Method and apparatus for time-varying filtering of signals for a continuous analyte monitoring system

Abstract

A method of filtering a signal in a continuous analyte monitoring (CAM) system includes applying time-varying filtering to the signal using a time-varying filter during an analyte monitoring period to generate a filtered continuous analyte monitoring signal. Other methods, devices, continuous analyte monitoring devices, and continuous glucose monitoring devices are also disclosed.

Description

[0001] Cross-reference to related applications

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 034,971, filed June 4, 2020, and U.S. Provisional Patent Application No. 63 / 112,138, filed November 10, 2020, the disclosures of which are incorporated herein by reference in their entirety for all purposes. Technical Field

[0003] This disclosure relates to apparatus and methods for continuous analyte monitoring. Background Technology

[0004] Continuous analyte monitoring (CAM), such as continuous glucose monitoring (CGM), has become a routine monitoring procedure, especially for individuals with diabetes. CAM provides an individual with real-time analyte analysis (e.g., analyte concentration). In the case of CGM, real-time glucose concentration is provided. By providing real-time glucose concentration, treatment / clinical measures can be administered to the monitored individual more promptly, resulting in better control of glycemic status.

[0005] Therefore, improved CAM and CGM methods and equipment are desired. Summary of the Invention

[0006] In some embodiments, a method is provided for filtering signals in a continuous analyte monitoring system. The method includes applying a time-varying filter to the signal during an analyte monitoring period to generate a filtered continuous analyte monitoring signal.

[0007] In other embodiments, a method for filtering a continuous glucose monitoring (CGM) signal is provided. The method includes: generating a CGM signal; and applying a time-varying filter to the CGM signal during an analyte monitoring period to generate a filtered continuous analyte monitoring signal.

[0008] In other embodiments, a continuous analyte monitoring (CAM) system is provided. The system includes at least one means configured to generate a signal, and a time-varying filter configured to apply a time-varying filter to the signal during analyte monitoring.

[0009] Other features, aspects, and advantages of embodiments according to this disclosure will become more fully apparent from the following detailed description, claims, and drawings, by way of example embodiments and implementations. Various embodiments according to this disclosure may also be capable of other and different applications, and several details therein may be modified without departing from the scope of the claims and their equivalents. Therefore, the drawings and description are to be regarded as illustrative in nature and not restrictive. Attached Figure Description

[0010] The accompanying drawings described below are for illustrative purposes only and are not necessarily drawn to scale. The drawings are not intended to limit the scope of this disclosure in any way. The same numerals are used throughout to denote the same or similar elements.

[0011] Figure 1 Partial cross-sectional side views and front views of a wearable device and an external device of a continuous analyte monitoring (CAM) system according to embodiments of the present disclosure are shown, respectively.

[0012] Figure 2A A cross-sectional side view of a wearable device attached to the skin surface according to an embodiment of the present disclosure is shown.

[0013] Figure 2B A partial cross-sectional side front view of a portion of the biosensor of a CAM system according to an embodiment of the present disclosure is shown.

[0014] Figure 3A The graph illustrates a signal within a CAM system according to an embodiment of the present disclosure, a signal with noise (noise signal), a noise signal with standard filtering applied, and a noise signal with time-varying filtering applied.

[0015] Figure 3B Graphical representations illustrating examples of an individual's blood glucose concentration, unfiltered CGM signal, and time-varying filtered CGM signal according to embodiments of the present disclosure.

[0016] Figure 4A This is a schematic diagram illustrating an example of circuit components within a wearable device that represents a CGM system according to an embodiment of the present disclosure.

[0017] Figure 4B This is a schematic diagram illustrating an example of a circuit component within a wearable device according to an embodiment of the present disclosure that can communicate with an external device of the CGM system.

[0018] Figure 4C This is a schematic diagram illustrating another example of the circuitry within a wearable device and external device of a CGM system according to an embodiment of the present disclosure.

[0019] Figure 5AA block diagram illustrating an example of time-varying filtering in an embodiment of a CGM system for wearable devices according to an embodiment of the present disclosure.

[0020] Figure 5B This is a schematic diagram of a time-varying filter comprising a plurality of low-pass filters connected in series, according to an embodiment of the present disclosure.

[0021] Figure 5C A block diagram illustrating an example of signal processing including time-varying filtering in an embodiment of a wearable device using a CGM system according to an embodiment of the present disclosure.

[0022] Figure 5D A block diagram illustrating an example of signal processing in an embodiment of a CGM system according to an embodiment of the present disclosure, wherein at least some time-varying filtering is performed in an external device.

[0023] Figure 5E A block diagram illustrating another example of signal processing in an embodiment of a CGM system according to an embodiment of the present disclosure, wherein at least some time-varying filtering is performed in an external device.

[0024] Figure 6 To illustrate the example time-varying filter response versus frequency for different time periods (T1 to T4) according to the embodiments described herein.

[0025] Figure 7 A block diagram illustrating an example of an infinite impulse response filter according to an embodiment of the present disclosure.

[0026] Figure 8 A flowchart illustrating a method for filtering signals in a CAM system according to an embodiment of the present disclosure is shown. Detailed Implementation

[0027] Continuous Analytical Monitoring (CAM) systems measure and report the concentration of an analyte over time in an individual. Some CAM systems include one or more implanted biosensors that directly or indirectly sense (e.g., measure) the presence of an analyte in bodily fluids and generate one or more signals (e.g., sensor signals or biosensor signals) in response to that sensing. The one or more sensor signals are then processed to generate and / or calculate a continuous analyte signal indicating the concentration of the analyte over time. The continuous analyte signal is sometimes referred to as a “CAM signal” and is reported to the user or healthcare provider via a display, download, or other communication type.

[0028] In some embodiments, one or more biosensors may include one or more probes that pierce the user's skin and are subcutaneously positioned or implanted, for example, in tissue fluid. In other embodiments, one or more biosensors may be, for example, optical devices capable of measuring subcutaneous reflectivity. CAM systems may use other types of biosensors.

[0029] When a biosensor is located in tissue fluid, a CAM system including a subcutaneous biosensor can monitor the current between two or more electrodes on the biosensor. This current can be used to determine the concentration of an analyte (e.g., glucose concentration) in the tissue fluid. In some embodiments, the biosensor may be contained within and inserted by a cannula (e.g., a needle) configured to extend into the user's skin for subcutaneous placement of the biosensor to contact the user's tissue fluid. After insertion, the cannula can be removed, leaving the implanted biosensor. The biosensor may include electrodes, for example, that contact the user's tissue fluid, such as a working electrode, a counter electrode, and / or a reference electrode.

[0030] During continuous glucose monitoring, a voltage is applied between electrodes, for example, between the working electrode and the counter electrode, and the current flowing through one or more electrodes is measured. The current is proportional to the concentration of the analyte (e.g., glucose) present in the tissue fluid. The current through the electrodes and the tissue fluid can be very small, such as a few nanoamps, making the CAM system highly sensitive to noise. When the signal indicating the current or other signals within the CAM system are subjected to noise, even at a weak noise level, the resulting signal-to-noise ratio can be very low, producing a signal that is difficult to process and / or interpret. In some embodiments, noise can cause jitter in the resulting CAM signal, which can make the resulting CAM signal difficult to interpret accurately.

[0031] One source of noise in a CAM system is, for example, performance degradation of components within the CAM system during an analyte monitoring period. The analyte monitoring period is the time during which the biosensor in the CAM system senses an analyte. In an example of a biosensor configured to be located subcutaneously, the analyte monitoring period is the time the biosensor is located subcutaneously and actively sensing. The analyte monitoring period can be, for example, 14 days or more, the length of time the biosensor has been implanted, sensing, and communicating. In one example, biosensor characteristics may degrade over time, which can make the noise in the signal generated by the biosensor increasingly louder during the analyte monitoring period. For example, in embodiments where the biosensor is located in tissue fluid, chemicals (e.g., enzymes) deposited on the biosensor reacting with the tissue fluid may degrade and / or be depleted during the analyte monitoring period. In some cases, biofilms may also accumulate on the biosensor during the analyte monitoring period.

[0032] During an analyte monitoring cycle, the degradation and / or depletion of chemicals can increase or otherwise alter the process, leading to increasing noise and / or jitter in the sensor signal as the monitoring cycle progresses. The same situation may occur with increased biofilm accumulation. This noise can be processed by the sensor signal to produce noisy and / or jittery CAM results that are difficult to interpret or may lead the user to believe that the CAM system is not functioning correctly.

[0033] The apparatus and methods disclosed herein reduce the effects of noise in such CAM systems by applying time-varying filters to one or more signals in the CAM system to generate at least one time-varying filtered continuous analyte signal. Noise reduction can be achieved, for example, by smoothing one or more signals generated in the CAM system using time-varying filters. The performance degradation of the biosensors described above and / or the performance degradation of other components during the analyte monitoring cycle can be known or estimated, which allows the amount of filtering applied by the time-varying filter to be varied (e.g., increased) during the analyte monitoring cycle in order to filter out the varying (e.g., increased) noise levels.

[0034] The time-varying filtering described herein can be applied to various signals within a CAM system, including, for example, working electrode current signals, background current signals, CAM signals, estimated device sensitivity signals, and estimated analyte (e.g., glucose) concentration signals. Time-varying filtering smooths the signal and / or reduces the effects of noise and / or algorithmic artifacts, thus improving the user's ability to interpret analyte concentrations. The time-varying filtering can be applied based on the amount of time the CAM system has been running. For example, the filtering can be modified by adjusting the smoothing parameters accordingly to respond to changes over time (e.g., sensor performance degradation).

[0035] refer to Figure 1-8 These and other devices and methods are described in detail. This document describes embodiments of time-varying filtering devices and methods with reference to continuous glucose monitoring (CGM) systems. However, the time-varying filtering devices and methods described herein can be applied, for example, to other continuous analyte monitoring (CAM) systems for measuring analytes such as cholesterol, lactate, uric acid, and alcohols.

[0036] Now for reference Figure 1 The figure illustrates an example of a continuous glucose monitoring (CGM) system 100 including a wearable device 102 and an external device 104. As described herein, the wearable device 102 measures glucose concentration, and the external device 104 displays the glucose concentration. In some embodiments, the wearable device 102 may also display glucose concentration. For example, the wearable device 102 may be attached (e.g., adhered) to a user's skin 108, for example, via an adhesive backing layer 110.

[0037] Wearable device 102 may include a biosensor 112, which may be located subcutaneously in the user's tissue fluid 114 and can directly or indirectly measure glucose concentration. Wearable device 102 can transmit the glucose concentration to an external device 104, whereby the glucose concentration can be displayed on an external display 116. External display 116 can display glucose concentration in different formats, such as single numbers, graphs, and / or tables. Figure 1 In an example embodiment, the external display 116 is displaying a graph 118 showing past and present glucose concentrations, as well as a number indicating the glucose concentration from a recent glucose calculation. The external display 116 may also display a glucose trend, as indicated by the downward arrow 131 shown on the external display 116, indicating that the user's blood sugar level is currently decreasing. The external display 116 may display different or additional data in other formats. In some embodiments, the external device 104 may include multiple buttons 120 or other input devices that enable the user to select the data and / or data format displayed on the external display 116.

[0038] Now for reference Figure 2A The figure shows a partial cross-sectional side view of the wearable device 102 attached to the user's skin 108. The biosensor 112 may be located in tissue fluid 114 beneath the user's skin 108. Figure 2A In some embodiments, the biosensor 112 may include a working electrode 112A, a reference electrode 112B, and a counter electrode 112C, each of which is in contact with tissue fluid 114, as further described below. In some embodiments, the biosensor 112 may include fewer or more electrodes and other electrode configurations. For example, in some embodiments, a second working electrode (e.g., a background electrode) may be employed. Electrodes 112A, 112B, and 112C may be prepared and / or coated with one or more chemicals, such as one or more enzymes that react with specific chemical analytes within the tissue fluid 114. The reaction may alter the current passing through one or more of electrodes 112A, 112B, and 112C, which is detected by the wearable device 102 and used to calculate glucose concentrations as described herein.

[0039] Figure 2BA schematic enlarged partial cross-sectional side view of an embodiment of a biosensor 112 according to embodiments provided herein is shown. In some embodiments, the biosensor 112 may include a working electrode 112A, a counter electrode 112C, and a background electrode 112D. The working electrode 112A may include a conductive layer coated with a chemical substance 112F that reacts with a glucose-containing solution in a reduction-oxidation reaction, affecting the concentration of charge carriers and the time-dependent impedance of the biosensor 112. In some embodiments, the working electrode 112A may be formed of platinum or surface-roughened platinum. Other working electrode materials may be used. Example chemical catalysts (e.g., enzymes) for the working electrode 112A include glucose oxidase, glucose dehydrogenase, etc. For example, the enzyme component may be immobilized to the electrode surface by a crosslinking agent such as glutaraldehyde. An outer membrane layer (not shown) may be applied to the enzyme layer to protect the entire internal components including the electrode and the enzyme layer. In some embodiments, a mediator, such as ferricyanide or ferrocene, may be used. Other chemical catalysts and / or mediators may be used.

[0040] In some embodiments, the reference electrode 112B may be formed of Ag / AgCl. The counter electrode 112C and / or the background electrode 112D may be formed of a suitable conductor (e.g., platinum, gold, palladium, etc.). Other suitable conductive materials may be used for the reference electrode 112B, the counter electrode 112C, and / or the background electrode 112D. In some embodiments, the background electrode 112D may be the same as the working electrode 112A, but without the chemical catalyst and mediator. The counter electrode 112C may be isolated from other electrodes by an insulating layer 112E (e.g., polyimide or another suitable material).

[0041] The biosensor 112 may include other items and materials not shown. For example, the biosensor 112 may include other insulators, such as those that electrically insulate the electrodes from each other. The biosensor 112 may also include conductors, such as those that electrically connect the electrodes to components in the wearable device 102.

[0042] The aforementioned chemicals on or in the working electrode 112A, reference electrode 112B, counter electrode 112C, and background electrode 112D may be depleted and / or contaminated during the analyte (e.g., glucose) monitoring cycle. Depletion and / or contamination can cause the signal generated by or in conjunction with the biosensor 112 to become noisy and / or jittery over time, as described herein. Additionally, biofilms may accumulate on electrodes 112A, 112B, 112C, and / or 112D, which can further amplify the noise in the signal generated by the biosensor 112. The time-varying filtering described herein filters or smooths one or more signals within the CGM system 100 to counteract the effects of noise and / or jitter.

[0043] return Figure 2AThe wearable device 102 may include a substrate 124 (e.g., a circuit board), on which components 126 of the wearable device 102 may be located. Portions of the substrate 124 may be made of a non-conductive material, such as plastic or ceramic. In some embodiments, the substrate 124 may include a laminated material. The substrate 124 may include electrical traces (not shown) that conduct current to components within or attached to the substrate, such as a biosensor 112. For example, conductors (not shown) may electrically connect electrodes 112A, 112B, and 112C to component 126.

[0044] Component 126 can apply a bias voltage to two or more of the electrodes 112A, 112B, 112C, and 112D located in the tissue fluid 114, causing a bias sensor current to flow through the biosensor 112. Some of components 126 can be used to measure the sensor current and generate the measured current signal I. MEAS Part of the circuit. In some embodiments, chemicals (enzymes, etc.) on or within electrodes 112A, 112B, 112C change their impedance in response to contact with glucose or other chemicals or analytes present in tissue fluid 114. Therefore, the resulting measured current signal I... MEAS It can be proportional to one or more analytes (e.g., glucose) present in the tissue fluid 114. During the glucose monitoring cycle, the chemicals on electrodes 112A, 112B, and 112C may degrade and / or deplete, which may affect the sensor current and the measured current signal I. MEAS As described above, it becomes noisy (e.g., jitter).

[0045] The rates of chemical degradation and / or depletion on electrodes 112A, 112B, 112C, and 112D, and the rates of biofilm accumulation on electrodes 112A, 112B, 112C, and 112D, can be known (e.g., experimentally) or otherwise estimated. As described herein, time-varying filtering can be applied to the measured current signal I in wearable device 102 and / or external device 104. MEAS And / or other signals to reduce the effects of noise changes over time. In some embodiments, time-varying filtering may be applied to the resulting CGM signal to reduce noise (e.g., jitter) on the CGM signal. As described herein, time-varying filtering can vary (e.g., increase) the attenuation in the stopband and / or vary (e.g., increase) the order of the time-varying filter according to time.

[0046] Now for reference Figure 3A This figure is a graph illustrating examples of different filtering effects (including time-varying filtering) on ​​noisy signals. Figure 3AIn the example, the ideal signal 302 (solid line) is normalized to a signal value of 1.00. The noise signal 304 is shown as a dotted line with dots representing data points; the noise signal is the ideal signal 302 with added noise. Figure 3A As shown in the example, the amplitude of the noise signal 304 increases over time. The standard exponential moving average (EMA) filtered signal 306, which is the noise signal 304 after undergoing standard (EMA) filtering, is shown as a dashed line with squares within it. Standard EMA filtering is time-independent; therefore, the filtering applied by standard EMA filtering does not change over time. Figure 3A As shown, the noise on the standard EMA filtered signal 306 continues to increase over time.

[0047] When such conventional filtering is applied to a signal in a CGM system, the noise on the signal continues to increase over time. Therefore, the signal-to-noise ratio (SNR) of these signals decreases over time, which can reduce the CGM system's 100% noise level. Figure 1 The data provided is inaccurate or difficult to interpret.

[0048] The time-varying filtered signal 308 is the result of the noise signal 304 undergoing time-varying filtering (e.g., time-varying EMA filtering), and... Figure 3A The image is displayed as a dashed line with x on it. This produces... Figure 3A The time-varying filter signal 308 shown increases with time. For example, smoothing or high-frequency attenuation can increase with time. Therefore, as the amplitude of noise on the ideal signal 302 increases with time, as shown by the increase in the amplitude of noise signal 304 over time, the resulting time-varying filter signal 308 is smoothed or filtered to a greater extent over time. Thus, the resulting time-varying filter signal 308 with applied time-varying filtering more closely follows the ideal signal 302. When applied to a CGM system, the time-varying filter reduces noise that increases during the analyte (e.g., glucose) monitoring period, allowing the CGM system user to receive more accurate information about the concentration of the analyte (e.g., glucose).

[0049] For further reference Figure 3B The figure is a graph illustrating an example of a reference blood glucose concentration 312, an unfiltered CGM signal 314, and a time-varying filtered CGM signal 316. Figure 3B The horizontal axis of the graph is referenced to the elapsed time (in days) and sample number. Note that... Figure 3B The example shown in the graph was recorded during a portion of an analyte (e.g., glucose) monitoring cycle, approximately eight hours from the end of day 12 to day 13. The unfiltered CGM signal 314 can be generated by a wearable device 102 that measures and / or calculates the concentration of the user's analyte (e.g., glucose). Figure 1The unfiltered CGM signal 314 is generated by the external device 104. The time-varying CGM signal 316 can be generated by applying time-varying filtering to the unfiltered CGM signal 314 and / or one or more other signals used to generate the unfiltered CGM signal 314.

[0050] In some embodiments, one or more signals generated by biosensors within the wearable device 102 may be subjected to time-varying filtering, which may produce a time-varying filtered CGM signal 316 (sometimes referred to as filtered CGM signal 316). For example, noise signal 304 ( Figure 3A The signal 316 can be generated by a biosensor within the wearable device 102. The time-varying filtered CGM signal 316 can be the result of applying a time-varying filter to the noise signal 304. For example... Figure 3B As shown, the resulting time-varying filtered CGM signal 316 generally follows the reference blood glucose concentration 312 and is much smoother than the unfiltered CGM signal 314, which exhibits more pronounced jitter.

[0051] Now refer to another source Figure 4A The figure shows wearable device 102 ( Figure 2A A schematic diagram of an embodiment of an example circuit. In Figure 4A In the illustrated embodiment, the biosensor 112 does not include a background electrode 112D ( Figure 2B ).like Figure 4A As shown, the working electrode 112A may be surrounded by a guard ring 412, which reduces interference from stray currents to the working electrode 112A. In some embodiments, the guard ring 412 may operate at the same potential as the working electrode 112A. The working electrode 112A may be connected to the working electrode source 430 via a current measuring device, such as an ammeter 432. The ammeter 432 measures the working electrode current I generated by the working electrode source 430. WE And generate an indicator current I for the working electrode. WE The measured current signal I MEAS During operation of the wearable device 102, the working electrode source 430 can generate a voltage V applied to the working electrode 112A. WE This generates a working electrode current I through the working electrode 112A. WE Ampere 432 measures the working electrode current I. WE And generate the measured current signal I. MEAS .

[0052] exist Figure 4A In some embodiments, the wearable device 102 may include a counter electrode source 436 electrically connected to the counter electrode 112C, the counter electrode source generating a counter electrode voltage V. CE Therefore, the working electrode current I WEWith working electrode voltage V WE and counter electrode voltage V CE The difference between the two is proportional to the impedance of the tissue fluid 114 (Figure 2) and the impedance of the electrode in the biosensor 112. In some embodiments, the current flowing in from the counter electrode source 436 is equal to the working electrode current I. WE .

[0053] Both the working electrode source 430 and the counter electrode source 436 can be coupled to and controlled by the processor 438. The processor 438 may include a memory 440 containing computer-readable instructions stored therein, which cause the processor 438 to send instructions to the working electrode source 430 and the counter electrode source 436. The instructions can cause the working electrode source 430 and the counter electrode source 436 to output a predetermined voltage (e.g., V). WE and V CE The memory 440 may also include instructions that cause the processor to perform other functions as described herein, such as applying time-varying filtering.

[0054] Figure 4A The circuitry of an embodiment of the wearable device 102 shown may include a protection source 444, which is connected to a protection ring 412 and transmits a protection voltage V. G The protection source 444 is supplied to the protection ring 412. It can also be connected to the processor 438 and can receive instructions from the processor 438 to set a specific protection voltage V. G The reference electrode 112B can be connected to the processor 438 and can transmit the reference voltage V. R Supply to processor 438. Processor 438 can use reference voltage V. R To set the operating voltage V WE and reverse voltage V CE The value of .

[0055] As mentioned above, the ammeter 432 can generate the measured current signal I. MEAS It is the working electrode current I WE The measurement. In a conventional CGM system, if the working electrode current I... WE If noise exists in the measured current signal I, then... MEAS The resulting CGM signal may be noisy. For example, the resulting CGM signal may resemble... Figure 3B The unfiltered CGM signal 314. Figure 4A In the embodiment, the time-varying filter 448 is applied to the measured current signal I. MEAS Before being processed and / or received by processor 438, the measured current signal I... MEAS Apply time-varying filtering. The time-varying filter 448 outputs the filtered measured current signal I. FILTThe current signal can be processed by processor 438 to reproduce the time-varying filtered CGM signal 316.

[0056] For further reference Figure 5A The figure is shown Figure 4A A block diagram illustrating the function of a portion of the circuit. (See example in...) Figure 5A As shown in the example, the processor 438 processes the measured current signal I. MEAS Previously, time-varying filtering was applied to the measured current signal I by time-varying filter 448. MEAS In other embodiments described herein, time-varying filtering may be performed by processor 438 and / or applied to other signals within external device 104. Figure 1 ).

[0057] The measured current signal I MEAS It can be similar to Figure 3A The noise signal 304 shown is a noise signal. As described herein, the performance of the biosensor 112 can degrade over time, which causes the measured current signal I to... MEAS The noise level increases over time. If the processor 438 processes a noisy measured current signal I... MEAS The resulting CGM signal may be corrupted and noisy (e.g., jitter), such as... Figure 3B The unfiltered CGM signal 314 is shown. The filtered measured current signal I is output from the time-varying filter 448. FILT The measured current signal I MEAS The signal is smoother (e.g., less noise and / or jitter), resulting in a smoother CGM signal. For example, the filtered measured current signal I... FILT It can be similar to Figure 3A The time-varying filtered signal 308 more closely follows the ideally measured current signal (e.g., noise-free), for example... Figure 3A The ideal signal 302 in the equation. The resulting CGM signal is similar to... Figure 3B The time-varying filtered CGM signal 316 typically follows the reference blood glucose concentration 312 and is much smoother than the unfiltered CGM signal 314. Therefore, the current signal I applied to the measured signal... MEAS The time-varying filter provides the CGM signal, which more closely follows the user's blood glucose level, and is suitable for wearable devices 102 ( Figure 1 This might be easier for users to understand.

[0058] Embodiments of the time-varying filter 448 include analog and digital filters. In some embodiments, the time-varying filter 448 may be an analog or digital low-pass filter. In some embodiments, the time-varying filter 448 may be an infinite impulse response (IIR) filter or a finite impulse response (FIR) filter. In some embodiments, the time-varying filter 448 may apply an exponential moving average (EMA) to the measured current signal I. MEAS Or other signals. The attenuation of the low-pass filter can increase over time, resulting in greater attenuation later in the analyte (e.g., glucose) monitoring period. In some embodiments, the time-varying filter 448 may be an analog low-pass filter, wherein the order of the low-pass filter can increase over time. In some embodiments, one or more cutoff frequencies of the time-varying filter 448 may change over time.

[0059] For further reference Figure 5B The figure illustrates an example of a time-varying filter 448, implemented, for example, as a filter bank of multiple low-pass filters, referred to as a first low-pass filter LPF1, a second low-pass filter LPF2, and a third low-pass filter LPF3 connected in series. The time-varying filter 448 may include fewer or more low-pass filters. The time-varying filter 448 may also include a switch (SW) connected in parallel with each of the low-pass filters LPF1, LPF2, and LPF3. Figure 5B In this embodiment, the switches are referred to as first switch SW1, second switch SW2, and third switch SW3, respectively. The states of switches SW1, SW2, and SW3 can be controlled by processor 438. The amount of time-varying filtering applied by time-varying filter 448 can be adjusted by opening or closing switches SW1, SW2, and SW3. For example, during a first time period, all switches SW1, SW2, and SW3 can be closed, and therefore no filtering is applied. During subsequent time periods, the switches can be opened to gradually apply more filtering over time.

[0060] For further reference Figure 6 This figure shows an example filter response of time-varying filter 448 over time, where time-varying filter 448 is a low-pass filter with a cutoff frequency f0. See below for reference. Figure 5B Time-varying filter 448 description Figure 6 The graph. However, other time-varying filters, such as time-varying IIR filters, can produce similar results.

[0061] During the first time period T1, the time-varying filter 448 may not apply filtering. For example, filtering may not be necessary in the early stages of the glucose monitoring cycle. Therefore, the unfiltered elapsed time is the first time period T1. During the second time period T2, the time-varying filter 448 can be used as a first-order low-pass filter. The elapsed time of the second time period T2 can begin at a predetermined time after the start of the glucose monitoring cycle. In some embodiments, the second time period T2 can begin after the start of the glucose monitoring cycle, for example, at least twenty-four hours after the start of the glucose monitoring cycle. The elapsed time of the second time period T2 can begin after the end of the first time period T1 and can end when the third time period T3 begins. Figure 6 As shown, the attenuation in the stopband is minimal during the second time period T2. The filtering shown during the second time period T2 can be achieved by turning on one of the switches, such as SW-1.

[0062] During the third time period T3, which may occur after the second time period T2, the low-pass filter can be a higher-order filter than the one used during the first time period T1. Regarding Figure 5B The time-varying filter 448 has two switches (e.g., switches SW-1 and SW-2) that can be turned on by the processor 438. The elapsed time of the third time period can begin at the end of the second time period and end at the beginning of the fourth time period T4. During the fourth time period T4, the time-varying filter 448 can be a higher-order low-pass filter than during the third time period T3. During the fourth time period T4, the processor can turn on all switches SW-1, SW-2, and SW-3. The elapsed time of the fourth time period T4 can begin at the end of the third time period T3 and end at the end of the monitoring cycle.

[0063] The time-varying filter 448 can increase the order of the low-pass filter according to time. In some embodiments, the cutoff frequency f0 can change with each different time period. For example, a higher noise level on the signal may include higher or lower frequency components. The cutoff frequency f0 can change with the change of the frequency components of the noise.

[0064] In some embodiments, the time-varying filter 448 can be a digital filter, such as an FIR (Finite Impulse Response) filter or an IIR (Infinite Impulse Response) filter. Other types of digital filters may be used. See also... Figure 7 This figure is a block diagram of an example embodiment of an IIR filter 760 that can be used in a time-varying filter 448. Other configurations of digital filters and IIR filters can be used. The IIR filter 760 receives the measured current signal I as a digital signal. MEAS (or another signal). In some embodiments, the ampere 432 ( Figure 4A ) Generates digital signals, and in other embodiments, Figure 4AThe circuit includes a digitizable measured current signal I. MEAS An analog-to-digital converter (not shown). In other embodiments, the IIR filter 760 can be used to filter other signals in the CGM system 100, such as unfiltered CGM signals. Figure 1 ) Perform filtering.

[0065] The measured current signal I MEAS The input is received at the feedforward side of the IIR filter 760 at the first unit delay 762A of a series of unit delays 762 and the first multiplier 764A of a series of multipliers 764. The output of the multiplier 764 is output to a plurality of adders 766, including the first adder 766A. The output of the first adder 766A is input to the first adder 768A of a series of adders 768 on the feedback side of the IIR filter 760. The output of the first adder 768A is the output of the IIR filter 760. The output is fed to a series of unit delays 770, which output to a series of multipliers 772. The output of the multipliers 772 is input to the adders 768. The filtering of the IIR filter 760 is established by the coefficients P0-P3 of the multipliers 764 and the coefficients -d1 to -d3 of the multipliers 772, which can be time-varying to provide the time-varying filtering described herein.

[0066] The following describes other embodiments of time-varying filtering with respect to the general signal S(t) in the CGM system 100. In these embodiments, a filter F is applied to the signal S(t) to obtain a smoother output S' as follows:

[0067] S'(t)=F(S(t)) Equation (1)

[0068] exist Figure 4A In this embodiment, the filter F can be a time-varying filter 448, and the signal S(t) can be, for example, the measured current signal I. MEAS The unfiltered CGM signal or another signal. In time-varying filtering, the filter F can depend on time t, such that equation (1) yields the following equation (2):

[0069] Equation (2) is S'(t)=F(t,S(t)).

[0070] The time-varying filter of equation (2) can produce the following equation (3) for the exponential smoothing filter:

[0071] S'(t)=alpha*S(t)+(1-alpha)*S'(t-1) Equation (3)

[0072] Here, alpha is a value less than or equal to 1.0. When alpha equals 1.0, no smoothing (e.g., filtering) is performed on the signal S(t). As alpha decreases over time, the filter smooths the signal S(t).

[0073] In an embodiment where the time-varying filter 448 is a digital filter, such as an IIR filter, and the signal S is a digital signal S(n), equation (2) can be written in the discrete domain as F(n, S(n)) shown in equation (4) below:

[0074] S'(n)=alpha*S(n)+(1-alpha)*S'(n-1) Equation (4)

[0075] Smoothing can be applied using an exponential moving average (EMA). Variations of the filtering / smoothing methods exist. Two variations are referred to as DEMA and TEMA (double EMA and triple EMA, respectively), which can be used in the time-varying filter 448. To make the filtering change over time, alpha can be changed according to time. In some embodiments, alpha is made to decrease steadily as the elapsed time from the start of the glucose monitoring cycle increases, as described below in equation (5):

[0076] alpha(t) = baseAlpha - t / N, equation (5)

[0077] Where t is time, baseAlpha can be a predetermined value, and can be available on wearable device 102 ( Figure 1 The nominal (e.g., maximum) value of alpha is determined during the design phase and may never change in some embodiments. N is a constant used to control the rate of change of alpha(t). In some embodiments, when t is greater than seven days, baseAlpha can be in the range of about 0.3 to about 0.5, and N can be chosen such that alpha(t) is less than or equal to baseAlpha / 2. Other values ​​of baseAlpha, t, and / or N, and their adjustments, can be used. Thus, in some embodiments, more smoothing is applied as time increases. In some embodiments, the increased smoothing is applied with a stable linear time schedule. In other embodiments, the increased smoothing is applied in a non-linear manner, such as in a stepwise manner or as a non-linear function. In some embodiments, alpha(n) can be used instead of alpha(t), where n is the sample number.

[0078] In some embodiments, alpha(t) may be greater than a minimum value to prevent over-smoothing. In other embodiments, alpha(t) may vary non-linearly over time, or be limited to certain time periods. In some instances, smoothing or filtering may begin at a fixed time after the start of the glucose monitoring cycle. In some embodiments, smoothing or filtering may begin at least twenty-four hours after the start of the glucose monitoring cycle. In some embodiments, filtering may be applied to any or all of the following: for example, the working electrode current I. WE The current through the reference electrode, the CGM signal, and the measured current signal I. MEAS etc.

[0079] Refer again Figure 4A The circuitry allows the processor 438 to receive the filtered measured current signal I. FILT And at least in part based on the filtered measured current signal I. FILT Calculate the CGM signal. For example, instructions (e.g., a program) stored in memory 440 can cause processor 438 to process the filtered measured current signal I. FILT The glucose concentration in tissue fluid 114 (Figure 2) is calculated or estimated, and a filtered CGM signal S is generated. FCGM Filtered CGM signals can reflect other analytes and can be referred to as time-varying filtered CAM signals. This is because the filtered measured current signal I... FILT The smoothed CGM signal S is obtained. FCGM This will also be relative to using a current signal I that is measured. MEAS The unfiltered current measurement signal is used to smooth the calculated CGM signal. In some embodiments, the time-varying filter 448 can smooth the measured current signal I. MEAS The CGM signal can be further filtered or smoothed by another time-varying filter implemented in processor 438 to produce the final filtered CGM signal S. FCGM .

[0080] Filtered CGM signal S FCGM The processor 438 can output to the transmitter / receiver 449. The transmitter / receiver 449 can filter the CGM signal S. FCGM The signal is transmitted to an external device (e.g., external device 104) for processing and / or display on an external display 116. In some embodiments, the processor 438 can filter the CGM signal S. FCGM The signal is transmitted to an optional local display 450 located on the wearable device 102, where the filtered CGM signal S can be displayed. FCGM And / or other information.

[0081] Now for reference Figure 4BThe figure illustrates another embodiment of the circuitry that can be configured in the wearable device 102. Figure 1 ).exist Figure 4B In this embodiment, the time-varying filter 448 is implemented in the processor 438. For example, the time-varying filter 448 may be a digital filter, wherein instructions for time-varying filtering are stored in memory 440 and executed by the processor 438. The processor 438 may apply the time-varying filter or smoothing described in equation (4) to the measured current signal I. MEAS And / or unfiltered CGM signal. Filtered CGM signal S FCGM It can be output to transmitter / receiver 449 for transmission to an external device (e.g., external device 104). Filtered CGM signal S FCGM It can also be transmitted to an optional local display 450 for display as described above. Figure 5C It shows Figure 4B A block diagram of a time-varying filter in an embodiment. (See diagram below.) Figure 5C As shown, processor 438 receives and processes the measured current signal I. MEAS Output filtered CGM signal S FCGM .

[0082] As described above, the time-varying filter 448 can be implemented in the processor 438. Therefore, the time-varying filter 448 can apply a smoothing function as described in equation (4). As described above, the time-varying filter 448 can implement an FIR filter or an IIR filter.

[0083] Now for reference Figure 4C This figure illustrates another embodiment of the circuitry in a CGM system 100, including a wearable device 102 and an external device 104. Figure 4C In some embodiments, time-varying filtering is implemented at least in part in external device 104 as described herein. Figure 4C In some embodiments, the external device 104 may include a transmitter / receiver 454, an external display 116, a processor 458, a memory 460, and a time-varying filter 462, which may be stored in the memory 460 and implemented (e.g., executed) by the processor 458. In some embodiments, the transmitter / receiver 454 may receive an unfiltered CGM signal from a transmitter / receiver 449 located in the wearable device 102. In some embodiments, the transmitter / receiver 449 and the transmitter / receiver 454 may communicate wirelessly, for example via... Or other suitable communication protocols. Transmitter / receiver 454 can also transmit instructions to wearable device 102.

[0084] The time-varying filter 462 can be a digital filter, wherein instructions for time-varying filtering are stored in memory 460 and combined with, for example, Figure 4B The same or similar manner described is performed by processor 458. As described above, time-varying filtering can be applied to the unfiltered CGM signal transmitted from wearable device 102. In some embodiments, external device 104 can receive the measured current signal I. MEAS Furthermore, the time-varying filter 462 can be combined with... Figure 4A and 4B The described processing of the measured current signal I MEAS To generate the filtered CGM signal S FCGM For example, the time-varying filter 462 can generate something similar to I. FILT The signal, which can be processed by processor 458 to generate a filtered CGM signal S. FCGM Filtered CGM signal S FCGM And / or other data calculated by processor 458 may be output to external display 116 and / or otherwise downloaded to another device (e.g., a computer).

[0085] exist Figure 5D and 5E It shows Figure 4C A block diagram of a time-varying filter in an embodiment. (See diagram below.) Figure 5D As shown, processor 458 receives an unfiltered CGM signal and executes time-varying filter 462 to generate a filtered CGM signal S. FCGM Unfiltered CGM signals can be received from wearable device 102. Figure 5E In the middle, the measured current signal I MEAS The signal is received in external device 104 and input to time-varying filter 462. Time-varying filter 462 outputs the filtered measured current signal I. FILT The current signal is processed by processor 458 to generate a filtered CGM signal S. FCGM .

[0086] In each embodiment, an optional local display 450 and / or external display 116 may display graphics and / or numbers indicating glucose concentration. The displayed information may also include trends in glucose concentration, such as downward and upward trends (e.g., displayed as upward or downward arrows). Other information, such as units, may also be displayed. Due to the filtered CGM signal S FCGM The signal has been filtered using a time-varying filter, therefore the graphics and / or other information are more accurate than the regular information displayed to the user. This is achieved by filtering the CGM signal S. FCGM Examples of higher accuracy in the information provided are given by Figure 3B The time-varying filtered CGM signal 316 is shown in the figure.

[0087] Examples of filtering and / or smoothing are described in the following examples. Figure 3BThe unfiltered CGM signals 314A and 314B shown are from the thirteenth day of the CGM monitoring period and contain significant noise. For example, portion 314A indicates that the user's glucose concentration rose from approximately 125 mg / dL to approximately 180 mg / dL over approximately four sample periods. Portion 314B indicates that the user's glucose concentration decreased from 180 mg / dL to approximately 120 mg / dL over the next four sample periods. Reference blood glucose concentration 312 indicates that the user's glucose concentration decreased from approximately 150 mg / dL to approximately 145 mg / dL over eight sample periods combined from portions 314A and 314B. If the user relies on the unfiltered CGM information in portion 314A, they will be notified that their glucose concentration is rising rapidly when it is actually decreasing slightly. If the user relies on the information in portion 314B, they will be notified that their glucose concentration is decreasing rapidly when it is actually decreasing slowly.

[0088] Filtered CGM signal S FCGM 316 includes portions 316A and 316B, which respectively reflect the filtered CGM signal S during the same sampling time period as portions 314A and 314B. FCGM A glucose concentration of 316. (For example...) Figure 3B As shown, the filtered CGM signal S FCGM 316 increased from approximately 125 mg / dL to approximately 155 mg / dL during part 316A and decreased from approximately 155 mg / dL to approximately 125 mg / dL during part 316B. This was determined by the filtered CGM signal S. FCGM The glucose concentration changes provided by 316 are not as abrupt as those provided by the unfiltered CGM signal 314. Therefore, the information provided to the user more accurately reflects the true glucose concentration. For example, the rise in glucose concentration shown in section 316A and the subsequent drop in glucose concentration shown in section 316B are not as severe as those shown in the unfiltered CGM signal 314 and more closely follow the reference blood glucose concentration 312. Therefore, using time-varying filtering in the CGM system improves the reliability of the data generated by the CGM system (including the CGM signal).

[0089] See Table 1 below, which summarizes the results for various filtering options. MARD used in Table 1 is the mean absolute relative difference. Static filters are those whose attenuation remains constant over time.

[0090] For CGM glucose determination, MARD is described by the following equation (6):

[0091] MARD = 100 * [Abs([G CGM -GREF ] / G REF Equation (6)

[0092] Among them G CGM Glucose levels measured by CGM REF Let n be a reference glucose concentration, for example, measured by a blood glucose meter (BGM), and n be the number of data points. The expression for MARD combines the mean and standard deviation of the sample population relative to the reference glucose value to produce a composite MARD value, where a smaller MARD value indicates higher accuracy. In some embodiments, a 10% MARD value may have approximate data accuracy within ±25%, or approximately 25% accuracy. Conversely, a CGM system with ±10% accuracy will be predicted to have a MARD value of 4%. As shown in Table 1, the embodiments described herein using time-varying filtering are approximately equivalent to the MARD values ​​of conventional filtering.

[0093] Table 1 - Data Comparison

[0094] MARD 0-7 days 13.75 13.89 14.06 MARD 0-10 days 13.73 13.90 14.03 Smoothness 0-10 days 0.154 0.118 0.107 Smoothness 7-10 days 0.194 0.143 0.125

[0095] Smoothness can be calculated using different techniques. For example, it can be calculated using the arithmetic mean. In other embodiments, smoothness can be calculated as the standard deviation of the glucose difference divided by the absolute value of the mean of the glucose differences. Other methods can be used to calculate smoothness. As shown in Table 1, signals with time-varying filters applied are smoother than conventional signals.

[0096] CGM has been described as a device using a biosensor located in tissue fluid. Other CGM devices can also be used. For example, optical sensors can also be used for continuous glucose or analyte monitoring. Optical devices can use fluorescence, absorbance, reflectance, etc., to measure glucose or other analytes. For example, fluorescence-dependent or fluorescence-quenched optical oxygen sensors can be used to indirectly measure glucose by measuring the oxygen concentration in tissue fluid, which is inversely proportional to the glucose concentration.

[0097] Now for reference Figure 8 This figure illustrates a flowchart depicting a method 800 for filtering a continuous analyte monitoring (CAM) signal. Filtering a continuous glucose monitoring (CGM) signal is one example. Other suitable analytes, such as lactate, can be monitored. Method 800 includes generating a CAM signal in 802. Method 800 includes applying a time-varying filter (e.g., time-varying filters 448, 462) to the CAM signal during the analyte monitoring period in 804 to generate a time-varying filtered CGM signal (e.g., a time-varying filtered CGM signal S). FCGM 316).

[0098] As discussed above, there are two general types of time-varying filtering, including: 1) signals within the CGM system 100 (e.g., the measured current I). MEAS The signal is time-varying and further processed to produce the filtered CGM signal S. FCGM The filtered CGM signal can be transmitted from transmitter / receiver 449 to external device 104, and 2)I MEAS The signal is processed to generate an unfiltered CGM signal, which is then further processed to produce a filtered CGM signal S. FCGM .

[0099] The foregoing description discloses only exemplary embodiments. Modifications to the devices and methods disclosed above that fall within the scope of this disclosure will be readily apparent to those skilled in the art.

Claims

1. One or more non-transient computer-readable media comprising computer-executable instructions, which, when executed by at least one processor, perform a method for filtering signals in a continuous analyte monitoring system during an analyte monitoring cycle, the method comprising: A time-varying filter is applied to the signal to smooth it, wherein the signal is generated by the biosensor during the analyte monitoring period, and the analyte monitoring period includes multiple different time periods. The time-varying filter includes: a plurality of low-pass filters and a plurality of switches, each of the plurality of switches being connected in parallel to at least one of the plurality of low-pass filters. For each of the plurality of different time periods, the time-varying filter applies a filtering level to the signal. The application of the time-varying filter includes: For each different time period, an additional low-pass filter is turned on, such that the additional low-pass filter applies the filtering level relative to the preceding low-pass filters that applied the filtering level during the immediately preceding time period. The additional low-pass filter of one of the plurality of low-pass filters applies an increased filtering level relative to the immediate preceding filtering level corresponding to the immediate preceding time period.

2. The one or more non-transient computer-readable media according to claim 1, Applying the time-varying filter to the signal further includes: Pass the signal through at least one low-pass filter; as well as For each of the multiple different time periods, the filter level is applied to the signal. For each filter level in each different time period, the attenuation level in the stopband of the at least one low-pass filter increases relative to the immediately preceding attenuation level in the stopband of the at least one low-pass filter corresponding to the immediately preceding time period.

3. The one or more non-transient computer-readable media according to claim 1, The signal is the measured current signal, and applying the time-varying filter to the signal further includes: The concentration of the analyte is calculated based on the measured current signal; A continuous analyte monitoring signal is generated based on the analyte concentration; as well as The time-varying filter is applied to the continuous analyte monitoring signal during the analyte monitoring period to generate a filtered continuous analyte monitoring signal.

4. The one or more non-transient computer-readable media according to claim 3, further comprising: At least a portion of the filtered continuous analyte monitoring signal is displayed on the monitor; The filtered continuous analyte monitoring signal is analyzed during the analyte monitoring period to generate a trend in analyte concentration; as well as The trend of the analyte concentration is displayed on the monitor.

5. The one or more non-transient computer-readable media according to claim 1, The signal is the measured current signal, and applying the time-varying filter to the signal further includes: The time-varying filter is applied to the measured current signal during the analyte monitoring period to generate a filtered measured current signal; The analyte concentration is calculated based on the filtered measured current signal; and A filtered continuous analyte monitoring signal is generated based on the analyte concentration.

6. The one or more non-transient computer-readable media according to claim 1, The time-varying filter mentioned above includes an infinite impulse response filter.

7. One or more non-transient computer-readable media according to claim 2, The time-varying filter mentioned above includes a finite impulse response filter.

8. One or more non-transient computer-readable media according to claim 3, Applying the time-varying filter to the signal further includes applying the filter level in the following manner: S' (n) = alpha(t)*S(n)+(1-alpha(t))*S'(n-1), Where S'(n) is the filtered continuous analyte monitoring signal, S(n) is the signal, alpha(t) is a value less than or equal to 1.0, t is time, and n is the sample number.

9. One or more non-transient computer-readable media according to claim 8, For each filter level in each different time period, the value of alpha(t) decreases relative to the value of the immediately preceding alpha(t) corresponding to the immediately preceding time period, such that alpha(t) continuously decreases as time increases during the analyte monitoring period.

10. One or more non-transient computer-readable media according to claim 1, Applying the time-varying filter to the signal further includes: An exponential moving average is applied to the signal.

11. The one or more non-transient computer-readable media according to claim 1, The time-varying filter is an analog low-pass filter, and applying the time-varying filter further includes: The signal is passed through an analog low-pass filter; as well as For each of the multiple different time periods, the filter level is applied to the signal. For each filter level in each different time period, the order of the low-pass filter increases relative to the order of the immediately preceding low-pass filter corresponding to the immediately preceding time period.

12. One or more non-transient computer-readable media according to claim 1, The multiple low-pass filters mentioned above are connected in series.

13. One or more non-transient computer-readable media comprising computer-executable instructions, which, when executed by at least one processor, perform a method for filtering a signal during an analyte monitoring period, the method comprising: Identify the first time period that occurs during the analyte monitoring cycle. The first time period begins near the start of the analyte monitoring cycle; A first filtering level is applied to the signal during the first time period; Identify a second time period that occurs during the analyte monitoring cycle. The second time period begins after the first time period ends; A second filtering level is applied to the signal during the second time period. Applying the second filtering level to the signal during the second time period includes: Turn on the first switch in the time-varying filter. The first switch is connected in parallel with the first low-pass filter included in the time-varying filter. Opening the first switch causes the first low-pass filter to apply the second filtering level to the signal, and The second filter level is an increased filter level relative to the first filter level; Identify a third time period that occurs during the analyte monitoring cycle. The third time period begins after the second time period ends; A third filtering level is applied to the signal during the third time period. The third filter level is an increase in filter level relative to the first filter level and the second filter level; Identify subsequent time periods that occur after the third time period and during the analyte monitoring cycle. The subsequent time period begins after the end of the third time period; and An increasing filtering level is applied to the signal during the subsequent time period, wherein for each filtering level in each subsequent time period, the filtering level is increased relative to the immediately preceding filtering level corresponding to the immediately preceding time period, thereby providing a continuously increasing filtering level to the signal over the subsequent time periods.

14. One or more non-transient computer-readable media according to claim 13, The second time period begins at least 24 hours after the start of the analyte monitoring cycle.

15. One or more non-transient computer-readable media according to claim 13, Applying an increased level of filtering to the signal during the subsequent time period further includes: Pass the signal through at least one low-pass filter; as well as An increased attenuation level is applied to the stopband of the at least one low-pass filter during the subsequent time period. For each filter level in each subsequent time period, the attenuation level in the stopband of the at least one low-pass filter is increased relative to the immediately preceding attenuation level in the stopband of the at least one low-pass filter corresponding to the immediately preceding time period, thereby providing a continuously increasing attenuation level in the stopband of the at least one low-pass filter over the subsequent time period.

16. One or more non-transient computer-readable media according to claim 13, The signal is the measured current signal, and applying an increased level of filtering to the signal during the subsequent time period further includes: The concentration of the analyte is calculated based on the measured current signal; A continuous analyte monitoring signal is generated based on the analyte concentration; as well as The increased filtering level is applied to the continuous analyte monitoring signal during the subsequent time period that occurs in the analyte monitoring cycle to produce a filtered continuous analyte monitoring signal.

17. One or more non-transient computer-readable media according to claim 16, The application of an increased filtering level to the continuous analyte monitoring signal during the subsequent time period occurring within the analyte monitoring cycle further includes applying the increased filtering level in the following manner: S' (t) = alpha(t)*S(t)+(1-alpha(t))*S'(t-1), Where S'(t) is the filtered continuous analyte monitoring signal, S(t) is the continuous analyte monitoring signal, alpha(t) is a value less than or equal to 1.0, and t is time.

18. One or more non-transient computer-readable media according to claim 17, For each filter level in each subsequent time period, the value of alpha(t) decreases relative to the value of the immediately preceding alpha(t) corresponding to the immediately preceding time period, thereby providing a filter level for the signal that continuously increases as time elapses in the analyte monitoring period.

19. One or more non-transient computer-readable media according to claim 13, The time-varying filter mentioned therein is a first-order low-pass filter during the second time period.

20. One or more non-transient computer-readable media according to claim 19, Applying the third filtering level to the signal during the third time period further includes: In addition to turning on the first switch, turn on the second switch in the time-varying filter. The second switch is connected in parallel with the second low-pass filter included in the time-varying filter, and In addition to turning on the first switch, turning on the second switch causes the first low-pass filter and the second low-pass filter to cooperate in applying the third filtering level to the signal, thereby increasing the filtering level relative to the first filtering level and the second filtering level.

21. One or more non-transient computer-readable media according to claim 20, The time-varying filter mentioned therein is a second-order low-pass filter during the third time period.

22. One or more non-transient computer-readable media according to claim 13, During the first time period, the first filtering level applied to the signal is no filtering.

23. One or more non-transient computer-readable media according to claim 13, The signal is the measured current signal, and applying an increased level of filtering to the signal during the subsequent time period further includes: The increased filtering level is applied to the measured current signal during the analyte monitoring period to produce a filtered measured current signal; The analyte concentration is calculated based on the filtered measured current signal; and A filtered continuous analyte monitoring signal is generated based on the analyte concentration.

24. The one or more non-transient computer-readable media of claim 23, further comprising: The filtered continuous analyte monitoring signal is transmitted to an external device for display.

25. One or more non-transient computer-readable media according to claim 13, Applying the filtering level to the signal includes applying an infinite impulse response filter to the signal.

26. A continuous analyte monitoring system, comprising: Wearable device, the wearable device comprising: A biosensor, wherein the biosensor generates a signal during an analyte monitoring period, the analyte monitoring period including a first time period and multiple subsequent time periods. The plurality of subsequent time periods begin after the first time period ends; A time-varying filter connected to the biosensor; and A processor connected to the biosensor and the time-varying filter; The time-varying filter mentioned above includes: Multiple low-pass filters connected in series; and Multiple switches, wherein each of the multiple switches is connected in parallel with one of the multiple low-pass filters; When executing computer-executable instructions, the processor controls the time-varying filter to apply a filtering level to the signal during the first time period and each of the plurality of subsequent time periods, wherein the processor: At least one of the plurality of switches is turned on during the first time period to apply a first filtering level to the signal from at least one of the plurality of low-pass filters during the first time period; and For each of the plurality of subsequent time periods, at least one additional switch among the plurality of switches is turned on, such that an additional number of low-pass filters among the plurality of low-pass filters apply a filtering level to the signal relative to the immediately preceding number of low-pass filters that applied the filtering level to the signal during the immediately preceding subsequent time period. For each subsequent time period, the filter level increases relative to the immediately preceding filter level corresponding to the immediately preceding time period, thereby providing a continuously increasing filter level for the signal over the plurality of subsequent time periods.

27. The system according to claim 26, The wearable device is attached to the user's skin, and The biosensor mentioned above includes: A working electrode, which contacts the user's tissue fluid when the biosensor is implanted under the user's skin, generates the signal during the analyte monitoring cycle. The signal mentioned therein is the measured current signal.

28. The system according to claim 27, The wearable device further includes: A protective ring surrounds the working electrode and is connected to a protection source. The protective ring reduces stray current interference to the working electrode.

29. The system according to claim 27, The processor, when executing computer-executable instructions, During the analyte monitoring period, the time-varying filter is controlled to apply a filtering level to the measured current signal during each subsequent time period of the first time period and the plurality of subsequent time periods to generate a filtered measured current signal. The concentration of the analyte is calculated based on the filtered measured current signal; as well as A filtered continuous analyte monitoring signal is generated based on the analyte concentration.

30. The system of claim 29, further comprising: An external device including a transceiver and a display, the external device communicating wirelessly with the transceiver included in the wearable device. This includes the transceiver in the wearable device transmitting the filtered continuous analyte monitoring signal to the external device for displaying the filtered continuous analyte monitoring signal on the display.

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