first analyte.

41. The method of any of claims 33-40, further comprising determining to redetermine the baseline sensor characteristic, the determining based at least in part on the error of the subject analyte sensor.

42. A system comprising: at least one processor and at least one memory' storing instructions, which when executed by the at least one processor perform operations comprising the method of any one of claims 33 to 41.

43. A multi-analyte sensor system operable in a first mode to measure a first analyte and in a second mode to measure a second analyte, the multi-analyte sensor system comprising:an analyte sensor; andAtty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT01sensor electronics configured to perform operations comprising:operating the multi-analyte sensor system in the first mode; while the multi-analyte sensor system is operating in the first mode, measuring a concentration of the first analyte; after operating the multi-analyte sensor system in the first mode, operating the multi-analyte sensor system in the second mode;while the multi-analyte sensor system is operating in the second mode, measuring a concentration of the second analyte; detecting a fault in the multi-analyte sensor system, the detecting being based at least in part on the concentration of the second analyte to detect a fault in the multi -analyte sensor system; andexecuting a responsive action in response to the detected fault.

44. The multi-analyte sensor system of claim 43, the first analyte being glucose and the second analyte being oxygen.

45. The multi-analyte sensor system of claim 44, the detecting of the fault in the multi-analyte sensor system comprising determining that the concentration of the second analyte is less than a threshold concentration.

46. The multi-analyte sensor system of claim 45, the threshold concentration being between about 0.1 ppm and about 0.5 ppm.

47. The multi-analyte sensor system of any of claims 45-46, the threshold concentration being between about 0.25 ppm and about 0.3 ppm.

48. The multi-analyte sensor system of any of claims 44-47, the detecting of the fault in the multi-analyte sensor system comprising determining that the concentration of the second analyte is less than a previously -measured concentration of the second analyte by more than a threshold.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT0149. The multi-analyte sensor system of claim 48, the threshold being between about 10% and about 50% of the previously -measured concentration.

50. The multi-analyte sensor system of any of claims 44-47, the detecting of the fault in the multi-analyte sensor system comprising executing a trained computerized model, the concentration of the second analyte being at least one input to the trained computerized model and an output of the trained computerized model indicating the fault in the multi-analyte sensor system.

51. A method of using a multi-analyte sensor system operable in a first mode to measure a first analyte and in a second mode to measure a second analyte, the method comprising:operating the multi-analyte sensor system in the first mode;while the multi-analyte sensor system is operating in the first mode, measuring a concentration of the first analyte;after operating the multi-analyte sensor system in the first mode, operating the multi-analyte sensor system in the second mode;while the multi-analyte sensor system is operating in the second mode, measuring a concentration of the second analyte:detecting a fault in the multi-analyte sensor system, the detecting being based at least in part on the concentration of the second analyte to detect a fault in the multi-analyte sensor system; andexecuting a responsive action in response to the detected fault.

52. The method of claim 51, the first analyte being glucose and the second analyte being oxygen.

53. The method of claim 52, the detecting of the fault in the multi-analyte sensor system comprising determining that the concentration of the second analyte is less than a threshold concentration.

54. The method of claim 53, the threshold concentration being between about 0.1 ppm and about 0.5 ppm.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT0155. The method of claim 54, the threshold concentration being between about 0.25 ppm and about 0.3 ppm.

56. The method of any of claims 52-55, the detecting of the fault in the multi-analyte sensor system comprising determining that the concentration of the second analyte is less than a previously-measured concentration of the second analyte by more than a threshold.

57. The method of claim 56, the threshold being between about 10% and about 50% of the previously-measured concentration.

58. The method of any of claims 52-55, the detecting of the fault in the multi-analyte sensor system comprising executing a trained computerized model, the concentration of the second analyte being at least one input to the trained computerized model and an output of the trained computerized model indicating the fault in the multi-analyte sensor system.

59. A method comprising:determining, based at least on in vitro data, first calibration information for an analyte sensor configured to generate at least an oxygen signal;accessing in vivo oxygen data, wherein the in vivo oxygen data is generated by the analyte sensor when the in vivo oxygen sensor is inserted into a host;determining, second calibration information for the analyte sensor based at least on the in vivo oxygen data; anddetermining an estimated oxygen concentration based at least on the second calibration information.

60. The method of claim 59, further comprising generating the in vivo oxygen data based at least on an oxygen signal generated by the analyte sensor when the analyte sensor is inserted into the host.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT0161. The method of any of claims 59-60, wherein the in vitro data is determined by at least: while operating the analyte sensor in a first mode, exposing the analyte sensor to a first concentration of oxygen;while operating the analyte sensor in the first mode, exposing the analyte sensor to a second concentration of oxygen, the second concentration being different than the first concentration.

62. The method of any of claims 59-61, wherein the analyte sensor configured to sense a first analyte when operated in a first mode and to sense a second analyte when operated in a second mode.

63. The method of any of claims 59-62, wherein the analyte sensor is further configured to generate a glucose signal.

64. The method of claim 63, further comprising: determining an estimated glucose concentration.

65. The method of claim 64, wherein the estimated glucose concentration is determined based at least on the estimated oxygen concentration.

66. The method of any of claims 59-65, wherein the estimated oxygen concentration is determined based at least on the first calibration information.

67. The method of any of claims 59-66, further comprising modifying the first calibration information based at least on the second calibration information.

68. The method of claim 67, wherein the estimated oxygen concentration is determined based at least on the modified first calibration information.

69. The method of any of claims 59-68, wherein the analyte sensor operates in a first mode for measuring a first analyte when a first bias condition is applied to the analyte sensor and in a second mode for measuring a second analyte when a second bias condition is applied to the analyte sensor.Atty. Dkt. No. 4855.145WO1 Client Reference No. 0957-PCT0170. A system comprising at least one data processor; and at least one memory storing instructions which when executed by the at least one data processor perform operations comprising the method of any one of claims 59 to 69.

71. A method, apparatus, and / or system comprising any combination of claims 1 to 70.

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