Method for analyte concentration monitoring using harmonic relationships

Abstract

Continuous glucose monitoring (CGM) can involve applying a periodic excitation signal to human interstitial fluid via electrodes of a CGM sensor to drive an oxidation / reduction reaction and measuring the current through the electrodes. In some embodiments, the measured current is sampled and digitized to extract various harmonics of the fundamental frequency of the excitation signal. A set of relationships for each of at least two harmonics is generated from the spectral amplitudes of a set of harmonic pairs, triplets, etc., and the relationship sets are mapped to glucose concentrations, e.g., based on the contents of a harmonic relationship database having existing harmonic relationship sets, and the harmonic relationship sets are mapped to corresponding glucose concentrations. Many other embodiments are provided.

Description

[Technical Field]

[0001] This application claims priority to U.S. Provisional Patent Application No. 62 / 809,039, filed February 22, 2019, entitled "Methods and Apparatus for Analyte Concentration Monitoring Using Harmonic Relationships," which is incorporated herein by reference in its entirety for all purposes.

[0002] TECHNICAL FIELD This application relates generally to determining analyte concentrations in analyte-containing fluids. [Background technology]

[0003] Many applications require the determination of the concentration of an analyte in an analyte-containing fluid. One application in particular is the determination of the glucose concentration in a person's blood. Determining a person's blood glucose level is important for the management and control of diabetes. To that end, blood glucose monitoring (BGM) methods and devices have been developed. However, BGM methods and devices require a blood sample, typically taken by "finger stick," although blood may also be collected from other parts of the body, such as the palm or forearm, and BGM results are not continuous but are a single snapshot of the blood glucose level at the time the blood sample was taken.

[0004] To more closely monitor a person's blood glucose levels and detect changes in blood glucose levels that can occur rapidly in people with diabetes, methods and devices for continuous glucose monitoring (CGM) have been developed. Although CGM systems are called "continuous," measurements are typically not truly continuous and are performed every few minutes. CGM products, which have both implantable and non-implantable components, can be worn for several days, or up to several weeks, before being removed and replaced.

[0005] Existing CGM products provide frequent measurements of a person's blood glucose level without requiring such measurements to involve blood sampling, such as with a finger stick. These existing CGM products may require the occasional finger stick and the use of a background glucose monitoring system to calibrate the CGM system. CGM products may include an inserted sensor portion that is positioned under the skin and an unimplanted processing portion that is adhered to the external surface of the skin, for example, on the abdomen or back of the upper arm. Unlike background glucose monitoring systems that measure glucose concentrations in the blood, CGM systems measure glucose concentrations in interstitial fluid. Summary of the Invention [Problem to be solved by the invention]

[0006] Improved CGM methods and devices are desirable. [Means for solving the problem]

[0007] In one exemplary embodiment, a method for electronically probing an oxidation-reduction reaction in an analyte-containing fluid is provided, the method including the steps of applying, by a first circuit, a periodic excitation signal to the analyte-containing fluid, the periodic excitation signal having a fundamental frequency; generating, by a second circuit, an amperometric signal while the first circuit is applying the periodic excitation signal, the amperometric signal having a magnitude indicative of a current produced by an oxidation-reduction reaction in the analyte-containing fluid, the magnitude being dependent, at least in part, on an analyte concentration in the analyte-containing fluid; sampling, by a third circuit, the amperometric signal; and providing, by the third circuit, digitized time-domain sample data representative of the amperometric signal. The method may include extracting a plurality of harmonic signals based at least in part on the extracted time-domain sample data, where the harmonic signals are harmonics of a fundamental frequency and each harmonic signal has a corresponding intensity; calculating a set of harmonic relationships based at least in part on the plurality of harmonic signals; accessing a harmonic relationship database, where the harmonic relationship database includes a plurality of sets of harmonic relationships, each set of harmonic relationships being associated with a corresponding analyte concentration; and determining a magnitude of the analyte concentration in the analyte-containing fluid based on the harmonic relationship database and the calculated set of harmonic relationships.

[0008] In another exemplary embodiment, a method for generating a set of predetermined harmonic-related information correlating to a corresponding value of an analyte concentration in an analyte-containing fluid includes the steps of: providing a plurality of analyte-containing fluid samples, each of the plurality of analyte-containing fluid samples having a known analyte concentration; for each of the plurality of analyte-containing fluid samples, applying, by a first circuit, a periodic excitation signal to the analyte-containing fluid sample, the periodic excitation signal having a fundamental frequency; and generating, by a second circuit, a amperometric signal while the first circuit is applying the periodic excitation signal, the amperometric signal representing a redox reaction in the analyte-containing fluid while the first circuit is applying the periodic excitation signal. and the magnitude is dependent, at least in part, on the analyte concentration in the analyte-containing fluid; sampling, by a third circuit, the current measurement signal; providing, by the third circuit, digitized time-domain sample data representative of the current measurement signal; extracting a plurality of harmonic signals from the digitized time-domain sample data; calculating a set of harmonic relationships based at least in part on the plurality of harmonic signals; associating the set of harmonic relationships with known analyte concentrations in the analyte-containing fluid; and storing the set of harmonic relationships associated with the known analyte concentrations in a harmonic relationship database.

[0009] In another exemplary embodiment, a continuous analyte monitoring (CAM) system includes a first circuit configured to apply a periodic excitation signal to an analyte-containing fluid; a second circuit configured to generate an amperometric signal, the amperometric signal having a magnitude indicative of a current in the analyte-containing fluid, the magnitude being at least partially dependent on an analyte concentration in the analyte-containing fluid; a third circuit configured to sample the amperometric signal, the third circuit further configured to generate digitized time-domain sample data; and a processor coupled to a memory, the memory having a harmonic relation database stored therein. The method may further include a processor having stored instructions that, when executed by the processor, cause the processor to extract a plurality of harmonic signals from the digitized time-domain sample data, calculate a set of harmonic relationships based at least in part on the plurality of harmonic signals, access a harmonic relationship database, the harmonic relationship database including a plurality of sets of harmonic relationships, each set of harmonic relationships being associated with a corresponding analyte concentration, and determine a magnitude of the analyte concentration in the analyte-containing fluid based on the harmonic relationship database and the calculated set of harmonic relationships.

[0010] In another exemplary embodiment, a continuous glucose monitoring (CGM) system may include a CGM sensor configured for insertion into a region of interstitial fluid of a user; a first electronic circuit configured to couple to the CGM sensor and configured to be removably attached to an exterior surface of the user, the first electronic circuit including a periodic excitation signal generator configured to couple to the CGM sensor, a current sensor configured to couple to the CGM sensor, and a sampling circuit configured to couple to the current sensor, the sampling circuit configured to output sampled time-domain data; and a second electronic circuit coupled to the first electronic circuit, the second electronic circuit configured to extract a predetermined number of harmonics from the sampled time-domain data, generate a set of harmonic relationships based on the extracted harmonics, and determine a blood glucose level based on the set of harmonic relationships.

[0011] In another exemplary embodiment, a method of continuous glucose monitoring (CGM) may include generating, by a periodic excitation signal generator, a periodic excitation signal having an amplitude and a fundamental frequency; applying the periodic excitation signal to electrodes of a CGM sensor; sensing, by a current sensor circuit, a current through the CGM sensor to generate a measured current signal; sampling, by a sampling circuit, the measured current signal at a sampling rate for a period of time with bit resolution to generate a set of time-domain sample data; converting the set of time-domain sample data into a set of frequency-domain data, the set of frequency-domain data including at least the magnitude of each of a predetermined number of harmonics of the fundamental frequency; generating a set of harmonic relationships based on the magnitude of each of the predetermined number of harmonics; and determining a blood glucose level based on the set of harmonic relationships.

[0012] In some embodiments, an analyte monitoring system includes a first circuit configured to apply a periodic excitation signal to an analyte-containing fluid; a second circuit configured to generate a current measurement signal, the current measurement signal having a magnitude indicative of a current in the analyte-containing fluid, the magnitude being at least partially dependent on an analyte concentration in the analyte-containing fluid; a third circuit configured to sample the current measurement signal, the third circuit further configured to generate digitized time-domain sample data; and a processor coupled to a memory, the memory having a stored harmonic relationship database and further having stored instructions that, when executed by the processor, cause the processor to: (a) extract a plurality of harmonic signals from the digitized time-domain sample data; (b) calculate a set of harmonic relationships based at least in part on the plurality of harmonic signals; and (c) determine a magnitude of the analyte concentration in the analyte-containing fluid based on the calculated set of harmonic relationships.

[0013] In some embodiments, the analyte monitoring system includes: (1) a first circuit configured to apply a periodic excitation signal to an analyte-containing fluid, the periodic excitation signal having a fundamental frequency selected based at least in part on an approximate analyte concentration in the analyte-containing fluid; (2) a second circuit configured to generate a amperometric signal, the amperometric signal having a magnitude indicative of a current in the analyte-containing fluid, the magnitude being at least in part dependent on the analyte concentration in the analyte-containing fluid; (3) a third circuit configured to sample the amperometric signal, the third circuit further configured to generate digitized time-domain sample data; and (4) a processor coupled to a memory. and a processor having a memory having a harmonic relationship database stored therein, and further having stored instructions that, when executed by the processor, cause the processor to: (a) extract a plurality of harmonic signals from the digitized time-domain sample data; (b) calculate a set of harmonic relationships based at least in part on the plurality of harmonic signals; (c) access the harmonic relationship database, the harmonic relationship database including a plurality of sets of harmonic relationships, each set of harmonic relationships being associated with a corresponding analyte concentration; and (d) determine a magnitude of the analyte concentration in the analyte-containing fluid based on the harmonic relationship database and the calculated set of harmonic relationships.

[0014] In some embodiments, a method for electronically probing a redox reaction in an analyte-containing fluid includes: (a) determining an approximate analyte concentration in the analyte-containing fluid; (b) determining a frequency of a periodic excitation signal to apply to the analyte-containing fluid based at least in part on the determined approximate analyte concentration; (c) applying the periodic excitation signal to the analyte-containing fluid by a first circuit; and (d) generating, by a second circuit, a amperometric signal while the first circuit is applying the periodic excitation signal, the amperometric signal having a magnitude indicative of a current produced by the redox reaction in the analyte-containing fluid, the magnitude being at least in part dependent on the molecular weight of the molecule in the analyte-containing fluid. (e) sampling the current measurement signal by a third circuit; (f) providing digitized time-domain sample data representing the current measurement signal by the third circuit; (g) extracting a plurality of harmonic signals based at least in part on the digitized time-domain sample data, the harmonic signals being harmonics of a fundamental frequency of the periodic excitation signal, each harmonic signal having a corresponding magnitude; (h) calculating a set of harmonic relationships based at least in part on the plurality of harmonic signals; and (i) determining a magnitude of the analyte concentration in the analyte-containing fluid based on the calculated set of harmonic relationships.

[0015] In some embodiments, an analyte monitoring system includes: (1) a first circuit configured to apply a periodic excitation signal to an analyte-containing fluid, the periodic excitation signal having a fundamental frequency selected based at least in part on an approximate analyte concentration in the analyte-containing fluid; (2) a second circuit configured to generate an amperometric signal, the amperometric signal having a magnitude indicative of a current in the analyte-containing fluid, the magnitude being dependent at least in part on the analyte concentration in the analyte-containing fluid; and (3) a second circuit configured to sample the amperometric signal. (3) a third circuit further configured to generate digitized time-domain sample data; and (4) a processor coupled to the memory, the memory having stored instructions that, when executed by the processor, cause the processor to: (a) extract a plurality of harmonic signals from the digitized time-domain sample data; (b) calculate a set of harmonic relationships based at least in part on the plurality of harmonic signals; and (c) determine a magnitude of an analyte concentration in the analyte-containing fluid based on the calculated set of harmonic relationships.

[0016] Other features, aspects, and advantages of embodiments according to the present disclosure will become more fully apparent from the following detailed description, the appended claims, and the accompanying drawings, which set forth several exemplary embodiments and examples. Various embodiments according to the present disclosure may also be capable of other and different applications, and their several details may be modified in various respects, all without departing from the spirit and scope of the claims. Accordingly, the drawings and descriptions should be regarded as illustrative in nature, and not as restrictive. The drawings are not necessarily drawn to scale. [Brief explanation of the drawings]

[0017] [Figure 1A]1 is a high-level block diagram of a first exemplary embodiment of a CGM system with a harmonic relation database stored in a wearable portion of the CGM system according to the present disclosure. FIG. [Figure 1B] FIG. 1 is a high-level block diagram of a second exemplary embodiment of a CGM system with a harmonic relation database stored in a portable user device portion of the CGM system according to the present disclosure. [Figure 1C] FIG. 1 is a high-level block diagram of a third exemplary embodiment of a CGM system according to the present disclosure. [Figure 2] FIG. 1 is a high-level block diagram of another CGM system, according to an exemplary embodiment of the present disclosure. [Figure 3] FIG. 1 is a high-level block diagram of yet another CGM system according to an exemplary embodiment of the present disclosure. [Figure 4] FIG. 1 illustrates, at a high level, a data structure for mapping harmonic relationships to analyte concentrations, according to an exemplary embodiment of the present disclosure. [Figure 5] FIG. 1 illustrates a high-level view of a harmonic relationship database for mapping harmonic relationships to analyte concentrations, according to an exemplary embodiment of the present disclosure. [Figure 6A] FIG. 10 shows a semi-logarithmic plot of the ratio of the intensity of the second harmonic to the intensity of the third harmonic of the fundamental frequency of a periodic excitation signal applied to three different concentrations of glucose-containing fluid, according to an exemplary embodiment of the present disclosure. [Figure 6B] FIG. 10 shows a semi-logarithmic plot of the ratio of the intensity of the third harmonic to the intensity of the fifth harmonic of the fundamental frequency of a periodic excitation signal applied to three different concentrations of glucose-containing fluids, according to an exemplary embodiment of the present disclosure. [Figure 6C] FIG. 10 shows a semi-logarithmic plot of the ratio of the intensity of the fourth harmonic to the intensity of the third harmonic of the fundamental frequency of a periodic excitation signal applied to three different concentrations of glucose-containing fluids, according to an exemplary embodiment of the present disclosure. [Figure 6D]FIG. 10 shows a semi-logarithmic plot of the ratio of the intensity of the second harmonic to the intensity of the fourth harmonic of the fundamental frequency of a periodic excitation signal applied to three different concentrations of glucose-containing fluids, according to an exemplary embodiment of the present disclosure. [Figure 7A] 1 is a flow diagram of a method for generating a harmonic relational database for a continuous analyte monitoring system, according to an exemplary embodiment of the present disclosure. [Figure 7B] 1 is a flow diagram of a method for generating a harmonic relational database for a continuous analyte monitoring system, according to an exemplary embodiment of the present disclosure. [Figure 8] 1 is a flow diagram of a method for determining an analyte concentration according to an exemplary embodiment of the present disclosure. [Figure 9] 1 is a flow diagram of a method for continuous glucose monitoring according to an exemplary embodiment of the present disclosure. [Figure 10] 1 is a flow diagram of another exemplary method for analyte concentration monitoring, according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0018] CGM systems offer many benefits to people, including, but not limited to, reducing the number of finger sticks and detecting rapid changes in blood glucose levels. However, one of the technical challenges in implementing CGM systems is that measuring glucose in interstitial fluid rather than directly in a person's blood can result in a low signal-to-noise ratio (SNR). A low SNR can make it difficult to process signals obtained from a patient's interstitial fluid.

[0019] In various embodiments provided herein and described in more detail below, a low-frequency periodic excitation signal is injected into a glucose-containing fluid (e.g., to drive glucose oxidation / reduction (redox) reactions in the fluid) to generate nonlinear signal distortions in a corresponding measurement of electrical current in the fluid. Harmonics can be extracted from the complex waveform generated by the nonlinear signal distortion and used to generate a set of harmonic relationships, such as a set of harmonic ratios or other relationships between harmonics, that correlate with the glucose concentration in the fluid. For different glucose concentrations, the set of harmonic relationships has a corresponding set of unique values. That is, the set of harmonic relationships can function as a vector in glucose concentration space, and the vector coordinates (i.e., the evaluated set of harmonic relationships) refer to or define the glucose concentration. More generally, sets of harmonic relationships can be generated for other analyte-containing fluids and employed to define the analyte concentration in such fluids.

[0020] In some embodiments, if multiple analytes are simultaneously present in the analyzed fluid and the dependence of the harmonic relationships differs for different analytes and / or analyte concentrations, the concentrations of multiple analytes can be determined simultaneously. Similarly, one or more interferents can be detected, analyzed, and / or corrected for during the detection of analyte concentrations. As an example, the analyzed fluid may contain glucose and at least one interferent, which can mimic glucose in electrochemical measurements. However, the glucose concentration may affect a different set of harmonic relationships than the interferent concentration. In this way, the harmonic relationships can be used to determine the glucose concentration and the interferent concentration, and / or to determine the glucose concentration while correcting for the presence of the interferent.

[0021] CGM device To perform continuous glucose monitoring with a CGM system, a sensor is inserted into the patient. The inserted sensor provides electrodes that are disposed in the patient's interstitial fluid. An electrical circuit may be coupled to the sensor. The electrical circuit, or a housing or other package containing the electrical circuit, may be attached to the patient's skin and remain attached for several days or longer. Together, the sensor and electrical circuit constitute a wearable portion of the CGM system. Such a wearable portion may have additional electrical circuitry to implement additional features and functions.

[0022] The electrical circuitry can be used to process electrical signals obtained by the sensor from the interstitial fluid. These signals are dependent on the person's blood glucose level. These signals can be obtained automatically, for example, multiple times per day. The electrical circuitry of the wearable portion can be further configured to store, display, and / or communicate information regarding the patient's blood glucose level.

[0023] As described above, BGM systems determine the blood glucose level in a person's blood sample simultaneously with blood sampling, for example, with a finger stick. Much effort has been focused on accelerating BGM measurements to obtain a single glucose measurement for a person as quickly as possible. The current state-of-the-art for determining blood glucose levels with BGM systems is less than five seconds. However, for CGM systems, there may be little advantage to measuring glucose in interstitial fluid on a timescale significantly faster than the inherent time lag of approximately five minutes between glucose in arterial blood and glucose in interstitial fluid. Therefore, a meaningful time constraint for determining blood glucose levels with a CGM system is on the order of one minute. That is, more time is available in CGM systems than in BGM systems, and this extra time can be used to process the lower SNR signal (compared to BGM systems) obtained from interstitial fluid to determine blood glucose levels. Therefore, the embodiments provided herein may compensate for the relatively lower SNR in interstitial fluid compared to arterial blood (e.g., through the use of frequency-domain algorithms that can provide scalable SNR by adding more excitation cycles). Continuous monitoring systems for other types of analytes, interferents, or other substances, such as maltose, galactose, hematocrit, drugs such as acetaminophen, etc., may similarly be employed in accordance with the embodiments described herein.

[0024] 1A illustrates a high-level block diagram of an exemplary CGM system 100 according to embodiments provided herein. While not shown in FIG. 1A , it should be understood that various electronic components and / or circuits are configured to couple to a power source, such as, but not limited to, a battery. The CGM system 100 includes a first circuit 102 that can be configured to couple to a CGM sensor 104. The first circuit 102 can be configured to apply a periodic excitation signal, such as a periodic voltage signal, to an analyte-containing fluid via the CGM sensor 104. In this exemplary embodiment, the analyte-containing fluid can be human interstitial fluid, and the periodic voltage signal can be applied to electrodes 105 of the CGM sensor 104.

[0025] In some embodiments, the CGM sensor 104 may include two electrodes, and a periodic voltage signal may be applied to the pair of electrodes. In such cases, a current may be measured through the CGM sensor 104. In other embodiments, the CGM sensor 104 may include three electrodes, such as a working electrode, a counter electrode, and a reference electrode. In such cases, a periodic voltage signal may be applied between the counter electrode and the reference electrode, and a current may be measured, for example, through the working electrode. The CGM sensor 104 includes chemicals that react with the glucose-containing solution in a reduction-oxidation reaction, which affects the concentration of charge carriers and the time-dependent impedance of the CGM sensor 104. In some embodiments, such as when a three-electrode sensor is used, the first circuit 102 may be configured to include a potentiostat, the output terminal of which forms the output terminal of the first circuit 102.

[0026] The periodic voltage signal generated by the first circuit 102 is a time-varying signal that can be, for example, but not limited to, sinusoidal, square, sawtooth, triangular, or the like, with or without a DC offset. To simplify subsequent signal processing, the periodic signal may be stabilized before being applied to the analyte-containing fluid (e.g., to avoid the introduction of harmonics not generated by the redox reaction). However, in embodiments in which the periodic voltage signal has a fundamental frequency on the order of the sensor's chemical reaction dynamics, e.g., much less than 1 kHz but greater than 0.01 Hz, the generation of such a low-frequency periodic signal can be very "clean" from the start, so the stabilization process or waiting period can be shortened or eliminated. At frequencies much higher than 1 kHz, the sensor's chemical reaction dynamics often do not affect the current response, so a signal stabilization period is not necessary. In some embodiments, the periodic voltage signal generated by the first circuit 102 may include a DC offset voltage.

[0027] In some embodiments, the periodic voltage signal may have a fixed or slowly varying frequency. In other embodiments, the periodic voltage signal may have a frequency that varies between different discrete frequencies (e.g., maintained at each frequency for multiple periods of the periodic voltage signal, such as 10 or more). Exemplary excitation frequencies for the periodic voltage signal range from about 0.1 Hz to 10 Hz, and in some embodiments, from about 0.5 to 2 Hz, although other values ​​may be used. Exemplary peak voltages range from about 0.5 to 500 millivolts, and in some embodiments, up to about 1 volt, although larger or smaller values ​​may be used (e.g., with or without a DC offset).

[0028] The current through the CGM sensor 104 in the analyte-containing fluid in response to the periodic voltage signal is nonlinear and may be communicated from the CGM sensor 104 to a second circuit 106. The second circuit 106 may be configured to generate a current measurement signal having a magnitude indicative of the magnitude of the current communicated from the CGM sensor 104. In some embodiments, the second circuit 106 may include a resistor having a known nominal value and a known nominal accuracy (e.g., in some embodiments, 0.1% to 5%, or even less than 0.1%) through which the current communicated from the CGM sensor 104 is passed. The voltage developed across the resistor of the second circuit 106 represents the magnitude of the current and may be referred to as a current measurement signal.

[0029] The nonlinear components of the amperometric signal depend on the analyte (e.g., glucose and / or other analyte) concentration in the analyte-containing fluid being analyzed. As described further below, according to embodiments provided herein, the nonlinear characteristics of the amperometric signal (which depend on the analyte concentration) can be quantified by extracting higher harmonics of the fundamental frequency of the applied periodic signal. For example, the "strength" of each harmonic of the fundamental frequency, such as amplitude, power, etc., can be extracted for integer multiples of the fundamental frequency f (e.g., harmonics n*f, where n=2, 3, 4, 5, 6, 7, 8, 9, 10, or more). As with specific frequencies, the entire spectrum or a portion of the frequencies of the nonlinear amperometric signal can be employed (e.g., using Fourier, fast Fourier, discrete Fourier, Goertzel, or other transforms).

[0030] The third circuit 108 may be coupled to the second circuit 106 and configured to sample the current measurement signal and generate digitized time-domain sample data representing the current measurement signal. For example, the third circuit 108 may be any suitable, well-known A / D converter circuit configured to receive the analog current measurement signal and convert it into a digital signal having a desired number of bits as an output. The number of bits output by the third circuit 108 may be 16 in some embodiments, although more or fewer bits may be used in other embodiments. In some embodiments, the third circuit 108 may sample the current measurement signal at a sampling rate ranging from approximately 10 samples per second to 1000 samples per second. Faster or slower sampling rates may be used. For example, a sampling rate such as approximately 10 kHz to 100 kHz may be used, and downsampling may be used to further reduce the signal-to-noise ratio.

[0031] The third circuit 108 may sample the current measurement signal at a sampling rate for a period of time called a sampling window. In various embodiments, the sampling window may be between about 10 seconds and 300 seconds. Longer or shorter sampling windows may be used.

[0032] 1A , the processor 110 may be coupled to the third circuit 108, which may further be coupled to the memory 112. In some embodiments, the processor 110 and the third circuit 108 are configured to communicate directly with each other via a wired path. In some embodiments, the wired path between the processor 110 and the third circuit 108 is a serial path, in which one bit of digital data is transferred at a time. In other embodiments, the wired path between the processor 110 and the third circuit 108 is a parallel path, in which two or more bits of digital data are transferred at a time. In yet other embodiments, the coupling between the processor 110 and the third circuit 108 may be via the memory 112. In this configuration, the third circuit 108 writes digital data to the memory 112, and the processor 110 reads digital data from the memory 112.

[0033] The memory 112 may store therein a harmonic relation database 114 (described in more detail below). The memory 112 may also store therein a plurality of instructions. In various embodiments, the processor 110 may be a computational resource such as, but not limited to, a microprocessor, a microcontroller, an embedded microcontroller, a digital signal processor (DSP), a field programmable gate array (FPGA) configured to perform as a microcontroller, or the like.

[0034] In some embodiments, the instructions stored in memory 112 may include instructions that, when executed by processor 110, cause processor 110 to (a) extract a plurality of harmonic signals from the digitized time-domain sample data generated by third circuit 108; (b) calculate or otherwise determine a set of harmonic relationships based at least in part on the plurality of harmonic signals; (c) access harmonic relationship database 114; and (d) determine a magnitude of an analyte concentration in the analyte-containing fluid sensed by CGM sensor 104 based on harmonic relationship database 114 and the determined set of harmonic relationships.

[0035] The memory 112 may be any suitable type of memory, such as, but not limited to, one or more of volatile memory and / or nonvolatile memory. For example, the memory 112 may include a combination of different types of memory, such as volatile memory and nonvolatile memory. The volatile memory may include, but is not limited to, static random access memory (SRAM) or dynamic random access memory (DRAM). The nonvolatile memory may include, but is not limited to, electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory (e.g., a type of EEPROM in either a NOR or NAND configuration, and / or in either a stacked or planar arrangement, and / or in a single-level cell (SLC), multi-level cell (MLC), or combination SLC / MLC arrangement), resistive memory, filament memory, metal oxide memory, phase-change memory (such as a chalcogenide memory), or magnetic memory. The memory 112 may be packaged, for example, as a single chip or multiple chips. In some embodiments, memory 112 may be embedded in an integrated circuit, such as, for example, an application specific integrated circuit (ASIC), along with one or more other circuits.

[0036] As noted above, memory 112 may store instructions that, when executed by processor 110, cause processor 110 to perform various actions specified by one or more of the stored instructions. Memory 112 may further have a portion reserved for one or more “scratchpad” storage areas that may be used for read or write operations by processor 110 in response to execution of one or more of the instructions.

[0037] 1A , the first circuit 102, the CGM sensor 104, the second circuit 106, the third circuit 108, the processor 110, and the memory 112 including the harmonic relation database 114 may be located within a wearable sensor portion 116 of the CGM system 100. In some embodiments, the wearable sensor portion 116 may include a display 117 for displaying information such as glucose concentration information (e.g., without the use of external equipment). The display 117 may be any suitable type of human-perceivable display, such as, but not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, or an organic light-emitting diode (OLED) display.

[0038] 1A , the CGM system 100 further includes a portable user device portion 118. The processor 120 and the display 122 may be disposed within the portable user device portion 118. The display 122 may be coupled to the processor 120. The processor 120 may control the text or images shown by the display 122. The wearable sensor portion 116 and the portable user device portion 118 may be communicatively coupled. In some embodiments, the communicative coupling between the wearable sensor portion 116 and the portable user device portion 118 may be via wireless communication. Such wireless communication may be via any suitable means, including, but not limited to, a standards-based communication protocol such as the Bluetooth® communication protocol. In various embodiments, the wireless communication between the wearable sensor portion 116 and the portable user device portion 118 may alternatively be via near field communication (NFC), radio frequency (RF) communication, infrared (IR) communication, or optical communication. In some embodiments, the wearable sensor portion 116 and the portable user device portion 118 may be connected by one or more wires.

[0039] Display 122 may be any suitable type of human-perceivable display, such as, but not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display, or an organic light emitting diode (OLED) display.

[0040] 1B, an exemplary CGM system 150 similar to the embodiment shown in FIG. 1A is shown, but with a different component partitioning. In CGM system 150, wearable sensor portion 116 includes a first circuit 102 coupled to CGM sensor 104 and a second circuit 106 coupled to CGM sensor 104. Portable user device portion 118 of CGM system 150 includes a third circuit 108 coupled to processor 120 and a display 122 coupled to processor 120. Processor 120 is further coupled to memory 112 having harmonic relation database 114 stored therein. In some embodiments, processor 120 in CGM system 150 may also perform the functions described above, for example, performed by processor 110 of CGM system 100 of FIG. 1A. The wearable sensor portion 116 of the CGM system 150 may be smaller, lighter, and therefore less invasive than the wearable sensor portion 116 of the CGM system 100 of FIG. 1A because it does not include the third circuit 108, processor 110, memory 112, etc.

[0041] 1C , an exemplary CGM system 170 similar to the embodiment shown in FIG. 1B is shown, but with a different component partitioning. In CGM system 170, wearable sensor portion 116 includes a first circuit 102 coupled to CGM sensor 104, a second circuit 106 coupled to CGM sensor 104, and a third circuit 108 coupled to second circuit 106. Portable user device portion 118 of CGM system 170 includes processor 120 and a display 122 coupled to processor 120. Processor 120 is further coupled to memory 112 having harmonic relation database 114 stored therein. Because processor 110, memory 112, etc. are not included therein, wearable sensor portion 116 of CGM system 170 may be smaller, lighter, and therefore less invasive than wearable sensor portion 116 of CGM system 100 of FIG. 1A.

[0042] 2, an example CGM system 200 provided herein includes a wearable portion 202 (shown within a dashed box) and a portable user device portion 204 (shown within a dashed box). The wearable portion 202 includes a periodic signal generator 206 coupled to electrodes 207 of a CGM sensor 208, one of which may be, for example, a working electrode 209. The electrodes 207 of the CGM sensor 208 are shown within an analyte-containing fluid 210. In some embodiments, the analyte-containing fluid is human interstitial fluid and the analyte is glucose. The electrodes 207 of the CGM sensor 208 are further coupled to a current measurement circuit 212 (the signal generator 206 and the current measurement circuit 212 may be similar to the first circuit 102 and the second circuit 106 of FIGS. 1A, 1B, and 1C, respectively).

[0043] The current measurement circuit 212 receives from the electrodes 207 a current produced by the redox reaction in the analyte-containing fluid 210, the current being responsive to a voltage applied to the electrodes 207 by the signal generator 206. The current measurement circuit 212 generates a current measurement signal, the magnitude of which is responsive to the voltage applied to the electrodes 207 by the signal generator 206. The current measurement circuit 212 is coupled to the sampling circuit 214.

[0044] A sampling circuit 214, similar to the third circuit 108 in FIGS. 1A, 1B, and 1C, is configured to receive as an input the current measurement signal generated by the current measurement circuit 212. The sampling circuit 214 may be configured to sample the current measurement signal and generate digitized time-domain sample data representing the current measurement signal. In some embodiments, the sampling circuit 214 may be an A / D converter with any suitable bit resolution. In some embodiments, the sampling circuit 214 may have a bit resolution of 16 bits. More or fewer bits may be used. The sampling circuit 214 may sample the current measurement signal at a sampling rate ranging, for example, from about 10 samples per second to 1000 samples per second. Faster or slower sampling rates may be used. For example, a sampling rate such as about 10 kHz to 100 kHz may be used, and downsampling may be used to further reduce the signal-to-noise ratio. Furthermore, the sampling circuit 214 may sample the current measurement signal at the sampling rate for a period referred to as a sampling window. In various embodiments, the sampling window may be between about 10 seconds and 300 seconds. Other sampling rates and / or sampling windows may be used.

[0045] 2, the sampling circuit 214 is coupled to a memory 216. In some embodiments, the sampling circuit 214 may be configured to write digitized time-domain sample data representing the current measurement signal to the memory 216.

[0046] The memory 216 may be any suitable type of memory, such as, but not limited to, volatile or non-volatile memory, as described above with reference to Figures 1A, 1B, and 1C. For example, the memory 216 may include a combination of different types of memory, such as volatile and non-volatile memory.

[0047] The memory 216 may have a plurality of instructions stored therein. The memory 216 of the wearable portion 202 is further coupled to a transceiver 218 coupled to an antenna 220. The memory 216 is further coupled to a microcontroller 222. The plurality of instructions stored in the memory 216, when executed by the microcontroller 222, cause the microcontroller 222 to perform various actions specified by one or more of the plurality of stored instructions. In some embodiments, the memory 216 may have a harmonic relation database (HRD) 223 stored therein.

[0048] Continuing to refer to FIG. 2, the microcontroller 222 may be a stand-alone microcontroller, an embedded microcontroller, a digital signal processor (DSP), a field programmable gate array (FPGA) configured to perform as a microcontroller, or the like.

[0049] In the exemplary CGM system 200, the transceiver 218 may be a wireless transmitter / receiver configured to read information from the memory 216 and transmit that data to the portable user device portion 204 via the transceiver 224 and its antenna 226. In some embodiments, the transceiver 224 may write the information received from the transceiver 218 to a memory 228 located in the portable user device portion 204. In alternative embodiments described below, one or more other electrical components may facilitate the transfer of data between the sampling circuit 214 and the memory 216. In various embodiments, wireless communication between the transceivers 218, 224 may be via Bluetooth® communication, near field communication (NFC), radio frequency (RF) communication, infrared (IR) communication, optical communication, etc. In some embodiments, the wearable sensor portion 202 and the portable user device portion 204 may be connected by one or more wires.

[0050] The memory 228 is further coupled to the microprocessor 230. The memory 228 may be any suitable type of memory, such as, but not limited to, volatile and / or non-volatile memory, as described above with reference to Figures 1A, 1B, and 1C.

[0051] Microprocessor 230 may be a computational resource implemented in any suitable manner, for example, but not limited to, a standalone chip, multiple logically coupled chips, an FPGA configured to perform the functions of a microprocessor, an embedded processor, a digital signal processor, or the like.

[0052] The microprocessor 230 may further be coupled to a display 232. The display 232 may be any suitable display. For example, the display 232 may be a human-perceivable display such as, but not limited to, an LCD, or an LED, or an OLED display.

[0053] In some embodiments, the wearable sensor portion 202 may include a display 233 similar to the display 117 of FIG. 1A for displaying information such as glucose concentration information (e.g., without the use of external equipment). The display 233 may be any suitable type of human-perceivable display, such as, but not limited to, an LCD, or LED, or OLED display.

[0054] In some alternative embodiments, data from the sampling circuit 214 may not be written directly to memory 216, but may be read from the sampling circuit 214 by the microcontroller 222 and then written to memory 216 (or the portable user device portion 204) by the microcontroller 222. This data path is indicated by the dashed arrows 234a, 234b in FIG.

[0055] 1A, 1B, 1C, and 2 illustrate two-piece CGM systems having a wearable portion and a portable user device portion, respectively. FIG. 3 illustrates an example one-piece CGM system 300 that includes the functionality of the aforementioned wearable and portable user portions. Specifically, the example one-piece CGM system 300 is configured to apply a periodic excitation voltage to an analyte-containing fluid, generate a current measurement signal representative of the resulting current, extract harmonics based on the current measurement signal, generate a set of harmonic relationships from the extracted harmonics, and / or determine a magnitude of the analyte concentration in the analyte-containing fluid based at least in part on the generated set of harmonic relationships. In some embodiments, the one-piece CGM system 300 may be further configured to wirelessly transmit the determined analyte concentration to one or more receivers physically separated from the CGM system 300.

[0056] As shown in FIG. 3, an exemplary CGM system 300 (shown within a dashed box) includes a periodic signal generator 206 coupled to electrodes 207 of a CGM sensor 208. FIG. 3 shows the electrodes 207 of the CGM sensor 208 disposed in an analyte-containing fluid 210. A current measurement circuit 212 is coupled to the electrodes 207 of the CGM sensor 208 and is further coupled to a sampling circuit 214, which is coupled to a memory 302. A transceiver 304 and its corresponding antenna 306 may also be included in the CGM system 300. The transceiver 304 is coupled to the memory 302. The memory 302 is further coupled to a microprocessor 308. The memory 302 may be similar to memories 216 and / or 228 of FIG. 2 and may include, for example, a harmonic relational database (HRD) 223. As described above, the signal generator 206, current measurement circuit 212, sampling circuit 214, memory 302, harmonic relationship data (HRD) 223, and microprocessor 308 may be employed to determine the analyte concentration in the analyte-containing fluid 210. The transceiver 304 and antenna 306 may then wirelessly transmit the determined analyte concentration to one or more receivers physically separated from the CGM system 300. In some embodiments, the CGM system 300 may include a display 310 for displaying the analyte and / or other information (e.g., the analyte concentration). For example, the display 310 may be an LCD, or an LED or OLED display, or another suitable display.

[0057] 4 and 5 illustrate data structures for mapping a set of harmonic relationships to corresponding analyte concentration values ​​according to embodiments provided herein. FIG. 4 illustrates the relationship between harmonic ratios and analyte concentrations at a high level of abstraction. FIG. 5 illustrates the relationship between harmonic ratios and analyte concentrations in further detail. While FIGS. 4 and 5 are described in terms of harmonic ratios, it will be understood that other harmonic relationships may be used, including, for example, multiplication, addition, or subtraction of properties (e.g., intensities) of two or more harmonics; trigonometric, logarithmic, exponential, or polynomial functions based on properties (e.g., intensities) of two or more harmonics; combinations of the above; and the like.

[0058] 4 shows a simplified high-level block diagram of an exemplary data structure 400 useful for determining an analyte concentration of an analyte-containing fluid based at least in part on one or more harmonic ratios. In some embodiments, harmonic relational database 114 and / or 223 may include one or more data structures similar to database structure 400. As shown below with reference to FIG. 5, several harmonic ratios may generally be used to determine an analyte concentration.

[0059] The data structure 400 may be, for example, a table (e.g., a look-up table) having a predetermined number of rows and columns. In the example data structure 400 shown in FIG. 4, there are six rows, 402, 404, 406, 408, 410, and 412, and two columns, 414 and 416. In the example data structure 400, each row includes a first entry that includes a range designation for each of at least one set of harmonic ratios. For example, X represents a set of harmonic ratios that can be associated with a particular analyte concentration or range of analyte concentrations. In the example shown in FIG. 4, X in row 402 is characterized as being greater than or equal to 0 and less than or equal to 1, which is associated with an analyte concentration of 1. Similarly, X in row 404 is characterized as being greater than 1 and less than or equal to 2, which is associated with an analyte concentration of 2. X in row 406 is characterized as being greater than 2 and less than or equal to 3, which is associated with an analyte concentration of 3. X in row 408 is characterized as being greater than 3 and less than or equal to 4, which is associated with an analyte concentration of 4. X in row 410 is characterized as being greater than 4 and less than or equal to 5, which is associated with an analyte concentration of 5. Similarly, X in row 412 is characterized as being greater than 5, which is associated with an analyte concentration of 6. The example values ​​provided in example data structure 400 are merely to illustrate the use of a set of harmonic ratios to map to corresponding analyte concentrations. Various embodiments may have more or fewer entries in such a data structure.

[0060] 5 shows a high-level block diagram of an exemplary harmonic relationship database 500 useful for determining analyte concentrations of analyte-containing fluids based at least in part on one or more harmonic ratios. Examples of analytes include glucose, maltose, galactose, hematocrit, drugs such as acetaminophen, etc. In some embodiments, the harmonic relationship databases 114 and / or 223 of FIGS. 1A-3 may be similarly configured.

[0061] As described above, providing a periodic excitation, such as an AC excitation signal, to an analyte-containing fluid can generate a time-domain current signal characteristic of the concentration of an analyte in the analyte-containing fluid. Harmonics of the fundamental frequency of the excitation signal can be extracted from the time-domain current signal. Harmonic relationships between multiple harmonics, such as a set of ratios of the intensities of various pairs of extracted harmonics, referred to herein as harmonic ratios, can be determined. Harmonic ratios or other harmonic relationships, such as those in the time-domain current signal, are characteristic of the analyte concentration. A harmonic ratio is the quotient of the intensity of a first harmonic divided by the intensity of a second harmonic. Intensities may include, for example, amplitude, power, etc. Harmonic ratios can be generated from any pair of harmonic intensity values ​​extracted from the time-domain sample data. Thus, the analyte concentration can be represented by a corresponding set of harmonic ratios. By determining a characteristic set of harmonic ratios for particular (predetermined) analyte concentrations and storing those characteristic sets in a database along with their corresponding predetermined analyte concentrations, subsequent generated harmonic ratio sets obtained from analyte-containing fluids having unknown analyte concentrations can be compared to the database of harmonic ratio sets and matched with corresponding analyte concentration values. As previously mentioned, other harmonic relationships may be used in addition to or instead of harmonic ratios.

[0062] fundamental frequency f in If , the harmonics are f n =n*f in where n is the (integer) order of the harmonic. From the harmonic intensities, different attributes can be formed, and these attributes can be optimized to correlate to the concentration of glucose, glucose interferents, and / or sensor temperature. Example attributes can include: (1) harmonic intensity; (2) the ratio of harmonic intensity to fundamental frequency intensity (e.g., to normalize hardware calibration of the current sensing electronics); (3) the ratio of two or more harmonic intensities, which can reduce calibration coefficients in the current sensing electronics; and / or (4) any other functional relationship involving two or more harmonic intensities.

[0063] The advantage of harmonic ratios is that they generate several different attributes. If N is the maximum number of measurable harmonics, there are N(N-1) / 2 different attributes that can be included in correlation functions with glucose concentration, temperature, and / or glucose interferents. For example, with N=15 harmonics, there are 105 different attributes from harmonic ratios alone that can be used to construct more complex functions, such as index functions.

[0064] More generally, harmonics and their derived attributes (e.g., amplitude, power, etc.) may be used in a wide range of machine learning algorithms, including but not limited to multivariate regression, neural networks, Newton-Raphson methods, or conjugate gradient optimization to analyze analyte concentrations or other properties.

[0065] 5, an exemplary harmonic relation database 500 includes n sets of harmonic ratios, each set having a link or pointer to a corresponding analyte concentration. Each set of harmonic ratios is an n-tuple and can be thought of as a vector in analyte concentration space. In the exemplary harmonic relation database 500, each of the n vectors is m-dimensional, with the value of each of the m dimensions being set by the ratio value of a pair of harmonic intensities. In general, any number of harmonic ratios may be used.

[0066] Here, n vectors are shown as first vector 502, second vector 506, third vector 510, and nth vector 514. Each vector 502, 506, 510, and 514 contains links or pointers 503, 507, 511, and 515 to m harmonic ratios and corresponding analyte concentration values ​​504, 508, 512, and 516, respectively.

[0067] As an example, the harmonic ratio h of the analyte-containing fluid a1b1 , h a2b2 , h a3b3 ...h ambm By determining the harmonic ratio h a1b1 , h a2b2 , ha3b3 ...h ambm can be compared with each harmonic ratio set in the harmonic relation database 500. For example, the harmonic ratio h a1b1 , h a2b2 , h a3b3 ...h ambm is the harmonic ratio h of the analyte-containing sample a1b1 -1, h a2b2 -1, h a3b3 -1...h ambm -1, the sample may be determined to have an analyte concentration value of Conc. -1. a1b1 , h a2b2 , h a3b3 ...h ambm is the harmonic ratio h of the analyte-containing sample a1b1 -2, h a2b2 -2, h a3b3 -2...h ambm -2, then the sample has an analyte concentration value of Conc.-2, and so on. The number m of harmonic ratios per vector n can be any suitable value (e.g., 2, 3, 4, 5, 10, 15, etc., up to the size of the complete set of harmonic ratios). Any number of vectors n can be used.

[0068] In some embodiments, harmonic ratios and / or other harmonic relationships can be correlated with glucose concentrations between experimentally measured data sets using numerical approximations such as multivariate polynomials, multidimensional interpolation such as using cubic splines.

[0069] 6A-6D show semi-logarithmic plots of the ratios of the intensities of various harmonics of the fundamental frequency of a periodic voltage signal applied to a glucose-containing fluid at three different concentrations, according to an exemplary embodiment of the present disclosure. Referring to Figures 6A, 6B, 6C, and 6D, various exemplary harmonic ratio curves are shown for the ratio of the intensity of the second harmonic to the intensity of the third harmonic (h23), the ratio of the intensity of the third harmonic to the intensity of the fifth harmonic (h35), the ratio of the intensity of the fourth harmonic to the intensity of the fifth harmonic (h45), and the ratio of the intensity of the second harmonic to the intensity of the fourth harmonic (h24), respectively, for various glucose concentrations in the sample (e.g., 50, 100, and 300 milligrams per deciliter (mg / dl)). The intensities of the harmonics are determined by extracting the harmonics from the digitized time-domain current measurement signal (e.g., from the third circuit 108 of FIG. 1A, FIG. 1B, or FIG. 1C, or from the sampling circuit 214 of FIG. 2 or FIG. 3) and identifying the intensities (e.g., amplitude, power, etc.) of these spectral components of the digitized time-domain current measurement signal. For example, a Fourier transform, a discrete Fourier transform, a fast Fourier transform, a Goertzel transform, etc. may be used to extract the spectral components. FIGS. 6A-6D show that for any particular glucose concentration, there is a unique set of harmonic ratio values. Thus, a given set of harmonic ratio values ​​maps to a unique corresponding glucose concentration.

[0070] 7A-7B show a flow diagram of a method 700 for generating a harmonic relationship database for a continuous analyte monitoring system according to an exemplary embodiment of the present disclosure. For example, harmonic ratios and / or other harmonic relationships may be used. Referring to FIG. 7A, the exemplary method 700 provides a method for generating a set of predetermined harmonic relationship information that correlates to corresponding values ​​of analyte concentration in an analyte-containing fluid. In this manner, for example, a harmonic relationship database may be generated that can be used to determine blood glucose levels in human interstitial fluid in a CGM product according to the present disclosure. The method 700 may include step 702 of providing a plurality of analyte-containing fluid samples, each of the plurality of analyte-containing fluid samples having a known analyte concentration. To generate a harmonic relationship database for a glucose-containing fluid, the fluid samples contain a known concentration of glucose. In an alternative embodiment, a harmonic relationship database for a different analyte may be generated by using a fluid sample containing a known concentration of the analyte. Because method 700 can be used to generate a set of harmonic-related information for each of a plurality of analyte-containing fluid samples, a decision operation 704 is performed to determine whether all of the analyte-containing fluid samples have been processed. If all of the fluid samples have been processed, method 700 ends. On the other hand, if there are more fluid samples to be processed, method 700 further includes, for each of the remaining plurality of analyte-containing fluid samples, applying a periodic excitation (e.g., voltage) signal (having a fundamental frequency) to the analyte-containing fluid sample by a first circuit, such as first circuit 102 of FIG. 1A, FIG. 1B, or FIG. 1C, or signal generator 206 of FIG. 2 or FIG. 3. The method 700 also includes generating 708 a current measurement signal by a second circuit while the first circuit applies the periodic voltage signal, the current measurement signal having a magnitude indicative of a current produced by an oxidation-reduction reaction in the analyte-containing fluid sample while the first circuit applies the periodic voltage signal, the magnitude of the current measurement signal being at least partially dependent on an analyte concentration in the analyte-containing fluid sample. The second circuit may be, for example, second circuit 106 of FIG. 1A, FIG. 1B, or FIG. 1C. , or current measurement circuit 212 of Figure 2 or 3. Method 700 further includes sampling 710, by a third circuit, the current measurement signal, and providing 712, by the third circuit, digitized time-domain sample data representative of the current measurement signal. The third circuit may be, for example, third circuit 108 of Figure 1A, 1B, or 1C, or sampling circuit 214 of Figure 2 or 3.

[0071] The current measurement signal is a complex, time-varying signal that can be represented, for example, by the sum of a series of signals, each with its own frequency, amplitude, and phase characteristics, using a Fourier transform from the time domain to the frequency domain. The spectral content of the complex, time-varying signal can be extracted, for example, by converting the time-domain signal to a frequency-domain signal and identifying the frequencies of the spectral peaks. Any suitable transform can be used, such as a Fourier transform, a discrete Fourier transform, a fast Fourier transform, or a Goertzel transform. A harmonic signal is a signal that has a frequency that is an integer multiple of the fundamental frequency, which in this example is the frequency of the periodic voltage signal applied to the analyte-containing fluid sample.

[0072] Referring to FIG. 7B, once the digitized time-domain sample data is available, method 700 continues with step 714 of extracting a plurality of harmonic signal strengths from the digitized time-domain sample data and step 716 of calculating a set of harmonic relationships, such as harmonic ratios, based at least in part on the plurality of harmonic signals.

[0073] Each extracted harmonic signal component has a value representing the intensity, e.g., amplitude, power, etc., of the corresponding harmonic frequency. According to some embodiments of method 700, the determined harmonic relationship may be a harmonic ratio (e.g., the ratio of the intensities of at least two harmonic signals), which is the quotient of the intensities of at least two harmonic signals. By way of example and not limitation, the harmonic ratio may be the ratio of the intensities of the third harmonic of the fundamental frequency to the fifth harmonic of the fundamental frequency. As noted, other functional relationships between harmonic signals and / or harmonic signal intensities may be used in addition to or instead of a harmonic ratio. In various embodiments according to the present disclosure, multiple harmonic relationships, such as the ratio of the intensities of multiple harmonics, may be used to identify the concentration of at least one analyte in an analyte-containing fluid.

[0074] 1A-3 may perform and / or assist in harmonic extraction and / or harmonic relationship calculations, for example, using one or more time domain to frequency domain transforms (e.g., forming digitized frequency domain data). Method 700 continues by associating 718 a set of harmonic relationships with known analyte concentrations of the analyte-containing fluid and storing 720 the set of harmonic relationships associated with the known analyte concentrations in a harmonic relationship database. For example, a harmonic relationship database similar to harmonic relationship database 500 of FIG. 5 could be developed.

[0075] FIG. 8 shows a flow diagram of a method 800 for determining an analyte concentration according to an exemplary embodiment of the present disclosure. Referring to FIG. 8, the exemplary method 800 provides for electronically probing an oxidation-reduction reaction in an analyte-containing fluid to determine the concentration of at least one analyte in the fluid. The method 800 includes applying a periodic excitation (e.g., voltage) signal (having a fundamental frequency) to the analyte-containing fluid by the first circuit 102 of FIG. 1A, FIG. 1B, or FIG. 1C or the signal generator 206 of FIG. 2 or FIG. 3 in step 802; and generating, by a second circuit, a current measurement signal having a magnitude indicative of a current generated by the oxidation-reduction reaction in the analyte-containing fluid in step 804, the magnitude of the current measurement signal being at least partially dependent on the analyte concentration in the analyte-containing fluid while the first circuit applies the periodic voltage signal. The second circuit may be, for example, the second circuit 106 of FIG. 1A, FIG. 1B, or FIG. 1C or the current measurement circuit 212 of FIG. 2 or FIG. 3. In one embodiment, the analyte may be glucose, and in alternative embodiments, one or more other analytes may be in the analyte-containing fluid. Method 800 further includes sampling 806 the amperometric signal by a third circuit and providing 808 digitized time-domain sample data representative of the amperometric signal by the third circuit. In some embodiments, the sampling circuit may be an A / D converter having an appropriate bit resolution. The third circuit may be, for example, third circuit 108 of FIG. 1A, FIG. 1B, or FIG. 1C, or sampling circuit 214 of FIG. 2 or FIG. 3.

[0076] Once digitized time-domain sample data representing the current measurement signal is available, method 800 continues with step 810 of extracting a plurality of harmonic signals based at least in part on the digitized time-domain sample data, where the harmonic signals are harmonics of a fundamental frequency and each harmonic signal has a corresponding intensity. Method 800 continues with step 812 of calculating a set of harmonic relationships of at least two harmonic intensities, such as a harmonic ratio, based at least in part on the extracted plurality of harmonic signals, and step 814 of accessing a harmonic relationship database, where the harmonic relationship database includes a set of a plurality of harmonic relationships (see FIG. 5 ), each set of harmonic relationships being associated with a corresponding analyte concentration. Method 800 further includes step 816 of determining the analyte concentration in the analyte-containing fluid based on the harmonic relationship database and the calculated set of harmonic relationships. In some embodiments, determining the analyte concentration includes comparing the set of calculated harmonic relationships to one or more vectors in a harmonic relationship database (e.g., harmonic relationship database 500 of FIG. 5), and if a match is found between the set of calculated harmonic relationships and a vector in the harmonic relationship database, following a link or pointer associated with the matching vector to an associated analyte concentration value. In some embodiments, the processor and / or microcontroller 110, 120, 222, 230, and / or 308 of FIGS. 1A-3 may perform and / or assist in the harmonic extraction, harmonic relationship calculation, and / or analyte concentration determination by comparing the set of calculated harmonic relationships to one or more vectors in the harmonic relationship database.

[0077] In some embodiments, harmonic ratios and / or other harmonic relationships can be correlated with analyte concentrations between experimentally measured data sets using numerical approximations such as multivariate polynomials, multidimensional interpolation such as using cubic splines, etc. Additionally, in some embodiments, harmonics and their derived attributes (e.g., amplitude, power, etc.) can be used in a wide range of machine learning algorithms, including but not limited to multivariate regression, neural networks, Newton-Raphson, or conjugate gradient optimization to analyze analyte concentrations or other properties.

[0078] FIG. 9 shows a flow diagram of a method 900 of continuous glucose monitoring according to an exemplary embodiment of the present disclosure. Referring to FIG. 9 , after a CGM sensor is inserted under a person's skin and a wearable portion of the CGM system is attached to the person's skin, method 900 provides step 902 of generating a periodic signal having an amplitude and a fundamental frequency with a periodic excitation signal (e.g., an AC excitation signal, a periodic voltage signal, etc.) generator and step 904 of applying the excitation signal to electrodes of the CGM sensor. Method 900 continues with step 906 of sensing current through the CGM sensor with a current sensor circuit to generate a measured current signal. The measured current signal depends at least in part on the excitation signal and at least in part on the glucose concentration in the person's interstitial fluid. Method 900 continues with step 908 of sampling the measured current signal with a sampling circuit at a sampling rate, for a period of time, and with bit resolution to generate a set of time-domain sample data. In various embodiments, the sampling rate is, for example, but not limited to, 50 times, 100 times, or even 200 times or even 400 times greater than the fundamental frequency of the excitation signal. Method 900 continues by converting the set of time-domain sample data into a set of frequency-domain data, the set of frequency-domain data including the magnitude of each of a predetermined number of harmonics of the fundamental frequency. Method 900 further includes step 912 of generating a set of harmonic relationships based on the magnitude of each of the predetermined number of harmonics. Method 900 further includes step 914 of determining a blood glucose level (e.g., concentration) based on the set of harmonic relationships. As described above, in some embodiments, the set of harmonic relationships can be mapped to a concentration of glucose in a harmonic relationship database. Method 900 can be performed, for example, by one or more of CGM systems 100, 150, 170, 200, or 300.

[0079] In some embodiments, the frequency of the periodic excitation signal applied to the analyte-containing fluid may be selected based, at least in part, on the approximate concentration of the analyte in the analyte-containing fluid. For example, the approximate analyte concentration may be an expected analyte concentration (e.g., based on typical analyte concentrations observed for the patient, previous concentration levels measured for the patient, the time of day, the time and / or what the patient last ate, etc.). In some embodiments, a simple DC or other analyte concentration measurement may be performed to determine the approximate analyte concentration.

[0080] The fundamental frequency of the periodic excitation signal may be determined based on the approximate concentration of the analyte in the analyte-containing fluid. For example, in the case of glucose, if the approximate analyte (e.g., glucose) concentration indicates that an intermediate glucose concentration is present in the analyzed fluid, a frequency of approximately 0.5 to 1.5 Hz may be used for the periodic excitation signal. As another example, if the approximate analyte (e.g., glucose) concentration indicates that a low glucose concentration is present in the analyzed fluid, a frequency of approximately 4.5 to 5.5 Hz may be used for the periodic excitation signal. Other frequencies may be used for these and / or other analyte concentrations. In some embodiments, basing the selection of the frequency of the periodic excitation signal, at least in part, on the approximate analyte concentration may provide a more accurate frequency-domain-based determination of the analyte concentration.

[0081] FIG. 10 shows a flow diagram of a method 1000 of analyte concentration monitoring according to an exemplary embodiment of the present disclosure. Referring to FIG. 10 , to determine the analyte concentration in an analyte-containing fluid, the method 1000 provides step 1002 of determining an approximate analyte concentration of the analyte-containing fluid. As described above, this may include employing an expected analyte concentration, employing a simple DC analyte concentration measurement, etc., to obtain an estimated or approximate analyte concentration level. The method 1000 then includes step 1004 of determining a frequency of a periodic excitation (e.g., voltage) signal to apply to the analyte-containing fluid based on the approximate analyte concentration. For example, in some embodiments, a higher frequency may be used for low analyte concentrations than for medium or high analyte concentrations. The method 1000 then includes step 1006 of applying a periodic signal to the analyte-containing fluid and step 1008 of generating a current measurement signal. The measured current signal depends at least in part on the excitation signal and at least in part on the analyte concentration in the analyte-containing fluid. Method 1000 continues with step 1010 of sampling the measured current signal to generate a set of time-domain sample data (1012). In various embodiments, the sampling rate is, for example, but not limited to, 50 times, 100 times, even 200 times, or even 400 times greater than the fundamental frequency of the excitation signal. Method 1000 continues with step 1014 of extracting a plurality of harmonic signals based at least in part on the digitized time-domain sample data and step 1016 of calculating a set of harmonic relationships based at least in part on the plurality of harmonic signals. For example, harmonic ratios and / or other combinations of two or more harmonic intensities may be employed to form the set of harmonic relationships. Method 1000 then includes step 1018 of determining an analyte concentration of the analyte-containing fluid based on the set of harmonic relationships. In some embodiments, a harmonic relationship database, multivariate polynomials, multidimensional interpolation such as using cubic splines, or the like may be used to determine the analyte concentration. The method 1000 may be implemented, for example, in a monitoring system 100, 150, 170, 200, or 300. 00.

[0082] As noted, the methods and systems described herein may be employed to determine the concentrations of analytes other than glucose, such as maltose, galactose, hematocrit, interferents of such analytes, and drugs, such as acetaminophen.

[0083] One of the main challenges in continuous glucose monitoring is the calibration of sensors in interstitial fluid. Using a ratio or other relationship between at least two harmonic intensities as an algorithm attribute may be less dependent on calibration than using absolute current measurements. Compared to DC measurements, which generate a single data point per measurement, the advantage of algorithms with basis function sets built on harmonic relationships is that they can extract many harmonics (typically in the range of 10–20 or more). The ratio or other functional relationship between all these harmonics can represent hundreds of independent, simultaneous measurements. Harmonics can be extracted from any time range that is an integer multiple of the fundamental period of the excitation pulse. Because the use of harmonic relationships is largely calibration-independent, the bit resolution of the A / D converter stage can be relaxed, potentially reducing component cost and analog front-end complexity. Harmonic-based algorithms are scalable, enabling optimal deployment across a variety of CPU platforms, from efficient Goertzel transforms in small microcontrollers to full-scale, real-time Fourier analysis for high-performance processors.

[0084] As described herein, the nonlinear component in the current response depends on the glucose concentration in a repeatable manner. One way to quantify the nonlinear contribution is to extract the higher harmonics of the fundamental frequency. [1] f n =n*f in , where n is the integer harmonic order.

[0085] Embodiments described herein may include extracting amplitude or power components of several harmonic orders in the spectrum of the current through the working electrode (e.g., including harmonics above n=2 through n=10). Any suitable numerical method for extracting the entire spectrum or only the contributions of specific frequencies may be used, including, but not limited to, the discrete Fourier transform, the fast Fourier transform, or the Goertzel transform. For example, a discrete Fourier transform based on equation [1] is:

[0086]

number

[0087] where timebins is the sampling interval, [3] k / timebins=nf in It is t.

[0088] From equation [3], the harmonic frequency f n The Fourier transform S from equation [2] is extracted in k is the harmonic intensity h n From the harmonic intensities, different attributes can be generated, and these attributes can be optimized to correlate with the concentration of glucose, glucose interferents, and / or sensor temperature, and the correlations can be explained through the simplest functional relationships. The attributes can be, for example, n , the ratio of harmonic intensity to fundamental wave: h n1 =h n / h1, the ratio of the two harmonic intensities: h nm =h n / h m , and / or any other functional relationship involving two or more harmonic intensities. The ratio of harmonic intensity to fundamental intensity, h n1 =h nWith respect to / h1, the advantage of this technique is that it effectively normalizes the hardware calibration of the current-sensing electronics. For example, if the DC offset is removed (e.g., if the harmonic extraction is configured as a properly tuned digital filter), absolute calibration of the current-sensing electronics may not be necessary. The ratio of the two harmonic intensities: h nm =h n / h m With respect to , this may have similar advantages, such as eliminating calibration coefficients in the current-sensing electronics. Furthermore, the harmonic ratios generate a number of N(N-1) / 2 different attributes that can be included in correlation functions with glucose, temperature, or glucose interferents, where N is the maximum number of measurable harmonics. With N=15, 105 different attributes can be used to construct more complex functions, such as index functions, from simple harmonic ratios alone.

[0089] The embodiments disclosed herein can provide continuous measurement of the concentration of a reagent in an aqueous solution by monitoring the ratio of harmonics in the current flowing through the working electrode of a sensor. Continuous monitoring of multiple current harmonics allows for self-calibration of the signal in a CGM (e.g., eliminating the need for DC current measurements and frequent calibration). Attributes extracted using harmonics can eliminate the absolute current scale, reducing the demands (and costs) on sensor electronics and calibration. Attributes are acquisition-independent and can be easily analyzed using any suitable algorithm.

[0090] The above embodiments may be implemented as a continuous algorithm, for example, via a digital filter. The operation on the continuous data stream can be described as a filter, such as a finite impulse response (FIR) filter, an infinite impulse response (IIR) filter, or a recursive response (RR) filter. The exact implementation will depend on the processing environment in which the algorithm is deployed, as well as the number of harmonics to be extracted. Combining the general definition of an FIR filter with the Fourier transform in equation [2] above, we get:

[0091]

number

[0092] and, [5] b timebin,k =exp(-2πi*tbink / timebins), In the above equation, the FIR coefficients b for each frequency mode k are timebin,k , which are integrated "backwards" in time. For a moderate number of extracted frequencies, the Fourier transform in equation [4] is timebin,k I measThe time bins can be updated by adding (-timebin) and subtracting the "oldest" if timebin = timebins. This provides the fastest response time to changes in the measured current, but may not be the optimal approach if it is desired to extract a large number of frequencies. If it is desired to extract a large number of frequencies, a batch algorithm such as a fast Fourier transform (FFT) can be used, which can be applied to a window (the "timebins" in Equations [4] and [5]), but the periodicity of application is much longer than the sampling time (e.g., determined by the expected rate of change of the underlying chemistry in this case). For example, in a CGM scenario, the FFT can be applied every 1-5 minutes, with the data window covering less than 1-5 minutes (perhaps much shorter, such as a few seconds if CPU constraints are severe). In still other cases where only a few harmonics are extracted, a Goertzel transform can be employed.

[0093] The embodiments described herein provide a more efficient method for performing measurements of glucose and possibly other analyte concentrations in a continuous acquisition and signal processing mode, generating time-domain measurements that lag from ground truth in interstitial fluid by a time interval small compared to the delay between arterial blood and interstitial fluid. The algorithm is compatible with any implementation of predictive modeling of hypoglycemic / hyperglycemic events (e.g., linear regression, autoregressive predictive modeling, Kalman filtering, or artificial neural networks). In some embodiments, predictive modeling may be employed (e.g., instead of or in addition to using a harmonic relationship database) to generate one or more predictive equations employing a set of one or more harmonic relationships, such as harmonic ratios, and to determine analyte concentrations during continuous analyte sensing. For example, in some embodiments, multivariate regression may be employed using a number of harmonic relationships, such as analyte concentrations and harmonic ratios and / or other terms and / or cross terms, known as regression targets, as input parameters that provide information gleaned from nonlinear current responses to periodic excitation signals. In some embodiments, one or more of such predictive equations may be stored in a memory of a continuous analyte sensing device and used to calculate analyte values ​​through the use of harmonic relationships during continuous analyte sensing. For example, one or more predictive equations may be stored in memory 112, 228 and / or 302 of CGM system 100, 150, 170, 200 or 300.

[0094] One of the main challenges of continuous glucose monitoring is the calibration of sensors in interstitial fluid. Harmonic ratios or other relationships as algorithm attributes can significantly reduce the algorithm's reliance on calibration compared to utilizing absolute current measurements. Compared to DC measurements, which generate a single data point per measurement, the advantages of the embodiments described herein are that many harmonics (typically in the range of 10–20) can be extracted, and the ratios or other functional relationships between all these harmonics can provide hundreds of truly independent simultaneous measurements. In contrast to many existing algorithms derived from BGM measurements, the embodiments described herein demonstrate algorithms that can be time-invariant (e.g., glucose values ​​can be extracted from any time range that is an integer multiple of the period of the excitation pulse). Using harmonic ratios can eliminate the need for "absolute" sensor calibration, potentially relaxing the bit resolution of the analog-to-digital converter stage, reducing component cost and analog front-end complexity. The algorithm is highly scalable, enabling optimized deployment on different CPU platforms, from efficient Goertzel transforms in small microcontrollers to full-scale real-time Fourier analysis for high-performance processors.

[0095] In some embodiments, phase angle and / or power spectral density data may be generated from the digitized frequency domain data and used for harmonic intensity information.

[0096] In some embodiments, the non-wearable portion of the analyte monitoring system may include a mobile phone, such as a smartphone, a smartwatch, or the like.

[0097] As described above in connection with FIG. 1A , various electronic components and circuits are configured to couple to a power source, such as, but not limited to, a battery. In some embodiments, a battery alternative may include a supercapacitor. In some embodiments, one or more energy harvesting circuits, such as the class of ubiquitous energy harvesting circuits well known for use in RFID tags, may be used to charge the capacitor or supercapacitor, which then functions as a battery. A well-known class of energy harvesting circuits is configured to convert energy from an RF energy field into a regulated DC voltage. In some alternative embodiments, the charging current for the capacitor or supercapacitor may be provided by a thermoelectric generator. The temperature gradient required to generate a voltage by the thermoelectric generator may be obtained, for example, from the difference between the user's skin at the CGM attachment point and a distal end of the CGM positioned away from the attachment point.

[0098] As used herein, the term "amperometry" refers to the detection of ions in solution, which detection is based, at least in part, on the detection of current or changes in current.

[0099] "Processor" means any one or more microprocessors, one or more central processing units (CPUs), one or more graphics processing units (GPUs), one or more computing devices, one or more microcontrollers, one or more digital signal processors (DSPs), one or more embedded processors such as those in a system-on-chip (SoC), one or more field programmable gate arrays (FPGAs) such devices, or various combinations of the foregoing.

[0100] While it is contemplated that a suitably programmed general-purpose computer or computing device may be used, it is also contemplated that hard-wired circuitry or custom hardware (e.g., application-specific integrated circuits (ASICs)) may be used in place of or in combination with software instructions to implement the processes of various embodiments. Thus, embodiments are not limited to any specific combination of hardware and software.

[0101] The term "computer-readable medium" refers to any legal medium that participates in providing data (e.g., instructions) that may be read by a computer, a processor, or a similar device. Such media can take many forms, including but not limited to, non-volatile media, volatile media, and certain types of transmission media. Non-volatile media can include, for example, optical disks, magnetic disks, and other persistent memory. Volatile media can include, but is not limited to, static random access memory (SRAM) and dynamic random access memory (DRAM). Types of transmission media can include, for example, coaxial cables, conductive wires, traces, or lines, including the wires, traces, or lines that comprise a system bus coupled to a processor, and fiber optics. Common forms of computer-readable media may include, for example, floppy disks, flexible disks, hard disks, magnetic tape, any other magnetic media, CD-ROMs, digital video disks (DVDs), any other optical media, punch cards, paper tape, any other physical media with a pattern of holes, read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory (a type of EEPROM), resistive memory, filament memory, metal oxide memory, phase change memory, spin transfer memory, USB memory sticks, any other memory chip or cartridge, or any other medium from which a computer or processor can access data or instructions stored thereon. As used herein, the terms "computer-readable memory" and / or "tangible media" specifically exclude signals, waves, and waveforms, or other intangible or transitory media that may nevertheless be read by a computer.

[0102] As used herein, the term "nominal" refers to a desired or target value of a characteristic, measurement, or other parameter of a component, product, signal, or process, along with a range of values ​​above or below the desired value. The range of values ​​is typically due to slight variations or tolerances in the manufacturing process.

[0103] As used herein, "or" means an inclusive or, not an exclusive or. That is, as used herein, A or B means including A only, B only, and A and B together. If it is intended to express A only or B only, rather than A and B together, this will be explicitly stated. At least one of A and B means including A only, B only, and A and B.

[0104] An enumerated list of items (which may be numbered or unnumbered) does not imply that any or all of the items are mutually exclusive, unless otherwise specified. Similarly, an enumerated list of items (which may be numbered or unnumbered) does not imply that any or all of the items are inclusive of any category, unless otherwise specified. For example, the enumerated list "computers, laptops, smartphones" does not imply that any or all of the three items in the list are mutually exclusive, nor does it imply that any or all of the three items in the list are inclusive of any category.

[0105] Terms such as first, second, and the like may be used herein to describe various elements, components, regions, parts, or sections, but these elements, components, regions, parts, or sections should not be limited by these terms. These terms may be used to distinguish one element, component, region, part, or section from another element, component, region, part, or section. For example, a first element, component, region, part, or section discussed above could be referred to as a second element, component, region, part, or section without departing from the teachings of this disclosure. A first, second, or third circuit may be a subcomponent of one or more other circuits.

[0106] The foregoing description discloses only exemplary embodiments. Modifications of the above-disclosed apparatus and methods which fall within the scope of the present disclosure will be readily apparent to those of ordinary skill in the art. [Explanation of symbols]

[0107] 100 CGM systems 102 First Circuit 104 CGM sensors 105 Electrode 106 Second Circuit 108 Third Circuit 110 processors 112 memory 114 Harmonic Relation Database 116 Wearable sensor part 117 Display 118 Portable User Device Part 120 processors 122 Display 150 CGM systems 170 CGM systems 200 CGM systems 202 Wearable part 204 Portable User Device Part 206 Periodic Signal Generator 207 Electrode 208 CGM sensors 209 Working electrode 210 Analyte-containing fluid 212 Current measurement circuit 214 Sampling Circuit 216 memory 218 Transceiver 220 Antenna 222 Microcontroller 223 Harmonic Relational Database (HRD) 224 Transceiver 226 Antenna 228 memory 230 microprocessor 232 Display 233 Display 234a Arrow 234b Arrow 300 CGM system 302 memory 304 Transceiver 306 Antenna 308 Microprocessor 310 Display 400 Data Structures 400 Database Structure 500 Harmonic Relation Database 502 First Vector 506 Second Vector 510 Third Vector 514 nth vector 504, 508, 512, and 516 Analyte concentration values 503, 507, 511, and 515 Links or pointers 700 methods 704 Decision Action 800 ways 900 methods 1000 ways

Claims

1. 1. A method for electronically probing redox reactions in an analyte-containing fluid, comprising: applying, by a first circuit, a periodic voltage excitation signal to a first electrode of a CGM sensor disposed within the analyte-containing fluid, the periodic voltage excitation signal having a fundamental frequency; generating, by a second circuit while the first circuit applies the periodic voltage excitation signal, an amperometric signal having a magnitude of a current transmitted from the CGM sensor, the current being generated by an oxidation-reduction reaction in the analyte-containing fluid, the magnitude of the amperometric signal depending, at least in part, on an analyte concentration in the analyte-containing fluid; sampling the current measurement signal by a third circuit coupled to the second circuit; providing, by the third circuit, digitized time-domain sampled data representative of the current measurement signal; extracting a plurality of harmonic signals based at least in part on the digitized time-domain sample data, the harmonic signals being harmonics of the fundamental frequency, each harmonic signal having a corresponding magnitude, and converting the digitized time-domain sample data into digitized frequency-domain data; calculating a set of harmonic relationships based at least in part on the plurality of harmonic signals, the set of harmonic relationships being based on one or more harmonic ratios; accessing a harmonic relationship database, the harmonic relationship database having a plurality of sets of stored harmonic relationships, each set of stored harmonic relationships of the plurality of sets of stored harmonic relationships being associated with a known analyte concentration; determining the analyte concentration in the analyte-containing fluid by comparing the set of stored harmonic relationships from the harmonic relationship database with the set of calculated harmonic relationships; A method comprising:

2. The method of claim 1 , further comprising generating power spectral density data from the digitized frequency domain data.

3. accessing the harmonic relation database, The method of claim 1 , comprising accessing a lookup table.

4. 10. The method of claim 1, further comprising wirelessly transmitting the digitized time-domain sample data representing the amperometric signal from a wearable continuous glucose monitoring device to a smartphone.

5. 2. The method of claim 1, wherein sampling the current measurement signal comprises sampling at a first sampling rate ranging from 10 samples per second to 1000 samples per second for a first period of time.

6. 6. The method of claim 5, wherein the first period of time ranges from 10 seconds to 300 seconds.

7. 2. The method of claim 1, wherein the fundamental frequency of the periodic voltage excitation signal is in the range of 0.1 Hz to 10 Hz and / or the amplitude of the periodic voltage excitation signal is in the range of 150 millivolts to 500 millivolts.

8. The method of claim 1 , wherein the analyte-containing fluid comprises glucose.

9. The method of claim 1 , wherein determining the magnitude of the analyte concentration in the analyte-containing fluid is performed by a wearable portion of an analyte monitoring system.

10. 10. The method of claim 9, further comprising displaying the analyte concentration on a display of the wearable portion.

11. 10. The method of claim 1, further comprising employing the calculated harmonic relationship to distinguish an analyte from at least one analyte interferent.

12. 1. A continuous analyte monitoring (CAM) system comprising: a first circuit configured to apply a periodic voltage excitation signal to a first electrode of a CGM sensor disposed within the analyte-containing fluid; a second circuit configured to generate an amperometric signal having a magnitude of a current transmitted from the CGM sensor, the current being generated by an oxidation-reduction reaction in the analyte-containing fluid, the magnitude of the amperometric signal being at least partially dependent on an analyte concentration in the analyte-containing fluid; and a third circuit coupled to the second circuit and configured to sample the current measurement signal, the third circuit further configured to generate digitized time-domain sample data; a processor coupled to a memory, the memory having a harmonic relation database stored therein, the processor, when executed by the processor, causing the processor to: extracting a plurality of harmonic signals from the digitized time domain sample data, the extracting comprising converting the digitized time domain sample data to frequency domain data; calculating a set of harmonic relationships based at least in part on the plurality of harmonic signals, the set of harmonic relationships being based on one or more harmonic ratios; accessing the harmonic relationship database, the harmonic relationship database having a plurality of sets of stored harmonic relationships, each set of stored harmonic relationships of the plurality of sets of stored harmonic relationships being associated with a known analyte concentration; determining the analyte concentration in the analyte-containing fluid by comparing the set of stored harmonic relationships from the harmonic relationship database with the set of calculated harmonic relationships; a processor further having stored instructions to cause the processor to perform A continuous analyte monitoring (CAM) system comprising:

13. The CAM system of claim 12 , wherein the first circuit, the second circuit, and the third circuit are disposed on a wearable device.

14. The CAM system of claim 13 , wherein the processor and the memory are located on the wearable device.

15. 13. The CAM system of claim 12, wherein the instructions, when executed by the processor, further cause the processor to direct a wireless transmitter to transmit the analyte concentration.

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