Photoacoustic glucose sensors, systems, and methods

Abstract

Systems and methods for estimating analyte concentration levels using a photoacoustic sensor are described herein. Also described herein are training methods for training an analyte sensor to more accurately estimate analyte concentration levels based on received acoustic signals.

Description

Technical Field

[0001] Embodiments of the subject matter described herein generally relate to photoacoustic techniques for monitoring analyte concentration levels. More specifically, embodiments of the subject matter described herein relate to photoacoustic techniques for monitoring a user's blood glucose concentration level. Background Art

[0002] Approximately 450 million people worldwide suffer from diabetes. As is well known, diabetes is the result of inefficient production or use of insulin by the body, which leads to the medical complications of high or low blood sugar levels in the short term and, if left untreated, microvascular or macrovascular problems in the long term. Therefore, controlling blood sugar concentration levels within a desired range, for example by administering insulin, is essential to prevent the development of such complications.

[0003] In order to determine when blood sugar concentration levels need to be controlled, it is necessary to measure the blood sugar concentration levels of diabetic patients.

[0004] Photoacoustic technology for monitoring glucose concentration levels is desirable for several reasons. Specifically, photoacoustic technology does not require invasive components (such as transdermal sensor probes or "finger prick" punctures) to monitor a user's glucose concentration level. Due to the non-invasive nature of photoacoustic technology, the user's comfort when wearing the device can be improved, and the ease and comfort of device installation can also be improved. In addition, blood glucose concentration levels can be measured continuously using photoacoustic methods, compared to the less useful intermittent monitoring achieved through "finger prick" monitoring technology.

[0005] Photoacoustic technology relies on illuminating a target with light, such as that provided by a laser beam. Due to thermal diffusion, the light produces thermal effects in the target and the thin layer of air in contact with the target, such as volume expansion. This causes pressure oscillations that generate acoustic waves. The characteristics of these acoustic waves depend on several factors, such as the target's absorption coefficient for the wavelength of light used, the density of the medium through which the acoustic waves propagate, the target's thermal expansion coefficient, the speed of the acoustic waves, and more.

[0006] If the skin is used as a target, the light can penetrate the skin a certain distance and excite molecules under the skin, such as glucose molecules. The sound waves generated by the thermal excitation (and subsequent volume expansion) of these glucose molecules can be used to estimate the concentration of glucose molecules.

[0007] However, there are disadvantages associated with the use of photoacoustic technology for monitoring blood glucose concentration levels. Specifically, the acoustic waves generated by the excitation of glucose molecules may not have sufficient amplitude to obtain a sufficiently strong signal for accurate measurement of blood glucose concentration. Furthermore, the characteristics of the acoustic waves may vary from person to person, depending on, for example, the user's skin light transmittance properties, skin sweat gland activity, skin composition and structure, etc.

[0008] Therefore, it is desirable to overcome the disadvantages associated with photoacoustic techniques for measuring analyte concentrations, such as blood glucose concentration levels.Furthermore, other desirable features and characteristics will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background. Summary of the Invention

[0009] According to a first aspect, an analyte monitor is provided. The analyte monitor includes a light emitter configured to emit light at a target. The analyte monitor includes a sensor configured to sense acoustic waves generated by analyte molecules in the target in response to the emitted light. The analyte monitor also includes a resonant chamber having a size suitable for forming a standing wave with the generated acoustic waves. The analyte monitor includes a signal processor configured to estimate an analyte concentration level based on the sensed acoustic waves.

[0010] In one embodiment, the light emitter is configured to emit light having a wavelength in the mid-infrared region.

[0011] In one embodiment, the sensor is positioned proximate an antinode of the standing wave to be formed in the resonant chamber.

[0012] In one embodiment, the signal processor is configured to determine whether the estimated analyte concentration level falls within one of two or more predetermined ranges. Preferably, the analyte monitor further comprises a transmitter configured to transmit a signal when the estimated analyte concentration level falls within one of the two or more predetermined ranges.

[0013] In one embodiment, the sensor comprises a microphone.In an alternative embodiment, the sensor comprises a transducer.

[0014] In one embodiment, the resonant chamber comprises a resonant branch for forming the standing wave and a measuring branch connecting the resonant branch to the sensor, the measuring branch being positioned close to an antinode of the standing wave to be formed in the resonant branch.

[0015] According to a second aspect, a method for training an algorithm for estimating an analyte concentration level of a specific target based on an acoustic signal generated by thermal vibrations of analyte molecules in the target in response to irradiation of the target with light is provided. The method comprises the steps of obtaining an acoustic signal with a sensor of a first analyte monitor and simultaneously obtaining an analyte concentration level using a reference analyte monitor to form a training set, the reference analyte monitor and the first analyte monitor being different. The method comprises the steps of training an algorithm of a signal processor of the first analyte monitor using the obtained acoustic signal and features of the analyte concentration levels of the obtained training set. The method further comprises the steps of estimating the analyte concentration level based on the obtained acoustic signal using the first analyte monitor after training the algorithm.

[0016] In one embodiment, the reference analyte monitor is a continuous glucose monitor having invasive components and the first analyte monitor is non-invasive.

[0017] In one embodiment, the obtained characteristic of the acoustic signal is selected from the group consisting of: a timestamp at which the acoustic signal was recorded; an amplitude of the acoustic signal, an in-phase component of the acoustic signal; and an out-of-phase component of the acoustic signal; and a frequency of the acoustic signal.

[0018] In one embodiment, the step of estimating the analyte concentration level based on the obtained acoustic signal using the first analyte monitor includes determining whether the estimate of the analyte concentration level falls within two or more predetermined ranges.

[0019] In one embodiment, the method further comprises the step of determining a confidence level that the analyte concentration level falls within one of the two or more predetermined ranges.

[0020] In one embodiment, the method further comprises transmitting a signal using a transmitter in response to determining that the estimated value of the analyte concentration level falls within one or more predetermined ranges of the two or more predetermined ranges. Preferably, the signal is a predetermined range value of blood glucose concentration or an alarm signal.

[0021] In one embodiment, the analyte molecule is a glucose molecule.

[0022] In one embodiment, the step of estimating the concentration level from the obtained acoustic signal using the first analyte monitor comprises amplifying and filtering the obtained acoustic signal. Preferably, at least a portion of the amplifying and filtering the obtained acoustic signal is performed using a resonant chamber.

[0023] In one embodiment, the method further comprises the steps of: converting the obtained acoustic signal into an analog electrical signal using a sensor; and converting the analog electrical signal into a digital electrical signal using an analog-to-digital converter.

[0024] According to a third aspect, a computer-readable medium containing instructions is provided that, when executed by a processor, performs a method for training an algorithm for estimating an analyte concentration level of a specific target based on an acoustic signal generated by thermal vibrations of analyte molecules in the target in response to illumination of the target with light. The method comprises the following steps: obtaining an acoustic signal with a sensor of a first analyte monitor and simultaneously obtaining an analyte concentration level using a reference analyte monitor to form a training set, the reference analyte monitor and the first analyte monitor being different. The method comprises the following steps: training an algorithm of a signal processor of the first analyte monitor using the obtained acoustic signal and features of the analyte concentration levels of the obtained training set; and, after training the algorithm, estimating the analyte concentration level using the first analyte monitor based on the obtained acoustic signal.

[0025] According to a fourth aspect, a photoacoustic method for estimating an analyte concentration level in a target is provided. The method includes the step of measuring the impedance of the target via electrical impedance spectroscopy. The method further includes the steps of illuminating the target with light using a light emitter; and obtaining, using a sensor, a primary acoustic signal generated by the target in response to the illumination of the target with light of a first wavelength. The method then includes the step of estimating the analyte concentration level in the target based on both the obtained primary acoustic signal and the measured impedance of the target.

[0026] According to a fifth aspect, a photoacoustic method for estimating an analyte concentration level in a target is provided. The method includes applying heat to the target using a heating element. The method then includes measuring a thermal response of the target to the applied heat using a thermal sensor. The method then includes illuminating the target with light of a first wavelength using a light emitter, and obtaining, using a sensor, a primary acoustic signal generated by the target in response to the illumination of the target with the first wavelength. The method then includes estimating the analyte concentration level in the target based on both the obtained primary acoustic signal and the measured thermal response.

[0027] According to a sixth aspect, a photoacoustic method for estimating an analyte concentration level in a target is provided. The method comprises the steps of illuminating the target with light of a first wavelength; and obtaining, with a sensor, a primary acoustic signal generated by the target in response to the illumination of the target with the light of the first wavelength. The method further comprises the steps of illuminating the target with light of a second wavelength; and obtaining, with the sensor, a secondary acoustic signal generated by the target in response to the illumination of the target with the light of the second wavelength. The method further comprises the step of estimating a background absorption level of light based on the obtained secondary acoustic signal. The method further comprises the step of estimating the analyte concentration level in the target based on both the obtained primary acoustic signal and the estimated background absorption level of light.

[0028] According to a seventh aspect, an analyte monitor is provided. The analyte monitor includes a light emitter and a sensor, the light emitter being configured to emit light of a first wavelength toward the target, the sensor being configured to sense the acoustic waves generated by the analyte molecules in the target in response to the light with the first wavelength emitted by the light emitter. A primary acoustic signal is generated by the sensor based on the sensed acoustic wave. The analyte monitor further includes a voltage controller and a first electrode and a second electrode, the first electrode and the second electrode being arranged to be placed in contact with the target to apply a voltage to the target. The analyte monitor further includes an impedance sensor module to determine the impedance of the target based on the applied voltage. The analyte monitor further includes a signal processor, the signal processor being configured to estimate the analyte concentration level based on both the primary acoustic signal sensed by the sensor and the impedance determined by the impedance sensor module. In one embodiment, the analyte monitor further or alternatively includes a heating element and a thermal sensor, the heating element being configured to apply heat to the target, the thermal sensor measuring the thermal response of the target to the applied heat, wherein the signal processor further estimates the analyte concentration level based on the measured thermal response. Additionally or alternatively, the light emitter may emit light of a second wavelength toward the target, the first wavelength and the second wavelength being different, and wherein the signal processor estimates a background absorption level of light based on acoustic waves sensed by the sensor in response to the emission of light of the second wavelength, and wherein the signal processor further estimates the analyte concentration based on the estimated background absorption level of light.

[0029] According to an eighth aspect, an analyte monitor is provided. The analyte monitor includes a light emitter and a sensor, the light emitter being configured to emit light of a first wavelength toward a target, the sensor being configured to sense acoustic waves generated by analyte molecules in the target in response to the light emitted by the light emitter. A primary acoustic signal is generated by the sensor based on the sensed acoustic waves. The analyte monitor also includes a heating element configured to apply heat to the target and a thermal sensor configured to measure the thermal response of the target to the applied heat. The analyte monitor also includes a signal processor configured to estimate the analyte concentration level based on the primary acoustic signal and the measured thermal response. In one embodiment,

[0030] According to a ninth aspect, an analyte monitor is provided. The analyte monitor includes a light emitter configured to emit light of a first wavelength and a second wavelength toward a target, the first wavelength and the second wavelength being different. The analyte monitor includes a signal processor to estimate a background absorption level of light based on acoustic waves sensed by a sensor in response to the emission of the second wavelength of light. The signal processor also estimates an analyte concentration level based on both the primary acoustic signal and the estimated background absorption level of light.

[0031] According to a tenth aspect, an analyte monitor for estimating the analyte concentration level in a target is provided. The analyte monitor includes a light emitter and a sensor, the light emitter being configured to emit light toward the target, the sensor being configured to sense acoustic waves generated by analyte molecules in the target in response to the light emitted by the light emitter. The sensor generates a primary acoustic signal based on the sensed acoustic waves. The analyte monitor also includes a voltage controller and a first electrode and a second electrode, the first electrode and the second electrode being configured to contact the target. The voltage controller is configured to bias the electrodes. The analyte monitor also includes a signal processor configured to estimate the analyte concentration level based on the primary acoustic signal.

[0032] According to an eleventh aspect, a method for estimating an analyte concentration level in a target is provided. The method includes the steps of emitting light toward the target using a light emitter, and applying a potential bias to the target using a first electrode, a second electrode, and a voltage controller. The method also includes the steps of sensing, using a sensor, acoustic waves generated by analyte molecules in the target in response to the light emitted by the light emitter, and generating a primary acoustic signal based on the sensed acoustic waves. The method also includes the steps of estimating the analyte concentration level based on the acoustic waves sensed by the sensor using a signal processor.

[0033] This summary is provided to introduce a series of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] A more complete understanding of the subject matter may be derived by referring to the detailed description and claims when considered in conjunction with the following drawings, wherein like reference numerals refer to like elements throughout.

[0035] Figure 1 A schematic diagram showing a photoacoustic technique for measuring the concentration of a target analyte;

[0036] Figure 2 A graph showing the wavelengths of light that interact strongly with glucose molecules;

[0037] Figure 3 shows a schematic diagram of an analyte monitor according to an exemplary embodiment;

[0038] Figure 4 shows an exploded view of an analyte monitor according to an exemplary embodiment;

[0039] Figure 5 shows an angled, partially transparent perspective view of an analyte monitor according to an exemplary embodiment;

[0040] Figure 6 shows a computer simulation of a resonant chamber according to an exemplary embodiment;

[0041] Figure 7 A flow chart illustrating a method according to an exemplary embodiment is shown;

[0042] Figure 8 shows a flow chart of another method according to an exemplary embodiment;

[0043] Figure 9 shows a flow chart of another method according to an exemplary embodiment;

[0044] Figure 10 shows a flow chart of another method according to an exemplary embodiment;

[0045] Figure 11 shows a flow chart of another method according to an exemplary embodiment;

[0046] Figure 12 showing a graph comparing results obtained from an analyte monitor according to an exemplary embodiment with results obtained from a conventional analyte monitor;

[0047] Figure 13 Another graph illustrating results obtained from an analyte monitor according to an exemplary embodiment compared to results obtained from a conventional analyte monitor;

[0048] Figure 14 shows a schematic diagram of an analyte monitor according to an exemplary embodiment;

[0049] Figure 15 A flowchart illustrating a method of fitting a prediction model according to an exemplary embodiment is shown;

[0050] Figure 16 A flow chart illustrating a method according to an exemplary embodiment is shown;

[0051] Figure 17 shows a schematic diagram of an analyte monitor according to an exemplary embodiment;

[0052] Figure 18 A flowchart illustrating a method of fitting a prediction model according to an exemplary embodiment is shown;

[0053] Figure 19 A flow chart illustrating a method according to an exemplary embodiment is shown;

[0054] Figure 20 shows a schematic diagram of an analyte monitor according to an exemplary embodiment;

[0055] Figure 21 A flow chart illustrating a method according to an exemplary embodiment is shown;

[0056] Figure 22 shows a bottom view of an analyte monitor according to an exemplary embodiment;

[0057] Figure 23 A diagram explaining the reverse iontophoresis technique is shown;

[0058] Figure 24 shows a schematic diagram of an analyte monitor according to an exemplary embodiment; and

[0059] Figure 25 A flow chart of a method according to an exemplary embodiment is shown. DETAILED DESCRIPTION

[0060] The following detailed description is merely illustrative in nature and is not intended to limit the embodiments of the present invention or the application and uses of such embodiments. As used herein, the word "exemplary" means "serving as an example, instance, or illustration." Any specific implementation described herein as exemplary is not necessarily to be construed as preferred or advantageous over other specific implementations. Furthermore, no intention is to be bound by any expressed or implied theory presented in the preceding technical field, background, summary, or the following detailed description.

[0061] Techniques and techniques may be described herein in terms of functions and / or logic block components, with reference to symbolic representations of operations, processing tasks, and functions that may be performed by various computing components or devices. Such operations, tasks, and functions are sometimes referred to as being computer-executed, computerized, software-implemented, or computer-implemented. It should be understood that the various block components shown in the figures may be implemented by any number of hardware, software, and / or firmware components configured to perform a specified function. For example, embodiments of a system or component may employ various integrated circuit components, such as memory elements, digital signal processing elements, logic elements, and lookup tables, which may perform various functions under the control of one or more microprocessors or other control devices.

[0062] When implemented in software or firmware, the various elements of the systems described herein are essentially code segments or instructions that perform various tasks. In certain embodiments, the programs or code segments are stored in a tangible processor-readable medium, which can include any medium capable of storing or transmitting information. Examples of non-transient and processor-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, and hard disks.

[0063] "Connected / Coupled"—The following description refers to elements or features being "connected" or "coupled" together. As used herein, unless expressly stated otherwise, "coupled" or "connected" means that one element / structure is directly or indirectly joined to (or in direct or indirect communication with) another element / structure, and not necessarily mechanically.

[0064] In addition, certain terms may be used in the following description for reference purposes only, and thus these terms are not intended to be limiting. For example, terms such as "upper," "lower," "above," and "below" may be used to refer to directions in the accompanying drawings with reference to them. Terms such as "front," "rear," "back," "side," "outside," and "inside" describe the direction and / or position of parts of a component within a consistent but arbitrary framework, which is made clear by reference to the text and related drawings describing the component in question. Such terms may include the words specifically mentioned above, their derivatives, and words of similar meaning. Similarly, unless the context clearly indicates otherwise, the terms "first," "second," and other such numerical terms referring to structures do not imply an order or sequence.

[0065] For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, network control, and other functional aspects of the system (and the individual operating components of the system) may not be described in detail herein. In addition, the connecting lines shown in the various figures contained herein are intended to represent exemplary functional relationships and / or physical connections between the various elements. It should be noted that alternative or additional functional relationships or physical connections may exist in embodiments of the subject matter.

[0066] It should be understood that the digital signal processor and any corresponding logic elements described later, alone or in combination, are exemplary means for performing the claimed functions.

[0067] Figure 1 A schematic diagram illustrating how photoacoustic measurement techniques can be used to obtain a sensor signal representing an analyte concentration (e.g., a user's blood glucose concentration level) is shown. Figure 1 As can be seen in FIG, the light emitter 10 is configured to emit light toward a target 12, which may be a tissue covered by the user's skin. The light incident on the target 12 then penetrates a certain distance into the target 12 and interacts with the analyte molecules (e.g., glucose molecules) present in the target 12. The analyte molecules are thermally excited and vibrate, and the medium surrounding these molecules undergoes volume expansion, thereby generating an acoustic wave. This acoustic wave propagates out of the target 12 and into the medium surrounding the target, such as air. Figure 1 The propagation of the sound waves is shown in FIG. 1 by using bold arrows. The sound waves are then detected by a sensor 13 configured to convert the pressure of the sound waves into an electrical signal. In an exemplary embodiment, the sensor 13 comprises a microphone. In an alternative exemplary embodiment, the sensor 13 comprises a transducer, such as a piezoelectric transducer.

[0068] In an exemplary embodiment, the wavelength of light emitted by light emitter 10 is selected so as to interact strongly with the analyte of interest. Figure 2 The curve shown in Figure 20As can be seen, if the analyte of interest is glucose, the wavelength of the light emitted by the light emitter can be selected to correspond to a wavelength that strongly interacts with glucose molecules for improved thermal excitation of the glucose molecules. Figure 2 curve Figure 20 It is shown that wave numbers (the inverse of the wavelength) between about 1000 and 1150 interact strongly with glucose molecules. This wave number range corresponds to the wavelength of light in the mid-infrared region (8,700 nm to 10,000 nm).

[0069] exist Figure 2 It can also be seen that the absorbance of glucose molecules in this wavenumber region generally increases with increasing glucose concentration levels. Figure 20 The three spectra 21, 22 and 23 shown in FIG. 2 relate to absorbance at blood glucose concentration levels of 300 mg / dl, 200 mg / dl and 100 mg / dl, respectively.

[0070] Generally, the higher the absorbance level of an analyte, the larger the acoustic response signal that will subsequently be generated by thermal excitation of that analyte. Thus, the amplitude of the acoustic response signal after illuminating a target with light of a particular wavelength can be correlated to the analyte concentration level present in the target.

[0071] The present inventors recognized that at certain analyte concentration levels, the amplitude of the acoustic response was insufficient to accurately distinguish differences in analyte concentration without using highly sensitive, expensive sensors. These types of highly sensitive sensors can be prohibitively expensive when attempting to commercialize analyte monitors based on photoacoustic technology.

[0072] To avoid the need for these types of expensive sensors to accurately detect the amplitude of the acoustic response from a target, the inventors recognized that the acoustic signal response could be improved by using a resonant chamber. A resonant chamber uses the physical principles of resonance to enhance the acoustic response. More specifically, when a sound wave enters the resonant chamber, it reflects back and forth within the chamber with minimal energy loss, forming a standing wave. As additional sound waves enter the chamber, the strength of the standing wave increases.

[0073] Thus, by pulsing the light emitted from the light emitter 10 and then measuring the intensity of the acoustic standing waves formed in response to these light pulses, it is possible to more accurately correlate the acoustic response to analyte concentration values ​​for a given mass of the sensor.

[0074] The inventors have also discovered that by using a resonant chamber, a certain amount of noise filtering of the acoustic response can be achieved. More specifically, because the dimensions of the resonant chamber are designed to form a standing wave with the acoustic wave having the specific wavelength of interest, "noise" (acoustic waves of different wavelengths / frequencies) are not amplified by the standing wave in the same manner as the acoustic wave having the wavelength of interest. Thus, the resonant chamber not only amplifies the wavelength of the acoustic wave of interest, but also advantageously acts as a mechanical bandwidth filter for the acoustic wave of the acoustic response.

[0075] Figure 3 A schematic diagram of an exemplary analyte monitor 300 is shown in FIG. Figure 3 As shown, analyte monitor 300 includes a light emitter 310 for emitting light (shown with a thin dashed line) toward a target 312. In an exemplary embodiment, light emitter 310 includes a light emitting diode (LED). In an alternative embodiment, light emitter 310 includes a laser chip. Light emitter controller module 311 includes a circuit associated with light emitter 310. In an exemplary embodiment, light emitter controller module 311 is configured to control light emitter 10 so that the pulses of light emitted by light emitter 10 have a predetermined or variable pulse repetition frequency (PRF). In other words, light emitter controller module 311 is suitable for modulating the frequency of the light pulses emitted by light emitter 310. Preferably, light emitter controller module 311 is configured to control light emitter 310 to emit light pulses with a duration of about 500ns per pulse at a frequency of about 50kHz or higher. It has been found that this pulse duration and frequency achieve a good acoustic response for certain analytes of interest (e.g., glucose).

[0076] In one embodiment, the light emitter 310 may include a heat conduction element (not shown) configured to conduct heat away from the light emitter 310 to reduce the likelihood of the light emitter 310 being heated to an undesirable temperature. In additional or alternative embodiments, the light emitter 310 may include an anti-reflective surface (not shown) to reduce acoustic signal response noise caused by reflections of the emitted light.

[0077] Analyte monitor 300 also includes a sensor 313, such as a microphone or a transducer, such as a piezoelectric transducer. Sensor 313 is configured to detect acoustic waves (shown by a thick dashed line) emitted by thermal excitation of analyte molecules and volume expansion in target 312 by light emitted from light emitter 310, and to generate an electrical signal based on these acoustic waves.

[0078] The analyte monitor 300 also includes a resonant chamber 314. The size and dimensions of the resonant chamber 314 are set to form a standing wave of acoustic waves having a wavelength corresponding to the wavelength of the acoustic waves generated by the volume expansion of the analyte-containing medium of interest after irradiation with light, which is controlled by the pulse repetition frequency (PRF). For example, if the analyte of interest is glucose, and the wavelength of the acoustic waves generated by irradiating glucose with light at a pulse repetition frequency of about 55KHz is about 6mm, then the size and dimensions of the resonant chamber will be designed to support a standing wave having a length of about 6mm. In one embodiment, the length of the resonant chamber is a scalar multiple of the length of the acoustic waves generated by the thermal excitation and subsequent volume expansion of the glucose molecules, such as 6mm, 12mm, 18mm, etc. It will be understood that if the standing wave has a different wavelength, the size of the resonant chamber can be changed accordingly. Although Figure 3 The embodiment in FIG shows the light emitter being inside the resonant chamber, but in various embodiments, the light emitter is located outside the resonant chamber.

[0079] The inventors have found that by using such a resonant chamber, an amplification of the sound waves of up to about three times can be achieved compared to the underlying acoustic signal.In addition, as mentioned above, the resonant chamber can perform a noise filtering function.

[0080] In an exemplary embodiment, the sensor 313 is operatively connected to the signal processor 320. Figure 3 In the illustrated embodiment, signal processor 320 includes an operational amplifier ("op-amp") 315 that is configured to further amplify the electronic signal derived from the acoustic response of target 312. In the exemplary embodiment, op-amp 315 is operably connected to an analog-to-digital converter 316 that is configured to convert the analog electrical signal from sensor 313 into a digital signal. In the exemplary embodiment, analog-to-digital converter 316 is operably connected to a digital signal processor ("DSP") 317 for processing the digital signal.

[0081] Converting the analog signal from sensor 313 into a digital signal allows for accurate separation of acoustic responses of varying amplitudes into "bins" corresponding to varying glucose concentration levels, separated by predetermined thresholds. For example, an acoustic response of a first amplitude or less can be determined to be associated with a glucose concentration level of approximately 100 mg / dl or less, which can be determined by signal processor 320 to correspond to a "hypoglycemic" glucose level. An acoustic response having an amplitude between the first and second amplitudes can be determined to be associated with a glucose concentration between approximately 100 mg / dl and approximately 200 mg / dl, which can be determined by signal processor 320 to correspond to a "normal" glucose level. Further, an acoustic response having an amplitude greater than the second amplitude can be determined to be associated with a glucose concentration exceeding approximately 200 mg / dl, which can be determined by signal processor 320 to correspond to a "hyperglycemic" glucose level.

[0082] As will be discussed later Figure 11 Explained in more detail, in one embodiment, upon determining a "hypoglycemic" or "hyperglycemic" glucose level, the signal processor 320 is configured to transmit a warning signal to the user of the analyte monitor, or take some other form of action (such as automatically activating an insulin pump).

[0083] Furthermore, by "binning" the acoustic response using predetermined thresholds, a more computationally efficient and accurate monitoring of glucose concentration levels is achieved. Specifically, by comparing the amplitude of the acoustic response to a predetermined threshold, hypoglycemic and hyperglycemic events can be detected more quickly and accurately by the signal processor 320.

[0084] Steering Figure 4 , shows an exploded view of an analyte monitor 400 according to one embodiment. The analyte monitor 400 includes a light emitter 410 configured to emit light of a predetermined wavelength. The analyte monitor 400 also includes a sensor 413 configured to detect acoustic waves generated by the volume expansion of the analyte of interest after the analyte of interest is irradiated with light generated by the light emitter 410. The analyte monitor 400 also includes a resonant chamber 414 sized and configured to form a standing wave from the acoustic wave so as to amplify the amplitude of the acoustic wave. The analyte monitor 400 also includes a signal processor 420 configured to estimate the analyte concentration level based on the amplitude of the sensed acoustic response.

[0085] In an exemplary embodiment, the analyte monitor 400 also includes a power supply 422 configured to provide power to one or more of the light emitter 410 , the sensor 413 , and the signal processor 420 , or other components of the analyte monitor 400 .

[0086] In an exemplary embodiment, the analyte monitor 400 also includes a housing 421 that is configured to surround one or more of the above-mentioned components. In an exemplary embodiment, the housing 421 may include a housing cover to which the transmitter 423 is incorporated. In other exemplary embodiments, the transmitter 423 is located elsewhere in the analyte monitor, such as near the signal processor 420. Compared to the case where the transmitter 423 is located inside the housing 421 of the analyte monitor 400, incorporating the transmitter 423 into the housing cover allows the transmitter 423 to have a larger size and also reduces the overall size of the analyte monitor. For example, where the transmitter 423 includes an antenna (e.g., an RF antenna), the increased size of the transmitter 423 allows a stronger signal to be generated by the transmitter 423.

[0087] Transmitter 423 is operably connected to signal processor 420. When the measured analyte concentration is determined to be outside the "normal" range, transmitter 423 is configured to transmit a signal in response to this determination. For example, in one embodiment, transmitter 423 is configured to transmit a signal to a remote device (not shown) (such as a smart phone or smart watch) to warn the user of the analyte device of high or low analyte concentration levels. Additionally or alternatively, transmitter 423 is configured to transmit a signal to a remote device (not shown) for applying another substance to the user of the analyte monitoring device, such as an insulin pump for delivering insulin to a patient.

[0088] Figure 5 An angled perspective view of a partially assembled analyte monitor 400 is shown. Figure 5 As can be seen in FIG, the assembled analyte monitor 400 can have a length of less than about 50 mm, for example, less than about 40 mm, such as less than about 30 mm, preferably less than about 20 mm, and most preferably about 15 mm. The assembled analyte monitor 400 can have a width of less than about 50 mm, for example, less than about 40 mm, such as less than about 30 mm, preferably less than about 20 mm, and most preferably about 18 mm. Thus, the assembled analyte monitor 400 can be worn by a user alone, or even incorporated into the user's jewelry or clothing, such as into earrings, a watch, or a bracelet.

[0089] Figure 6 Another view of a resonant chamber according to an exemplary embodiment is shown. Figure 6 In FIG, two computer models of a resonant chamber 600 are shown. The resonant chamber 600 includes a resonant branch 601 and a measurement branch 602. The size and dimensions of the resonant branch 601 are designed to form a standing wave in the manner described above. The measurement branch 602 connects the resonant branch 601 to the sensor.

[0090] As in Figure 6 As can be seen in the two figures, the length of the resonant branch 601 and the placement of the measurement branch 602 along the resonant branch are important for obtaining a clear signal of the acoustic response. Figure 6 As seen in the lower figure of , the measurement branch 602 is positioned adjacent to the node of the standing wave formed in the resonant branch 601. Therefore, minimal propagation of the acoustic wave along the measurement branch 602 occurs, and the sensor measures a lower amplitude acoustic response. Figure 6 As can be seen in the above figure, measurement branch 602 is positioned close to the antinode of the standing wave formed in resonant branch 601. As a result, good propagation of the sound wave along measurement branch 602 occurs, and the sensor measures an acoustic response signal of acceptable amplitude. Therefore, in the exemplary embodiment, measurement branch 602 is positioned close to the antinode of the standing wave that will form in resonant chamber 600.

[0091] While developing the aforementioned analyte monitor, the inventors recognized that each user's acoustic response to light emitted from the light emitter is unique. In other words, a variety of variables may influence a user's acoustic response, such as skin transmittance, skin composition, sweat gland activity, and so on. Consequently, the inventors discovered that personalizing the analyte monitor's training program for individual users improves the analyte monitor's accuracy in detecting elevated or decreased analyte concentrations.

[0092] Figure 7 An overview of a general method S700 for training an analyte monitor for a particular user is shown. Figure 8 、 Figure 9 、 Figure 10 and Figure 11 The various steps of this general method are explained in more detail. In step S701, training data for training the signal processor of the analyte monitor is collected from a specific user. In one embodiment, the training data is collected by obtaining the user's analyte concentration level from a conventional analyte monitor (such as a commercially available continuous glucose monitor with an invasive component (such as a transdermal probe)) in combination with measuring the acoustic response of the analyte monitor according to various embodiments as described herein. After sufficient training data has been collected from the user, the method proceeds to step S702.

[0093] In step S702, the training data collected from the user is used to train an algorithm used by the signal processor to associate certain acoustic signal responses with the obtained analyte concentration level. In one embodiment, the training of the signal processor is a supervised machine learning process, in which the training data is first labeled and then used for training. In one embodiment, after the signal processor is trained using the training data, the signal processor can be tested against validation data to determine the accuracy of the signal processor in determining the analyte concentration level. After the signal processor is trained to an acceptable accuracy, the method proceeds to step S703.

[0094] At step S703, the analyte monitor has been adapted to the individual characteristics of the user and is used to monitor the user's analyte concentration level without concurrent use of another analyte monitor.

[0095] Each of steps S701 , S702 and S703 of method S700 will now be described in more detail.

[0096] Figure 8 Shown is a more detailed flow chart S800, which shows the exemplary steps involved in the collection of the training data set in step S701. In step S801, data is collected from a specific user via two or more analyte monitors. One of these analyte monitors is an analyte monitor according to an exemplary embodiment as described herein, and at least another analyte monitor in these analyte monitors includes a conventional analyte monitor, such as a continuous glucose monitor including a transdermal probe. Conventional analyte monitors collect data related to the user's blood analyte concentration level to serve as reference data. The analyte monitors according to various embodiments obtain acoustic responses in parallel with obtaining reference data.

[0097] Since the reference data is obtained in parallel with the acoustic response, the characteristics of the acoustic response can be associated with the reference data. In step S802, the characteristics (x1, x2, x3, x4, x5, and x6) of the acoustic response signal detected by the sensor of the analyte monitor according to the exemplary embodiment are associated with the analyte reference value (y) obtained simultaneously by the conventional analyte monitor.

[0098] For example, if the analyte of interest is glucose, a reference glucose concentration value (y) obtained from a conventional analyte sensor can be associated with the timestamp (x1), amplitude (x2), phase difference (x3), in-phase component (x4), out-of-phase component (x5), and frequency (x6) of an acoustic signal response obtained via an analyte monitor according to an exemplary embodiment. It should be noted that the above list of characteristics is not an exhaustive list, and other characteristics of the acoustic signal response can additionally or alternatively be associated with the reference glucose concentration value (y) obtained from a conventional analyte monitor.

[0099] After associating the features of the acoustic response (x1, x2, x3, x4, x5, and x6) with the reference data (y), the method proceeds to step S803 where this data is used to construct a labeled training data set using conventional techniques.

[0100] exist Figure 9 In another flow chart S900, how training data is collected by an analyte monitor according to an exemplary embodiment as described herein. In step S901, a user's tissue is stimulated via a light pulse from a light emitter. The method then proceeds to step S902.

[0101] In step S902 , an acoustic signal is generated by an analyte in the tissue in response to a light pulse.

[0102] In step S903, in the same manner as described above, the acoustic signal is detected by the sensor and converted into an electrical signal.

[0103] In step S904, the electrical signal is filtered to reduce the amount of noise in the signal. In one embodiment, this filtering is achieved by using a bandwidth filter. In one embodiment, this filtering is achieved via a time-based filtering technique, wherein a pulse reference signal is generated simultaneously with the emission of the light pulse in step S901, and wherein only acoustic signals received at a predetermined time after the pulse reference signal has been generated are allowed to pass through the signal filter. Additionally or alternatively, this signal filtering can utilize a timestamp associated with the pulse frequency of the light to filter out noise by comparing the timestamp of the emitted light with the timestamp of the acoustic signal response to the light. Also in step S904, the filtered electrical signal is amplified, for example, using an operational amplifier. The method then proceeds to step S905.

[0104] At step S905, a time series stationarity test is applied to the filtered, amplified electrical signal to test for noise in the signal caused by, for example, contact pressure and / or user motion. If the signal fails this time series stationarity test, the method proceeds to step S906, where the collected data is excluded from the training data set. If the signal passes this time series stationarity test, the method proceeds to step S907, where the collected data is included in the training data set for use in training an analyte monitor according to various embodiments as described herein.

[0105] Method S900 is repeated until enough data is collected to form a sufficiently large training data set for training the algorithm used by the signal processor.

[0106] Figure 10 A detailed flow chart S1000 illustrating exemplary steps involved in training an algorithm used in a digital signal processor according to step S702 of method S700 is shown. In step S1001, a labeled training data set is completed. The method then proceeds to step S1002.

[0107] In step S1002, a supervised learning method is used to train the algorithm used by the digital signal processor on the labeled training data set. More specifically, the algorithm used by the digital signal processor uses the features (x1), (x2), (x3), (x4), (x5) and (x6) of the electrical signal derived from the received acoustic response, and estimates the glucose concentration level based on these features. The estimated glucose concentration level is then compared with the obtained glucose concentration level (y) corresponding to those features obtained from a conventional analyte monitor. Based on the accuracy of the comparison, the parameters of the algorithm are adjusted. Statistical normalization and transformation techniques (such as linear regression techniques) can be used for this parameter adjustment.

[0108] After the parameters have been adjusted, the glucose concentration level is estimated again and compared to the obtained glucose concentration level (y) corresponding to those characteristics obtained from a conventional analyte monitor. In this way, according to various embodiments, based on the characteristics of the acoustic response signal obtained from the analyte monitor, the algorithm is iteratively trained to more accurately estimate the user's glucose concentration level. When the algorithm is fully trained, a more accurate personalized calibration for a specific user is achieved. Specifically, the analyte monitor according to the embodiments as described herein is able to better account for changes in the skin condition and characteristics of a specific user.

[0109] Figure 11 Shown according to Figure 7 Step S703 in FIG. 5 is a more detailed flowchart S1100 of using a trained analyte monitor in monitoring a user's analyte concentration level.

[0110] In step S1101, the trained analyte monitor obtains an acoustic response that is converted into an electrical signal having characteristics (x1, x2, x3, etc.) that can be used to estimate the user's analyte concentration.The method then proceeds to step S1102.

[0111] At step S1102, the analyte concentration level is estimated using the features of the electrical signal using the trained algorithm of the digital signal processor module.After estimating the analyte concentration level, the method proceeds to step S1103.

[0112] In step S1103, the estimated analyte concentration level is sorted into "bins," with each bin corresponding to the analyte concentration level for a particular user state. For example, the estimated analyte concentration level is a blood glucose concentration level, and the estimated glucose concentration level is sorted into one of three bins, a first bin having a range below approximately 100 mg / dl and corresponding to a hypoglycemic state, a second bin having a range between approximately 100 mg / dl and 200 mg / dl and corresponding to a "normal" state, and a third bin having a range above approximately 200 mg / dl and corresponding to a hyperglycemic state. In an exemplary embodiment, the endpoints of the bin ranges are variable and can vary based on the glycemic characteristics of a particular user.

[0113] In an exemplary embodiment, a trained algorithm of a digital signal processor is used to estimate the probability that the estimated blood glucose level falls within a specific interval based on the characteristics of the electrical signal derived from the acoustic response. In this way, a probabilistic confidence level for the "binning" step can be determined. In an exemplary embodiment, multiple estimates of blood glucose levels can be performed, and the confidence level can be iteratively reassessed based on each subsequent estimate. In this way, a high confidence level for the binning of blood glucose concentration estimates can be achieved.

[0114] After binning the analyte concentration estimates, the method proceeds to step S1104. In step S1104, the digital signal processor's algorithm outputs a "state" corresponding to the estimated analyte concentration level. For example, if the estimated blood glucose concentration level is above 200 mg / dl, the algorithm will output a "hyperglycemic" state. After outputting this state, the method proceeds to step S1105.

[0115] In step S1105, an action is performed, if necessary, in response to the status output. For example, if the analyte monitor outputs a "hyperglycemic" status, an alarm may be transmitted to a remote device to alert the user of this status. Additionally or alternatively, a signal may be transmitted to an insulin pump to administer a specific amount of insulin in response to the status output. Additionally or alternatively, a signal may be transmitted to a display device to display the blood glucose concentration status.

[0116] To test the accuracy of glucose concentrations estimated using an analyte monitor according to an embodiment as described herein and a conventional continuous glucose monitor with a transdermal probe, glucose concentration measurements were simultaneously tested via a computer using a trained analyte monitor according to an embodiment and a transdermal continuous glucose monitor, and then compared to each other. The results of these comparisons were presented in Figure 12 As shown in the diagram 1200. Figure 12 As shown in , glucose concentrations estimated by a trained analyte monitor according to embodiments as described herein closely match glucose concentrations measured using a conventional, commercially available continuous glucose monitor with a transdermal probe.

[0117] Glucose concentration estimates were also obtained using a computer simulation of an untrained analyte monitor according to embodiments described herein and compared to glucose concentration estimates obtained using a prototype analyte monitor with a trained algorithm according to embodiments described herein. It was determined that the prototype untrained analyte monitor had a success rate of approximately 60% in determining hypoglycemic events, while the prototype trained analyte monitor had a success rate of over approximately 85% in determining hypoglycemic events, demonstrating the effectiveness of the machine learning training algorithm in improving the accuracy of glucose concentration estimates.

[0118] In addition, in vivo experiments were performed, e.g. Figure 13 As shown in the diagram 1300. Figure 13 As shown, according to an embodiment having a trained algorithm, there is a strong correlation between the sensor signal obtained from the analyte monitor and the known blood glucose concentration level obtained from the reference sensor, thereby demonstrating the accuracy of this method in detecting blood glucose concentration levels.

[0119] The various tasks performed in conjunction with the processes described herein may be performed by software, hardware, firmware, or any combination thereof. For illustrative purposes, the following description of process S700 may refer to the following description of process S700 in conjunction with the Figures 1 to 6 In practice, portions of process S700 may be performed by different elements of the described system (e.g., a digital signal processor module or a different controller module). It should be understood that process S700 may include any number of additional or alternative tasks, Figure 7 The tasks shown do not have to be performed in the order illustrated, and process S700 may be incorporated into a more comprehensive program or process having additional functionality not described in detail herein. Furthermore, various steps may be omitted from an implementation of process S700 as long as the intended overall functionality remains intact. Figure 7 One or more tasks as shown.

[0120] Calibration technology

[0121] It will be appreciated that tissue properties, such as skin water content, fibrosis, etc., will vary from user to user. It will also be appreciated that the skin properties of an individual user may change over a given period of time.

[0122] Changes in tissue skin properties cause a resultant change in the acoustic signal response caused by thermal excitation of the analyte molecules targeted by the emitted light. This change in the acoustic signal response can be independent of the concentration level of the analyte itself (e.g., glucose level or diabetic ketoacidosis). Therefore, this change in the acoustic signal response may lead to inaccurate determination of the analyte concentration by the above-described methods and systems.

[0123] To compensate for this variation in acoustic signal response, analyte monitors according to embodiments of the present invention may be calibrated based on additional techniques, which will be explained in more detail below.

[0124] Electrical impedance spectroscopy (EIS )

[0125] Electrical impedance spectroscopy is a technique used to monitor the concentration of an analyte (such as glucose) in a target. In this technique, a voltage is applied to the target using electrodes. The target's current response is then measured. Knowledge of the characteristics of the applied voltage and the resulting current response allows calculation of the tissue's impedance, which consists of both a real component (corresponding to the target's resistance) and an imaginary component (corresponding to the target's reactance).

[0126] EIS can be used to determine analyte concentration levels based on measurements of the target's impedance itself. However, in the methods described herein, EIS itself is not used directly to determine analyte concentration. Rather, in the methods described herein, EIS is used to determine the target's impedance level, which is then used to detect changes in the acoustic response caused by changes in the target that are unrelated to changing analyte concentration levels.

[0127] Using the example where the target is user tissue and the analyte of interest is glucose, it can be determined that increased water content in the user's skin results in a decrease in the acoustic signal response. It can also be determined that increased water content in the user's skin may cause a larger reactive component of the impedance determined by EIS, as higher water content generally results in higher skin capacitance. It can also be determined that changes in the target's pH level cause an increase or decrease in the acoustic signal response.

[0128] For the above example, measuring skin impedance using EIS while measuring the acoustic signal obtained from the photoacoustic technique detailed above allows for determining that the smaller acoustic response is not the result of a decrease in glucose concentration (which in some cases may require taking some additional action, such as prompting the user to increase their glucose concentration level), but rather the result of increased skin hydration.

[0129] In this way, the use of EIS can improve the accuracy of the above-mentioned analyte monitors.

[0130] Figure 14 A logic diagram illustrating an analyte monitor including additional components for performing EIS calibration is shown. Figure 14 As shown, analyte monitor 1400 includes a light emitter 1410 for emitting light (shown with a thin dashed line) toward a target 1412. In an exemplary embodiment, light emitter 1410 includes a light emitting diode (LED). In some embodiments, light emitter 1410 includes a laser chip. Light emitter controller module 1411 includes circuitry associated with light emitter 1410. In an exemplary embodiment, light emitter controller module 1411 is configured to control light emitter 1410 so that the pulses of light emitted by light emitter 1410 have a predetermined or variable pulse repetition frequency (PRF). In other words, light emitter controller module 1411 is suitable for modulating the frequency of the light pulses emitted by light emitter 1410. Preferably, light emitter controller module 1411 is configured to control light emitter 1410 to emit light pulses with a duration of about 500ns per pulse at a frequency of about 50kHz or higher. It has been found that this pulse duration and frequency achieve a good acoustic response for certain analytes of interest (e.g., glucose).

[0131] Analyte monitor 1400 also includes a sensor 1413, such as a microphone or a transducer, such as a piezoelectric transducer. Sensor 1413 is configured to detect acoustic waves (shown by a thick dashed line) emitted by thermal excitation of analyte molecules and volume expansion in target 1412 by light emitted from light emitter 1410, and to generate an electrical signal based on these acoustic waves.

[0132] In an exemplary embodiment, the sensor 1413 is operatively connected to the signal processor 1420. Figure 14 In the illustrated embodiment, signal processor 1420 includes an operational amplifier ("op-amp") 1415 that is configured to further amplify the electronic signal derived from the acoustic response of target 1412. In an exemplary embodiment, op-amp 1415 is operably connected to an analog-to-digital converter 1416 that is configured to convert the analog electrical signal from sensor 1413 into a digital signal. In an exemplary embodiment, analog-to-digital converter 1416 is operably connected to a digital signal processor ("DSP") 1417 for processing the digital signal. It should be understood that the above-described components 1410, 1411, 1413, and 1420 of analyte monitor 1400 can be contained within a single housing or can be contained within separate housings.

[0133] In an exemplary embodiment, op-amp 1415 is adjustable to adjust the gain or amplification of the operational amplifier. In this way, the amplification of the electronic signal derived from the acoustic response of target 1412 can be adjusted as needed to ensure that the electronic signal is large enough to be accurately measured without causing saturation of the electronic signal.

[0134] The analyte monitor also includes a first electrode 1450 and a second electrode 1460. The first electrode 1450 and the second electrode 1460 are operably connected to a power supply 1470 and a voltage controller 1480. The power supply 1470 and the voltage controller 1480 are configured together to supply a voltage to the first electrode 1450 and the second electrode 1460. In an exemplary embodiment, this voltage has the form of an alternating current voltage. In an exemplary embodiment, the peak-to-peak voltage of the applied alternating current voltage is between about 0.1V and about 2V, preferably from about 0.2V to about 1.5V. The frequency of the alternating current can be from about 0.5kHz to about 3kHz, preferably from about 1kHz to about 2kHz. The first electrode 1450 and the second electrode 1460 can be spaced apart from each other by a distance of from about 0.1μm to about 5cm, preferably from about 0.5μm to about 4cm.

[0135] In use, the current response of the target to an applied alternating voltage is measured to determine the impedance of the target. The impedance measurement is then used to determine whether a decrease in the intensity of the acoustic response from thermal excitation of the analyte molecules is not due to a decrease in the concentration of the analyte molecules, but rather to some other property of the target that has changed, such as a change in the water content of the target.

[0136] In some embodiments, in order to correctly associate a specific measured impedance value with a specific characteristic of the target, a training period is implemented to associate the impedance measurement value with the expected skin characteristic change of the target. During this training period, the analyte monitor is non-invasively installed on the target (e.g., on the outside of the user's skin), and impedance measurements are obtained over a period of several hours or days, and the trend of the impedance measurements is monitored together with the photoacoustic method. When the analyte monitor is non-invasively installed on the target, electrodes 1450, 1460 can be placed in direct or indirect contact with the surface of the target. During this period, it can be determined that when the water content of the known skin increases or decreases, the measured impedance can change in a specific way. For example, using the example of the user's skin, it can be determined that when the water content of the expected skin decreases, the measured reactance of the user's skin decreases throughout the night. During this period, a set of impedance measurements and photoacoustic measurements are obtained, as well as target data from another technology (such as a transdermal continuous glucose monitor or via a finger prick test) for obtaining analyte concentration values. This set of measurements is divided into a calibration data set and a validation data set. The calibration data set is then analyzed using a processor module to fit the model to this calibration data set. After fitting the model, run it again using only the impedance and photoacoustic measurements from the validation dataset. Compare the results of this run to target data from the validation dataset, acquired using a different technique. In this way, the model can be trained to accurately predict analyte concentration values ​​based on the acoustic signal response and impedance measurements.

[0137] Figure 15 A method 1500 for training an analyte monitor comprising a photoacoustic sensor and electrodes for measuring target impedance via EIS is shown. Figures 7 to 11 A specific implementation of the method shown in FIG. 1 wherein the measured impedance is a characteristic “x n ”, this feature is considered together with the other features described in relation to this method.

[0138] The method begins at step S1510. At step S1510, the analyte monitor is mounted on a target (e.g., a user). The method then proceeds to step S1520. At step S1520, photoacoustic measurements and target impedance measurements are obtained over an initial calibration period, along with another technique for obtaining analyte concentration values ​​over this same period, such as a transdermal continuous glucose monitor (CGM) or "fingerstick" technology. These measurements are divided into a training data set and a validation data set. After obtaining the photoacoustic measurements, target impedance measurements, and analyte concentration measurements, the method proceeds to step S1530. At step S1530, a prediction model (e.g., as described above with reference to FIG. 5 ) is fitted using the training data set. Figures 7 to 11Specifically, the photoacoustic measurements and the target impedance measurements can be used as input vectors for the training dataset, and the analyte concentration measurements can be used as a series of targets for the training dataset. The predictive model assigns various weights to the input vectors to better fit the predictive model using conventional techniques such as variable selection and parameter estimation. After the predictive model has been fitted, the method moves to step 1540.

[0139] In step S1540, the fitted prediction model is used to predict target data based on the photoacoustic and impedance measurements of the validation dataset. These predicted targets are then compared to the analyte concentration values ​​of the validation dataset. If the fitted prediction model predicts the target within a predetermined accuracy threshold, the method proceeds to step S1540. If the fitted prediction model does not predict the target within the predetermined accuracy, the method returns to step S1510 for additional training and fitting.

[0140] At step S1540, the analyte monitor is assembled and analyte concentration values ​​are predicted based on future acquired photoacoustic measurements and target impedance measurements.Invasive techniques for measuring analyte concentration levels are no longer required.

[0141] Figure 16 A method 1600 is shown of estimating the analyte concentration level in a target using an analyte monitor of the form detailed above. The method begins at step S1610. At step S1610, the impedance of the target is measured using electrical impedance spectroscopy (EIS). The method then proceeds to step S1620. At step S1620, the target is illuminated with light of a first wavelength from a light emitter. Preferably, the light of the first wavelength has a wave number between about 1000 and about 1150. The method then proceeds to step S1630. At step S1630, a primary acoustic signal is obtained by a sensor, the primary acoustic signal being generated by the target in response to the illumination of the target by light from a light source. The method then proceeds to step S1640. At step S1640, the analyte concentration in the target is estimated based on both the primary acoustic signal obtained and the impedance of the measured target.

[0142] Thermal monitoring

[0143] Thermal monitoring can additionally or alternatively be used to calibrate the photoacoustic sensing technology of the analyte monitor. Specifically, measuring the thermal conductivity, specific heat capacity, and / or thermal resistance response of the target while using the above-described photoacoustic techniques can allow detection of changes in the acoustic response caused by changes in the target that are unrelated to changes in analyte concentration levels.

[0144] Using the example where the target is a user and the analyte of interest is glucose, it can be determined that increased water content in the user's tissue results in a decrease in the acoustic signal response from the above-described photoacoustic technique. It can also be determined that increased water content in the user's skin may result in, for example, a higher thermal conductivity of the target, since higher water content results in a decrease in thermal conductivity.

[0145] For the above example, measuring thermal properties simultaneously with measuring the acoustic signal obtained from the photoacoustic technique detailed above may allow for determining that a smaller (or larger) acoustic response is not the result of a corresponding decrease (or increase) in glucose concentration (which in some cases may require taking some additional action, such as prompting the user to raise their glucose concentration level), but rather is the result of a change in skin water content.

[0146] In this way, the analyte monitors described above may be made more accurate.

[0147] Figure 17 A logic schematic diagram illustrating an analyte monitor including additional elements for thermal property measurements is shown. Figure 17 As shown, analyte monitor 1700 includes a light emitter 1710 for emitting light (shown with a thin dashed line) toward a target 1712. In an exemplary embodiment, light emitter 1710 includes a light emitting diode (LED). In some embodiments, light emitter 1710 includes a laser chip. Light emitter controller module 1711 includes a circuit associated with light emitter 1710. In an exemplary embodiment, light emitter controller module 1711 is configured to control light emitter 1710 so that the pulses of light emitted by light emitter 1710 have a predetermined or variable pulse repetition frequency (PRF). In other words, light emitter controller module 1711 is suitable for modulating the frequency of the light pulses emitted by light emitter 1710. Preferably, light emitter controller module 1711 is configured to control light emitter 1710 to emit light pulses with a duration of about 500ns per pulse at a frequency of about 50kHz or higher. It has been found that this pulse duration and frequency achieve a good acoustic response for certain analytes of interest (e.g., glucose).

[0148] Analyte monitor 1700 also includes a sensor 1713, such as a microphone or a transducer, such as a piezoelectric transducer. Sensor 1713 is configured to detect acoustic waves (shown by a thick dashed line) emitted by thermal excitation of analyte molecules and volume expansion in target 1712 by light emitted from light emitter 1710, and to generate an electrical signal based on these acoustic waves (this electrical signal may be referred to as a 'primary acoustic signal').

[0149] In an exemplary embodiment, the sensor 1713 is operably connected to the signal processor 1720. Figure 17In the illustrated embodiment, signal processor 1720 includes an operational amplifier ("op-amp") 1715 that is configured to further amplify the electronic signal derived from the acoustic response of target 1712. In an exemplary embodiment, op-amp 1715 is operably connected to an analog-to-digital converter 1716 that is configured to convert the analog electrical signal from sensor 1713 into a digital signal. In an exemplary embodiment, analog-to-digital converter 1716 is operably connected to a digital signal processor ("DSP") 1717 for processing the digital signal. It should be understood that the above-described components 1710, 1711, 1713, and 1720 of analyte monitor 1700 can be contained within a single housing or can be contained within separate housings.

[0150] In an exemplary embodiment, op-amp 1715 is adjustable to adjust the gain or amplification of the operational amplifier. In this way, the amplification of the electronic signal derived from the acoustic response of target 1712 can be adjusted as needed to ensure that the electronic signal is large enough to be accurately measured without causing saturation of the electronic signal.

[0151] The analyte monitor also includes a heating element 1750. The heating element 1750 is connected to a power source 1770 and is configured to apply heat to the target. In an exemplary embodiment, the heating element 1750 includes a thermistor. The analyte monitor also includes a thermal sensor 1760. Figure 17 In the exemplary embodiment shown, a thermistor is used as both the heating element 1750 and the thermal sensor 1760. In some alternative examples, the thermal sensor is incorporated into or combined with the sensor 1713, or may be located elsewhere in the analyte monitor 1700 or separate from the analyte monitor. The heating element 1750 includes circuitry to control the application of heat to the target 1712. For example, the heating element 1750 may include a resistive element that generates heat when current passes through the resistive element. The internal circuitry of the heating element is configured to selectively control the application of current through the resistive element to cause heat emission from the resistive element. The internal circuitry of the heating element may be adjustable to increase or decrease the amount of current supplied to the resistive element, thereby allowing adjustment of the heat applied to the target by the heating element.

[0152] In use, the heating element 1750 applies heat to the target and the thermal sensor 1760 measures the thermal response of the target. The thermal response is then used to determine whether a decrease in the intensity of the acoustic response from the thermal excitation of the analyte molecules is not due to a decrease in the concentration of the analyte molecules, but rather to some other property of the target that has changed, such as a change in the water content of the target.

[0153] In some embodiments, the specific heat property determined is the specific heat capacity of the target. In some embodiments, the specific heat property determined is the thermal conductivity of the target. Both specific heat capacity and thermal conductivity are related to the water content of the target. In order to determine these thermal properties based on the thermal response of the target, a thermal pulse decay (TPD) technique can be used. In this technique, a thermistor is first used to apply heat to the target for a predetermined period of time, for example, between about 1 second and about 5 seconds. Subsequently, after heating stops, the temperature decay is measured using the same thermistor. According to Fourier's law that the local heat flux density is proportional to the product of thermal conductivity and the negative temperature gradient, the rate of temperature decay measured allows the thermal conductivity of the target to be determined.

[0154] In some embodiments, in order to correctly associate a particular measured thermal response value with a particular characteristic of a target, a training period is implemented to associate the thermal response measurements with changes in the characteristic of the target. During this training period, the analyte monitor is mounted on the target and thermal response measurements are obtained over a period of hours or days and the trends of the thermal response measurements are monitored in conjunction with the photoacoustic method. Using the example where the target is the user's tissue and the thermal response measured is thermal conductivity, it can be determined during this time period that the measured thermal response can change in a particular manner when the water content of the skin is known to increase or decrease. For example, it can be determined that the measured thermal conductivity of the user's skin increases throughout the night when the water content of the skin is expected to decrease. During this time period, the thermal conductivity of the user's skin is monitored in a manner similar to that described above with respect to the water content of the skin. Figures 7 to 11 The same approach is used to obtain training and validation data sets of thermal response measurements and photoacoustic measurements, along with target data from another technique for obtaining analyte concentration values, such as a transdermal continuous glucose monitor or via a finger stick test, where the measured thermal response is the feature “x n ”, this feature is considered together with the other features described in relation to this method.

[0155] After fitting the model, the model is run using the photoacoustic and thermal response measurements from the validation data set. The results of this run are compared with target data obtained using a different technique. In this way, the model can be trained to accurately predict analyte concentration values ​​based on the acoustic signal response and thermal response measurements. In an exemplary embodiment, the fitted model is stored on the DSP 1717.

[0156] Figure 18A method 1800 for training an analyte monitor is shown, the analyte monitor comprising a photoacoustic sensor, a heating element, and a thermal sensor. The method begins at step S1810. At step S1810, the analyte monitor is mounted on a target, such as placed on the skin of a user. The method then proceeds to step S1820. At step S1820, photoacoustic measurements and thermal response measurements are obtained over an initial calibration period, as well as another technique for obtaining analyte concentration values ​​over this same period. These measurements are divided into a training data set and a validation data set. After obtaining the photoacoustic measurements, thermal response measurements, and analyte concentration measurements, the method proceeds to step S1820. At step S1820, a prediction model is fitted using the training data set. Specifically, the photoacoustic measurements and thermal response measurements can be used as input vectors for the training data set, and the analyte concentration measurements can be used as a series of targets for the training data set. The prediction model assigns various weights to the input vectors so that the prediction model can be used as described above with respect to Figures 7 to 11 The described technique fits the prediction model better.After the prediction model has been fitted, the method moves to step S1830.

[0157] In step S1830, the fitted prediction model is used to predict targets based on the photoacoustic and thermal response measurements of the validation dataset. These predicted targets are then compared to the analyte concentration values ​​of the validation dataset. If the fitted prediction model predicts the target within a predetermined accuracy threshold, the method proceeds to step S1840. If the fitted prediction model does not predict the target within the predetermined accuracy, the method returns to step S1810 for additional training and fitting.

[0158] At step S1840, the analyte monitor is assembled and analyte concentration values ​​are predicted based on future photoacoustic and thermal response measurements. Invasive analyte concentration monitoring techniques are no longer required.

[0159] Figure 19A method 1900 for estimating the analyte concentration level in a target using an analyte monitor in the form detailed above is shown. The method begins at step S1910. In step S1910, heat is applied to the target using a heating element, and the thermal response of the target is measured using a thermal sensor. In one embodiment, a thermistor is used as both the heating element and the thermal sensor. In an alternative embodiment, a separate heating element (such as a heating coil) and a thermal sensor are provided. The method then proceeds to step S1920. In step S1920, the target is illuminated with light of a first wavelength from a light emitter. Preferably, the light of the first wavelength has a wave number between approximately 1000 and 1150. The method then proceeds to step S1930. In step S1930, a primary acoustic signal is generated by the sensor, which is generated by the sensor based on the acoustic waves generated by the target in response to the illumination of the target by light from the light source. The method then proceeds to step S1940. In step S1940, the analyte concentration in the target is estimated based on both the obtained primary acoustic signal and the measured thermal response of the target.

[0160] Background absorption level

[0161] Techniques for determining background levels of light absorption can additionally or alternatively be used to calibrate the photoacoustic sensing techniques of the analyte monitor. Specifically, measuring background light absorption levels while using the above-described photoacoustic techniques can allow detection of changes in the acoustic response caused by changes in the target that are unrelated to changes in analyte concentration.

[0162] Using the example of a user as the target and glucose as the analyte of interest, it can be determined that changes in the water content, fibrosis structure, or keratin levels of the user's tissue cause changes in the acoustic signal response from the photoacoustic technique, even when the analyte concentration level remains unchanged. By accounting for these changes in the acoustic signal response, the analyte monitor can be made more accurate.

[0163] Figure 20 A logic schematic diagram illustrating an analyte monitor including additional components for measuring background light absorption levels is shown. Figure 20As shown, analyte monitor 2000 includes a light emitter 2010 for emitting light (shown with a thin dashed line) toward target 2012. In one embodiment, light emitter 2010 is configured to emit light of multiple wavelengths. In one embodiment, light emitter 2010 includes a tunable laser. In an alternative embodiment, light emitter 2010 includes a plurality of light sources, each of which is configured to emit light of one or more wavelengths (e.g., a single wavelength or different wavelengths). Light emitter controller module 2011 includes a circuit associated with light emitter 2010. In an exemplary embodiment, light emitter controller module 2011 is configured to control light emitter 2010 so that the pulse of light emitted by light emitter 2010 has a predetermined or variable pulse repetition frequency (PRF). In other words, light emitter controller module 2011 is suitable for modulating the frequency of the light pulses emitted by light emitter 2010.

[0164] Analyte monitor 2000 also includes a sensor 2013, such as a microphone or a transducer, such as a piezoelectric transducer. Sensor 2013 is configured to detect acoustic waves (shown by a thick dashed line) emitted by thermal excitation of analyte molecules and volume expansion in target 2012 by light emitted from light emitter 2010, and to generate an electrical signal based on these acoustic waves.

[0165] In an exemplary embodiment, the sensor 2013 is operatively connected to the signal processor 2020. Figure 20 In the illustrated embodiment, signal processor 2020 includes an operational amplifier ("op-amp") 2015 that is configured to further amplify the electronic signal derived from the acoustic response of target 2012. In an exemplary embodiment, op-amp 2015 is operably connected to an analog-to-digital converter 2016 that is configured to convert the analog electrical signal from sensor 2013 into a digital signal. In an exemplary embodiment, analog-to-digital converter 2016 is operably connected to a digital signal processor ("DSP") 2017 for processing the digital signal. It should be understood that the above-mentioned components 2010, 2011, 2013, and 2020 of analyte monitor 2000 can be contained within a single housing or can be contained within different housings.

[0166] In an exemplary embodiment, op-amp 2015 is adjustable to adjust the gain or amplification of the operational amplifier. In this way, the amplification of the electronic signal derived from the acoustic response of target 2012 can be adjusted as needed to ensure that the electronic signal is large enough to be accurately measured without causing saturation of the electronic signal.

[0167] In use, light emitter 2010 is configured to first illuminate a target with light of a second wavelength. The second wavelength of light has a wavelength that does not strongly interact with the analyte of interest. For example, if the analyte of interest is glucose, the second wavelength may have a wavelength from approximately 3,000 nm to approximately 7,000 nm. An acoustic signal response from the target is then obtained from the photoacoustic effect generated by illuminating the target with light of the second wavelength. After obtaining the acoustic signal response generated by illuminating the target with light of the second wavelength, a signal processor determines a background absorption level based on the acoustic signal response.

[0168] After determining the background absorption level based on the acoustic signal response associated with illuminating the target with light of the second wavelength, light emitter 2010 is configured to illuminate the target with light of a first wavelength that strongly interacts with the analyte of interest. For example, if the analyte of interest is glucose, the wavelength of the first wavelength may be a value from approximately 8,700 nm to approximately 10,000 nm. The acoustic signal response generated in response to illumination with light of this second wavelength is then measured. By considering the measured value of the acoustic signal response generated in response to illumination of the target with light of the second wavelength to determine the estimated background absorption level of light, the analyte concentration can be more accurately determined based on the primary acoustic signal generated based on the acoustic waves generated by the target in response to illumination of the target with light of the first wavelength.

[0169] Figure 21 A method 2100 for estimating the analyte concentration level in a target using an analyte monitor of the form detailed above is shown. The method begins at step S2110. At step S2110, the target is illuminated with light of a second wavelength and a secondary acoustic signal response is obtained. The method then proceeds to step S2120. At step S2120, a background absorption level of light is estimated based on the secondary acoustic signal response. The method then proceeds to step S2130.

[0170] At step S2130, the target is illuminated with light of a first wavelength and a primary acoustic signal response is obtained. Preferably, the light of a second wavelength has a wave number between about 1000 and 1150. The method then proceeds to step 2140. At step 2140, the analyte concentration in the target is estimated based on both the obtained second acoustic signal and the estimated background absorption level of the light.

[0171] The inventors have also realised that the acoustic waves generated in response to the two different wavelengths of light have the same wavelength. Therefore, the resonant chamber detailed above will be used to amplify both acoustic waves, thereby advantageously allowing an inexpensive sensor to detect both acoustic waves.

[0172] Alternatively, light of the first wavelength and the second wavelength can be simultaneously applied to the target in pulses having different pulse repetition frequencies (PRFs). The pulse repetition frequency of the secondary acoustic signal corresponding to the background absorption level will correspond to the pulse repetition frequency of the pulses of the second wavelength of light. The pulse repetition frequency of the primary acoustic signal will correspond to the pulse repetition frequency of the pulses of the first wavelength of light. By separating the acoustic signals based on their different pulse repetition frequencies, the secondary acoustic signal response can be used to estimate the background absorption level while simultaneously obtaining the primary acoustic signal response.

[0173] In an exemplary embodiment, the pulse repetition frequency and / or the first wavelength and second wavelength of light are variable. This can be achieved by using one or more tunable lasers as light emitters 2010. By allowing changes in the first wavelength and second wavelength of light, the analyte monitor 2000 can be adapted to detect different analytes of interest. For example, if the first analyte of interest is glucose, the wavelength of the first wavelength of light can be selected to be a value from about 8,700nm to about 10,000nm, which interacts strongly with glucose molecules, and the second wavelength of light can be selected to be from about 2,500nm to about 3,000nm, which interacts relatively less strongly with glucose molecules. If, after monitoring the concentration level of glucose, it is desired to monitor a second analyte of interest, the first wavelength and the second wavelength can be changed accordingly. For example, if the second analyte of interest is a ketone, the wavelength of the first wavelength of light can be changed to a value from about 2,000 nm to about 2,500 nm, which interacts strongly with ketone bodies, and the second wavelength of light can be changed to a value from about 5,000 nm to about 7,000 nm, which interacts relatively less strongly with ketone bodies.

[0174] In this way, the analyte monitor can be adapted to accurately determine the concentration levels of different analytes of interest.

[0175] Combination of calibration techniques

[0176] It should be understood that the above three calibration techniques can work together to further improve the accuracy of the estimated analyte concentration measurement value. For example, the analyte monitor can include a thermistor for monitoring the thermal response of the target and an electrode probe for performing EIS measurement on the target. Each of the measured thermal response and the measured target impedance will be considered as a feature of the training model. As another example, the analyte monitor can include a thermistor for monitoring the thermal response of the target and a light emitter capable of emitting multiple wavelengths of light to estimate the background absorption level. Each of the measured thermal response and the estimated background absorption level will be considered as a feature of the training model. As another example, the analyte monitor can include a light emitter capable of emitting multiple wavelengths of light to estimate the background absorption level and an electrode probe for performing EIS measurement on the target. Each of the estimated background absorption level and the measured target impedance will be considered as a feature of the training model. As another example, the analyte monitor can include all of the following: a thermistor for monitoring the thermal response of the target, a light emitter capable of emitting multiple wavelengths of light to estimate the background absorption level, and an electrode probe for performing EIS measurement on the target. All measured thermal responses, estimated background absorption levels and measured target impedance will be considered as features for training the model. By combining all measured data, the above model can be used as the above Figures 8 to 11 Specifically, including multiple different data sets can be used to Figures 7 to 11 The described approach improves the training and validation datasets for photoacoustic measurements, where each sensor dataset is a separate feature "x n ”, this feature is considered together with the other features described in relation to this method.

[0177] In one embodiment, each of the above-mentioned technologies and combinations of technologies corresponds to a mode that can be selected by a user of the analyte monitor. For example, the analyte monitor may have seven modes: (1) a mode corresponding to the thermal response of the measurement target; (2) a mode corresponding to the background absorption level of the measurement target; (3) a mode corresponding to the electrical impedance of the measurement target; (4) a mode corresponding to the thermal response of the measurement target and the background absorption level of the measurement target; (5) a mode corresponding to the thermal response of the measurement target and the electrical impedance of the measurement target; (6) a mode corresponding to the background absorption level of the measurement target and the electrical impedance of the measurement target; (7) a mode corresponding to the thermal response of the measurement target, the background absorption level of the measurement target, and the electrical impedance of the measurement target. It should be understood that the analyte monitor may include less than seven modes, corresponding to the selection of various modes in the modes listed above. During the selection of a mode using multiple technologies, the components involved in each technology may be used simultaneously, in parallel, or sequentially.

[0178] For example, Figure 22 Shown is an upward view of an analyte monitor 2200, which includes features that allow the execution of mode (7) listed above, in which all three of the above-mentioned calibration techniques are used. Specifically, the analyte monitor includes a thermal sensor 2210 for measuring the thermal response of the target. In some examples, the thermal sensor 2210 is a thermistor. The analyte monitor also includes a first electrode 2220 and a second electrode 2230 for applying a voltage to measure the impedance of the target. The analyte monitor 2200 also includes a photoacoustic window 2240, through which light is emitted to be incident on the target. The analyte monitor 2200 includes a light emitter capable of emitting two different wavelengths of light to estimate the background absorption level. In an exemplary embodiment, these three calibration techniques are performed simultaneously, in parallel or continuously, and the model is fitted with the data obtained from each of these calibration techniques.

[0179] Reverse iontophoresis

[0180] Techniques according to the present disclosure can be used to increase the strength of the acoustic signal received by an analyte monitor.One such technique is to use reverse iontophoresis.

[0181] Reverse iontophoresis can be used as a technique to estimate analyte concentration levels. Specifically, reverse iontophoresis can be used to extract an analyte of interest through a user's skin by applying an electric field to the user, which is then drawn out via electroosmotic flow, and has previously been demonstrated for monitoring glucose concentration levels.

[0182] However, the present inventors have recognized that the inherent effect of reverse iontophoresis, which pulls glucose molecules toward the surface of a user's skin, can be used to increase the strength of the acoustic signal obtained by illuminating the user's skin with light.

[0183] Specifically, by bringing the analyte of interest closer to the user's skin, any acoustic signal response generated by the analyte of interest in response to illumination with light is less likely to be attenuated by other structures or molecules in the user's skin. Thus, reverse iontophoresis itself is not used to monitor glucose concentration levels, but rather to increase the signal strength obtained by the aforementioned photoacoustic technique.

[0184] Figure 23 A diagram illustrating reverse iontophoresis is shown. As shown, two electrodes 2301 and 2302 are placed in contact with a user's skin 2305. Anode electrode 2301 pulls negative ions, such as chloride ions, toward the user's skin. Cathode electrode 2302 pulls positive ions, such as sodium ions, toward the user's skin. The flow of sodium ions pulls water molecules toward the cathode. Under osmotic pressure, glucose molecules are also pushed toward the cathode.

[0185] By positioning the cathode close to the area of ​​the user's skin illuminated with light, the intensity of the acoustic signal response from the glucose molecules is increased. This increased acoustic signal response intensity has an improved signal-to-noise ratio, thereby improving subsequent estimation of the analyte concentration level.

[0186] Figure 24 1 shows a logic schematic diagram of an analyte monitor including electrodes for performing reverse iontophoresis while performing the photoacoustic technique described above. Figure 24 As shown, analyte monitor 2400 includes a light emitter 2410 for emitting light (shown with a thin dashed line) toward a target 2412. In an exemplary embodiment, light emitter 2410 includes a light emitting diode (LED). In an alternative embodiment, light emitter 2410 includes a laser chip. Light emitter controller module 2411 includes circuitry associated with light emitter 2410. In an exemplary embodiment, light emitter controller module 2411 is configured to control light emitter 2410 so that the pulses of light emitted by light emitter 2410 have a predetermined or variable pulse repetition frequency (PRF). In other words, light emitter controller module 2411 is suitable for modulating the frequency of the light pulses emitted by light emitter 2410. In some examples, light emitter controller module 2411 is configured to control light emitter 2410 to emit light pulses with a duration of from about 400ns to about 600ns (e.g., about 500ns) per pulse at a frequency of from about 40kHz to about 60kHz (e.g., about 50kHz). This pulse duration and frequency has been found to achieve a good acoustic response for certain analytes of interest (eg, glucose).

[0187] Analyte monitor 2400 also includes a sensor 2413, such as a microphone or a transducer, such as a piezoelectric transducer. Sensor 2413 is configured to detect acoustic waves (shown by a thick dashed line) emitted by thermal excitation of analyte molecules and volume expansion in target 2412 by light emitted from light emitter 2410, and to generate an electrical signal based on these acoustic waves.

[0188] The analyte monitor also includes a first electrode 2450 and a second electrode 2460. The first electrode and the second electrode are connected to a power supply and a voltage controller (not shown). The voltage controller is configured to bias the first electrode and the second electrode. In one embodiment, the first electrode is negatively biased by the voltage controller to act as a cathode (e.g., as described above with reference to Figure 23 ), and the second electrode is positively biased by a voltage controller to act as an anode (e.g., as described above with reference to Figure 23 The cathode is positioned close to an area on the target illuminated by light emitted from the light emitter 2410.

[0189] In use, glucose molecules are drawn in the manner described above towards areas on the target illuminated by light emitted from the light emitter 2410. In this way, an increase in the amplitude of the acoustic signal generated by illuminating the target with light is obtained.

[0190] As will be understood by those skilled in the art, if the analyte monitor 2400 is used to monitor the concentration level of another analyte of interest instead of glucose, the positions of the first electrode 2450 and the second electrode 2460 may be reversed if the analyte of interest will be drawn toward the anode.

[0191] In one embodiment, the first electrode 2450 and the second electrode 2460 are spaced apart by a distance of less than about 3 cm. In one embodiment, the first electrode 2450 and the second electrode 2460 include a hydrogel pad on their surface that is configured to contact the target surface. Alternatively, the first electrode 2450 and the second electrode 2460 can be bare metal electrodes, the metal of which is configured to contact the target surface. Further alternatively, the first electrode and the second electrode can be needle electrodes (e.g., microneedles).

[0192] In one embodiment, the current density of the current applied by the first electrode and the second electrode can be varied to ensure that the current density falls below the user's pain threshold. For example, the current density can be set to approximately 1 mA / cm 2 .

[0193] Figure 25 A method 2500 for estimating the analyte concentration level in a target using an analyte monitor of the form detailed above is shown. The method begins at step S2510. At step S2510, a potential bias is applied to the target using electrodes and a voltage controller. The method then proceeds to step S2520. At step S2520, the target is illuminated with light of a first wavelength and a primary acoustic signal response is obtained. The method then proceeds to step S2530.

[0194] In step S2530 , the analyte concentration in the target is estimated based on the obtained primary acoustic signal.

[0195] Other analytes of interest

[0196] In addition to monitoring glucose, the above techniques have been found to be effective in monitoring other analytes of interest, such as ketones. Ketone bodies are a marker for diabetic ketoacidosis (DKA), a life-threatening condition caused by insulin deficiency or elevated blood glucagon levels. By selecting the first wavelength of light emitted by the light emitter to be a wavelength that strongly interacts with ketone molecules (e.g., between about 2000 nm and about 2500 nm), the above techniques can be applied to estimate the concentration level of ketone bodies.

[0197] Although at least one exemplary embodiment has been presented in the foregoing detailed description, it should be understood that there are a large number of variations. It should also be understood that the one or more exemplary embodiments described herein are not intended to limit the scope, applicability, or configuration of the claimed subject matter in any way. On the contrary, the foregoing detailed description will provide a convenient roadmap for those skilled in the art to implement the one or more described embodiments. It should be understood that various changes may be made to the function and arrangement of the elements, including known equivalents and foreseeable equivalents at the time of filing this patent application, without departing from the scope defined by the claims.

Claims

1. A photoacoustic method for estimating an analyte concentration level in a target, the method comprising: measuring the impedance of the target via electrical impedance spectroscopy with control circuitry enclosed in a housing of the analyte monitor; illuminating the target with light of a first wavelength using a light source enclosed in a housing of the analyte monitor; obtaining, with an acoustic sensor and a resonant chamber enclosed in a housing of the analyte monitor, a primary acoustic signal generated by the target in response to the illumination of the target with light of the first wavelength, wherein both the light source and the acoustic sensor face a surface of the target, and wherein the acoustic sensor extends at least partially into the resonant chamber; as well as An analyte concentration level in the target is estimated with the analyte monitor based on both the obtained primary acoustic signal and the measured impedance of the target.

2. The photoacoustic method according to claim 1, further comprising: applying heat to the target using a heating element of the analyte monitor; A thermal response of the target to the applied heat is measured with a thermal sensor of the analyte monitor, wherein estimating the analyte concentration level includes additionally estimating the analyte concentration level in the target based on the measured thermal response.

3. The photoacoustic method according to claim 1 or 2, further comprising: illuminating the target with light of a second wavelength different from the first wavelength with the light source; obtaining, with the acoustic sensor, a secondary acoustic signal generated by the target in response to the illumination of the target with light of the second wavelength; A background absorption level of light is estimated based on the obtained secondary acoustic signal, wherein estimating the analyte concentration level in the target includes estimating the analyte concentration level additionally based on the estimated background absorption level of light. 4 . The method of claim 1 , wherein the impedance of the target is measured non-invasively using a first electrode and a second electrode, the first electrode and the second electrode being positioned in direct or indirect contact with a surface of the target. 5 . The method of claim 4 , wherein a hydrogel pad is disposed on a surface of each of the first electrode and the second electrode, the hydrogel pad being configured to directly contact the surface of the target.

6. The method of claim 4 or 5, wherein the electrodes are spaced apart by a distance of 0.1 cm to 5 cm. 7 . The method of claim 3 , wherein the irradiating the target with light of the first wavelength and the irradiating the target with light of the second wavelength are performed at different times.

8. The method of claim 3 , wherein the irradiating the target with the light of the first wavelength and the light of the second wavelength is performed simultaneously by pulse-modulating the light of the first wavelength at a first pulse frequency and simultaneously pulse-modulating the light of the second wavelength at a second pulse frequency, the first pulse frequency being different from the second pulse frequency.

9. The method of claim 3, wherein the first wavelength is selected such that the first wavelength has a relatively stronger interaction with the analyte than the second wavelength has with the analyte.

10. The method of claim 3, wherein the first wavelength has a value of about 8,700 nm to about 10,000 nm. The method of claim 3 , wherein the first wavelength has a value of about 2,000 nm to about 2,500 nm.

12. The method of claim 3, wherein the first wavelength is variable.

13. The method of claim 3, wherein the second wavelength is variable. The method of claim 2 , wherein the thermal response measured is thermal conductivity.

15. The method of claim 2 or 14, wherein a thermistor comprises both the heating element and the thermal sensor.

16. The method of claim 2 or 14, wherein the thermal response measured is specific heat capacity.

17. A photoacoustic method for estimating an analyte concentration level in a target, the method comprising: applying heat to the target using a heating element and control circuitry enclosed in a housing of the analyte monitor; measuring a thermal response of the target to the applied heat with a thermal sensor and control circuitry enclosed in a housing of the analyte monitor; illuminating the target with light of a first wavelength using a light source enclosed in a housing of the analyte monitor; obtaining, with an acoustic sensor and a resonant chamber enclosed in a housing of the analyte monitor, a primary acoustic signal generated by the target in response to the illumination of the target with light of the first wavelength, wherein both the light source and the acoustic sensor face a surface of the target, and wherein the acoustic sensor extends at least partially into the resonant chamber; as well as An analyte concentration level in the target is estimated with control circuitry of the analyte monitor based on both the obtained primary acoustic signal and the measured thermal response.

18. The photoacoustic method according to claim 17, further comprising: illuminating the target with light of a second wavelength different from the first wavelength with the light source; obtaining, with the acoustic sensor, a secondary acoustic signal generated by the target in response to the illumination of the target with light of the second wavelength; An estimated background absorption level of light is determined based on the obtained secondary acoustic signal, wherein estimating the analyte concentration level in the target includes estimating the analyte concentration level in the target also based on the estimated background absorption level of light.

19. The method of claim 18, wherein the irradiating the target with light of the first wavelength and the irradiating the target with light of the second wavelength are performed at different times.

20. The method of claim 18, wherein the irradiating the target with the light of the first wavelength and the light of the second wavelength is performed simultaneously by pulsing the light of the first wavelength at a first pulse frequency and simultaneously pulsing the light of the second wavelength at a second pulse frequency, the first pulse frequency being different from the second pulse frequency.

21. The method of claim 18, wherein the first wavelength is selected such that the first wavelength has a relatively stronger interaction with the analyte than the second wavelength has with the analyte.

22. The method of claim 18, wherein the first wavelength has a value of about 8,700 nm to about 10,000 nm.

23. The method of claim 18, wherein the first wavelength has a value of about 2,000 nm to about 2,500 nm.

24. The method of claim 18, wherein the first wavelength is variable.

25. The method of claim 18, wherein the second wavelength is variable.

26. The method of claim 17, wherein the thermal response measured is thermal conductivity.

27. The method of claim 17 or 26, wherein a thermistor comprises both the heating element and the thermal sensor.

28. The method of claim 17 or 26, wherein the thermal response measured is specific heat capacity.

29. A photoacoustic method for estimating an analyte concentration level in a target, the method comprising: illuminating the target with light of a first wavelength; obtaining, with an acoustic sensor and a resonant chamber, a primary acoustic signal generated by the target in response to the irradiation of the target with light of the first wavelength, wherein the acoustic sensor faces a surface of the target, and wherein the acoustic sensor extends at least partially into the resonant chamber; illuminating the target with light of a second wavelength; obtaining, with another acoustic sensor, a secondary acoustic signal generated by the target in response to the illumination of the target with light of the second wavelength; determining an estimated background absorption level of light based on the obtained secondary acoustic signal; and An analyte concentration level in the target is estimated based on both the obtained primary acoustic signal and the estimated background absorption level of light.

30. The method of claim 29, wherein the irradiating the target with light of the first wavelength is performed at different times than the irradiating the target with light of the second wavelength.

31. The method of claim 29, wherein the irradiating the target with light of the first wavelength and light of the second wavelength is performed simultaneously by pulsing the light of the first wavelength at a first pulse frequency and simultaneously pulsing the light of the second wavelength at a second pulse frequency, the first pulse frequency being different from the second pulse frequency.

32. The method of claim 29, 30 or 31, wherein the first wavelength is selected such that the first wavelength has a relatively stronger interaction with the analyte than the second wavelength has with the analyte.

33. The method of claim 29, 30 or 31, wherein the first wavelength has a value of about 8,700 nm to about 10,000 nm.

34. The method of any one of claims 29 to 31, wherein the first wavelength has a value of about 2,000 nm to about 2,500 nm.

35. The method of any one of claims 29 to 31 , wherein the first wavelength is variable.

36. A method according to any one of claims 29 to 31 , wherein the second wavelength is variable.

37. An analyte monitor, comprising: an electrical impedance spectroscopy device enclosed in the housing of the analyte monitor, the electrical impedance spectroscopy device being configured to measure impedance of a target; a light source enclosed in the housing, the light source being configured to illuminate the target with light of a first wavelength; an acoustic sensor and a resonant chamber enclosed in the housing, the acoustic sensor and resonant chamber for obtaining a primary acoustic signal generated by the target in response to the illumination of the target with light of the first wavelength, wherein both the light source and the acoustic sensor face a surface of the target, and wherein the acoustic sensor extends at least partially into the resonant chamber; as well as A processor is configured to estimate an analyte concentration level in the target based on both the obtained primary acoustic signal and the measured impedance of the target.

38. An analyte monitor, comprising: a heating element enclosed in a housing of the analyte monitor, the heating element for applying heat to a target; a thermal sensor for measuring a thermal response of the target to the applied heat; a light source enclosed in the housing, the light source being configured to illuminate the target with light of a first wavelength; an acoustic sensor and a resonant chamber enclosed in the housing, the acoustic sensor and resonant chamber for obtaining a primary acoustic signal generated by the target in response to the illumination of the target with light of the first wavelength, wherein both the light source and the acoustic sensor face a surface of the target, and wherein the acoustic sensor extends at least partially into the resonant chamber; as well as A processor is configured to estimate an analyte concentration level in the target based on both the obtained primary acoustic signal and the measured thermal response.

39. An analyte monitor, comprising: at least one light source enclosed in the housing of the analyte monitor, the at least one light source for illuminating a target with light of a first wavelength; an acoustic sensor and a resonant chamber, the acoustic sensor for obtaining a primary acoustic signal generated by the target in response to the irradiation of the target with light of the first wavelength, wherein the acoustic sensor faces a surface of the target, and wherein the acoustic sensor extends at least partially into the resonant chamber; the at least one light source, the at least one light source being configured to illuminate the target with light of a second wavelength; another acoustic sensor for obtaining a secondary acoustic signal generated by the target in response to the illumination of the target with light of the second wavelength; A processor is configured to determine an estimated background absorption level of light based on the obtained secondary acoustic signal, and to estimate an analyte concentration level in the target based on both the obtained primary acoustic signal and the estimated background absorption level of light.

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