Patent Application
20210379370
Not Applicable
The present invention generally relates to analyte-selective sensors and methods for configuration of the same, and a microneedle applicator integrated internally into a wearable sensor body housing.
The continuous assessment of circulating glucose levels remains of pivotal importance for the management of diabetes mellitus, especially among individuals who require periodic or continuous insulin infusion to manage this chronic condition.1 This challenge is addressed by the continuous glucose monitor (CGM), which is widely used by individuals with insulin-dependent diabetes mellitus.1 CGMs were developed throughout the 1990's and first commercialized in 1999 to provide further granularity to guide therapeutic insulin delivery rather than relying solely on infrequent fingerstick capillary blood sampling.2 In spite of the little-disputed clinical benefit of continuous glucose monitoring over fingerstick capillary blood sampling, CGM adoption among intensively-insulin-managed patients today remains tepid due, in part, to the limited reliability and accuracy of such systems.3 Indeed, accuracy oftentimes is compromised due to signal perturbations that are both endogenous (sensor warm-up, presence of reactive oxygen species in situ, convection of interstitial fluid) and exogenous (pressure-induced signal attenuation, temperature fluctuations) in nature. In these scenarios, a single sensing element can often be corrupted in its ability to faithfully track dynamical fluctuations in glucose or other circulating analytes of physiologic relevance. However, in line with aims towards improving device accuracy, the integration of both analyte-selective and analyte-invariant sensing modalities as extricable components within a continuous analyte monitor (such as a CGM) is an active area of development. With the above being said, the integration of said sensing elements presents its own set of unique challenges, namely, developing robust methods for the integration of multiple sensing elements into a single transducer, minimizing undue interactions among said sensing elements, and the accurate deposition of unique analyte-selective and analyte-invariant sensing chemistries within the said transducer. In light of these challenges, much of the prior art has instructed of single-transducer designs configured solely for the detection of an analyte. In such embodiments, the analyte sensing system is relegated to multiple electrodes comprised of adjacent metal wires or adjacent metal conduits on a flexible substrate.
U.S. patent Ser. No. 10/299,712 for a Dual Electrode System for a Continuous Analyte Sensor discloses systems and methods for a continuous analyte sensor, such as a continuous glucose sensor.
U.S. Pat. No. 8,010,174 for Systems and Methods for Replacing Signal Artifacts in a Glucose Sensor Data Stream discloses systems and methods for minimizing or eliminating transient non-glucose related signal noise due to non-glucose rate limiting phenomenon such as ischemia, pH changes, temperatures changes, and the like.
U.S. Pat. No. 8,548,553 for System and methods for processing analyte sensor Data discloses systems and methods for processing sensor analyte data, including initiating calibration, updating calibration, evaluating clinical acceptability of reference and sensor analyte data, and evaluating the quality of sensor calibration.
U.S. Pat. No. 9,662,056 for Optimizing analyte sensor calibration discloses a method and apparatus for optimizing analyte sensor calibration including receiving a current blood glucose measurement, retrieving a time information for an upcoming scheduled calibration event for calibrating an analyte sensor, determining temporal proximity between the current blood glucose measurement and the retrieved time information for the upcoming calibration event, initiating a calibration routine to calibrate the analyte sensor when the determined temporal proximity is within a predetermined time period, and overriding the upcoming scheduled calibration event using the current blood glucose measurement are provided.
U.S. Pat. No. 8,376,945 for a Method and system for providing calibration of an analyte sensor in an analyte monitoring system discloses a method and apparatus for providing calibration of analyte sensor including applying a control signal, detecting a measured response to the control signal, determining a variance in the detected measured response, and estimating a sensor sensitivity based on the variance in the detected measured response is provided.
U.S. Pat. No. 8,346,335 for Analyte sensor calibration Management discloses methods and devices to detect analyte in body fluid are provided. Embodiments include positioning an analyte sensor in fluid contact with an analyte, detecting an attenuation in a signal from an analyte sensor after positioning during a predetermined time period, categorizing the detected attenuation in the analyte sensor signal based, at least in part, on one or more characteristics of the signal, performing signal processing to generate a reportable data associated with the detected analyte sensor signal during the predetermined time period, managing if and when to request additional reference signal measurements, and managing if and when to temporarily not display results.
U.S. Pat. No. 6,801,041 for Sensor having electrode for determining the rate of flow of a fluid discloses sensors that are capable measuring the rate of flow of a fluid that passes over the electrodes of the sensor. In these sensors, an electrode, designated the flow rate-determining electrode, is used in conjunction with the conventional electrodes, e.g., the working electrode, the reference electrode, and the counter electrode, to determine the rate of flow of the fluid.
U.S. Pat. No. 9,743,871 for Multiple electrode system for a continuous analyte sensor, and related methods discloses a continuous analyte sensor having more than one working electrode, and configured to reduce or eliminate crosstalk between the working electrodes.
Prior art solutions have largely been concerned with the mitigation of non-analyte-related signal perturbations via physical, chemical, algorithmic, and contextual methods. The most pertinent and basic example includes requesting or otherwise prompting the user, through a software interface, to notify the system when partaking in certain activities; these activities include exercise, administration of certain therapeutic agents (i.e. acetaminophen, insulin), or consumption of carbohydrates (of relevance to CGM). Physical methods, as another example, include a reduction in the profile of the body-worn sensing contingent to reduce susceptibility to pressure-induced perturbations of the analyte signal. Within the chemical domain, the synthesis of ever-more selective receptor molecules and diffusive flux-limiting membranes aims to increase sensor selectivity in the wake of the undue influence imparted by the complex array of endogenous (i.e. metabolites, hormones, neurotransmitters, small molecules) and exogenous (i.e. pharmaceuticals, supplements, drugs of abuse) analytes co-habilitating the physiological fluid. Likewise, the implementation of advanced signal processing algorithms targeted at outlier detection, fault compensation, and non-physiological rates of change are often employed to reduce the preponderance of signal artifacts and the deleterious contribution of various physical and chemical processes upon sensor performance. More recently, sensor fusion methods aimed at contextual assessment of the user and the state of their body-worn sensor, have been studied as potential countermeasures to reducing the preponderance of signal artifacts. In such solutions, data from orthogonal measures (kinesthetic, electrophysiological, electrodermal, optophysiological sensors, among others) is integrated in a fusion algorithm in order to better understand the likelihood of non-analyte-derived contributions to the signal transduced by the analyte-selective sensor.
Microneedle arrays (MNAs) require insertion within a specific velocity range. Usually the MNAs are a component of a mechanically rigid assembly including the electronics, housing, adhesive, and MNA mounted into a sensor body. Inserting this entire sensor body presents a number of challenges including accelerating the mass to speed with a force over a short distance, stretching the skin prior to accelerating the sensor body into the skin so that the skin will be tight and not displace away from the MNA on impact, releasing the sensor body subsequently from the applicator, along with a myriad of other concerns such as the total cost and size of the applicator and preventing unintended misuse of this complex multi-stage mechanism.
Stretching the skin causes discomfort to the user and the impact of the sensor on the skin also causes discomfort to the user.
This same impact of the sensor body traveling at relatively high speeds can also cause the human body to react with immunological reactions such as redness, swelling, erythema, and/or edema in the area where the sensor was installed. This reaction elongates sensor warm up times and causes increased user discomfort and distress.
Existing needle-, trocar-, and cannula-based analyte-sensors, configured for the selective detection of a target analyte (i.e. glucose) are also sensitive to external mechanical, electrical, and chemical stimuli that corrupt the accurate determination of the target analyte or plurality of analytes. Specifically, these stimuli are largely manifested in undesired perturbations of the signal or signals transduced from said analyte-selective sensors, which serves to introduce error into measurement and thereby undermines the ultimate accuracy achievable with such devices. Indeed, these errors often compound and can result in potentially life-threatening circumstances when the analyte determination tendered by an analyte-selective sensor is utilized in a closed-loop system configured to manage chronic disease. In such a scenario, a potentially lethal dose of therapeutic agent can be delivered, autonomously and sans user intervention, as a response to counteract a perceived pathophysiological reading; a pertinent example is within the domain of automated insulin delivery (AID) for individuals with intensive insulin-managed diabetes. Although analyte-selective sensors operate in a manner to enhance the selectivity towards a target analyte via the implementation of a receptor molecule (i.e. enzyme, antibody, aptamer), capture probe (i.e. single-stranded DNA), or selective catalyst (i.e. noble metal, inorganic species, electrochemical mediator) these devices nevertheless often succumb to exogenous influences that include pressure-induced signal attenuations, non-specific binding of extraneous analytes to the receptor molecule, changes in equilibrium conditions, and interactions with endogenous and exogenous chemical species occupying the physiological milieu, among a number of others. This is largely due to challenges associated with the inability to extricate or otherwise measure the contributions imparted by external stimuli, which are largely non-deterministic and stochastic in nature. To address this challenge, analyte sensing systems have been constructed featuring both analyte-selective and analyte-invariant sensing elements, each embodying a unique chemical constituency, in order to ratiometrically scale the analyte-selective sensor response to mitigate external sources of undue signal influence.4 However, in practice, noteworthy difficulties arise when attempting to deposit dissimilar chemistries on sensor geometries wherein both analyte-selective and analyte-invariant sensors are co-located within a single aggregated sensing element/transducer or intermingled in close proximity, which can lead to undesirable effects such as cross-talk. More recent efforts have been targeted at algorithmic and contextual methods of de-noising the analyte signal without requiring the addition of a second sensing modality, albeit these approaches have enjoyed very limited success.
The ability to identify signal contributions which are non-analyte in origin enables the implementation of various mathematical methods to deconvolve or otherwise extricate the signal that is purely analyte-derived in origin from the signal arising from external influences. The current invention instructs of the implementation of at least two distinct sensing elements residing within a microneedle array, whereby at least one unique sensing element embodies the ability to quantify the presence of a target analyte (analyte-selective sensor) and is otherwise sensitive, albeit undesirably, to external stimuli and at least one unique sensing element which is not selective towards the presence of a target analyte (analyte-invariant sensor) and is otherwise sensitive, desirably, to external stimuli.
The current invention instructs of devices and methods to mitigate the erroneous signal imparted by physical and/or chemical process incident upon analyte-selective electrochemical sensors that are non-analyte-related in origin. These processes often serve to corrupt the measurement signal tendered by said analyte-selective sensors. The solution described herein concerns the implementation of an analyte-invariant measure that is otherwise sensitive to physical and chemical perturbations incident upon the sensing system. This requires the construction of a sensing system featuring at least one of an analyte-selective sensor and at least one of an analyte-invariant sensor. In the preferred embodiment, the analyte-invariant sensor exhibits identical construction and constituency as the analyte-selective sensor sans the addition of an active biorecognition element, affinity molecule, catalyst, or capture probe that is selective towards the target analyte. In an alternative embodiment, a deactivated biorecognition element, expressing no residual biospecific activity, may be included in the analyte-invariant sensor. In yet another embodiment, the said active biorecognition element may be incorporated in the analyte-invariant sensor, but is subject to a deactivation process during sensor manufacture. In this fashion, any non-analyte signal perturbations will be incident upon both the analyte-selective and analyte-invariant sensing elements and can, through various mathematical transformations, be extricated from the fundamental analyte-derived signal in order to maximize the accuracy and reliability of the measurement. In this manner, the mitigation of common-mode influences upon the analyte-selective sensor, which are also detected by the analyte-invariant sensor, can be achieved and hence an overall improvement to the analyte signal fidelity (e.g., signal-to-noise ratio or similar characteristic) can be expected.
Another objective is to eliminate the need to stretch the skin to insert a MNA.
Anther objective is the ability to insert a wide variety of needles, dull or sharp, effectively.
One aspect of the present invention is a device for the mitigation of a non-analyte-derived signal perturbation incident upon a body-worn, microneedle array-based analyte sensor. The device comprises a first electrode and a second electrode. The first electrode is positioned on a surface of a first microneedle of a microneedle array. A selective recognition element is disposed on the first electrode and configured to generate a product or change in physical state arising from the interaction of the selective recognition element and an analyte. A membrane is disposed on the selective recognition element. The second electrode is positioned on a surface of a second microneedle of the microneedle array, and a membrane is disposed on the second electrode. The first electrode and second electrode are positioned in spatially distinct locations within a viable epidermis or dermis of a user. A bias potential or current is applied to each of the first electrode and the second electrode. An ensuing electrical response from each of the first electrode and the second electrode is measured. A mathematical transformation is applied to the electrical response generated at the first electrode as a function of the electrical response generated at the second electrode to cause an attenuation of the common-mode signal.
Another aspect of the present invention is a device for the mitigation of a non-analyte-derived signal perturbation incident upon a body-worn, analyte sensor. The device comprises a first electrode and a second electrode. A selective recognition element is disposed on the first electrode and configured to generate a product arising from the interaction of the selective recognition element and an analyte. A membrane is disposed on the selective recognition element. A membrane is disposed on the second electrode. The first electrode and second electrode are positioned in spatially distinct locations within a viable epidermis or dermis of a user. A bias potential or current is applied to each of the first electrode and the second electrode. An ensuing electrical response from each of the first electrode and the second electrode is measured. A mathematical transformation is applied to the electrical response generated at the first electrode as a function of the electrical response generated at the second electrode to cause an attenuation of the common-mode signal.
Yet another aspect of the present invention is a device, with an analyte-selective sensor and an analyte-invariant sensor, for the mitigation of a non-analyte-derived signal perturbation incident upon a body-worn, analyte sensor. The device comprises an analyte-selective sensor and an analyte-invariant sensor. The analyte-selective sensor comprises a first electrode and a selective recognition element disposed on the first electrode and configured to generate a product or change in physical state arising from the interaction of the selective recognition element and an analyte. A membrane is disposed on the selective recognition element. An analyte-invariant sensor comprises a second electrode and a membrane disposed on the electrode. The analyte-selective sensor and the analyte-invariant sensor are positioned in spatially distinct locations within a viable epidermis or dermis of a user. A bias potential or current is applied to each of the analyte-selective sensor and the analyte-invariant sensor. An ensuing electrical response is measured from each of the analyte-selective sensor and the analyte-invariant sensor. A mathematical transformation is applied to the electrical response generated at the analyte-selective sensor as a function of the electrical response generated at the analyte-invariant sensor to cause an attenuation of the common-mode signal.
Yet another aspect of the present invention is a method for the mitigation of a non-analyte-derived signal perturbation incident upon a body-worn, microneedle array-based analyte sensor. The method includes positioning a first microneedle and a second microneedle of a microneedle array in spatially distinct locations within the viable epidermis or dermis of a user, wherein said first microneedle features a first electrode, a selective recognition element disposed on said first electrode and configured to generate a product or change in physical state arising from the interaction of said selective recognition element and the analyte, and a membrane is disposed on the selective recognition element and the second microneedle features a second electrode and a membrane disposed on the second electrode. The method also includes applying a bias potential or current to each of the first electrode and the second electrode. The method also includes measuring an ensuing electrical response from each of the first electrode and second electrode. The method also includes applying a mathematical transformation to the said electrical response generated at the first electrode as a function of the said electrical response generated at the second electrode to cause an attenuation of the common-mode signal.
Yet another aspect of the present invention is a method for the mitigation of a non-analyte-derived signal perturbation incident upon a body-worn, analyte sensor. The method includes positioning a first electrode and a second electrode of an analyte sensor in spatially distinct locations within the viable epidermis or dermis of a user, wherein the first electrode comprises a selective recognition element disposed on the first electrode and configured to generate a product or change in physical state arising from the interaction of the selective recognition element and the analyte, and a membrane is disposed on the selective recognition element and the second electrode comprises a membrane disposed on the second electrode. The method also includes applying a bias potential or current to each of the first electrode and the second electrode. The method also includes measuring an ensuing electrical response from each of the first electrode and the second electrode. The method also includes applying a mathematical transformation to the electrical response generated at the first electrode as a function of the electrical response generated at the second electrode to cause an attenuation of the common-mode signal.
Yet another aspect of the present invention is a method for the mitigation of a non-analyte-derived signal perturbation incident upon a body-worn, analyte sensor system. The method includes positioning an analyte-selective sensor and analyte-invariant sensor of said analyte sensor system in spatially distinct locations within the viable epidermis or dermis of a user, wherein the analyte-selective sensor features a first electrode, a selective recognition element disposed on the first electrode and configured to generate a product or change in physical state arising from the interaction of the selective recognition element and the analyte, and a membrane disposed on the selective recognition element and the analyte-invariant sensor comprises a second electrode and a membrane disposed on the second electrode. The method also includes applying a bias potential or current to each of said analyte-selective sensor and analyte-invariant sensor. The method also includes measuring an ensuing electrical response from each of the analyte-selective sensor and the analyte-invariant sensor. The method also includes applying a mathematical transformation to the electrical response generated at the analyte-selective sensor as a function of the electrical response generated at the analyte-invariant sensor to cause an attenuation of the common-mode signal.
The analyte preferably includes at least one of a biomarker, chemical, biochemical, metabolite, electrolyte, ion, hormone, neurotransmitter, vitamin, mineral, drug, therapeutic, toxin, pathogen, infectious agent, allergen, enzyme, protein, nucleic acid, DNA, and RNA.
The analyte sensor system is preferably a microneedle or a microneedle array, with each microneedle constituent possessing a vertical extent preferably between 200 and 2000 μm.
The microneedle or microneedle array preferably contains at least one projection capable of insertion into the viable epidermis or dermis of a user.
The first electrode and the second electrode preferably include a metal, metal alloy, metal oxide, semiconductor, or polymeric surface.
The first electrode and the second electrode are confined to the tapered distal region of the microneedle or the elements of the microneedle array.
The selective recognition element preferably includes at least one of an enzyme, aptamer, antibody, capture probe, ionophore, catalyst, biocatalyst, DNA, RNA, organelle, or cell.
The product is preferably a chemical, biochemical, mediator, resistance change, electrical signal, electrochemical signal, conductance change, impedance change, or absorbance change.
The membrane is preferably at least one of a polymer, hydrophilic layer, biocompatible layer, diffusion-limiting layer, hydrogel, film, and coating.
The bias potential or current is preferably either of the direct current or alternating current variety.
The electrical response preferably includes at least one of a potential, current, impedance, conductance, resistance, capacitance, and inductance.
The mathematical transformation preferably includes at least one of a difference operation, denoising operation, regression, deconvolution, Fourier decomposition, background subtraction, Kalman filtering, and Maximum Likelihood Estimation.
The attenuation preferably includes at least one of the removal, minimization, or reduction in duration of the common-mode signal.
The common-mode signal preferably includes at least one of a warm-up signal following application of the microneedle array-based analyte sensor to the skin of a wearer, a pressure-induced signal artefact, a temperature-induced signal fluctuation, and an interference signal originating from an endogenous or exogenous chemical species circulating in a physiological fluid of a user.
The endogenous or exogenous chemical species preferably includes at least one of a biomarker, chemical, biochemical, metabolite, electrolyte, ion, hormone, neurotransmitter, vitamin, mineral, drug, therapeutic, toxin, pathogen, infectious agent, allergen, enzyme, protein, nucleic acid, DNA, and RNA.
The physiological fluid is preferably at least one of interstitial fluid, dermal interstitial fluid, or blood of a user.
Having briefly described the present invention, the above and further objects, features and advantages thereof will be recognized by those skilled in the pertinent art from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
Body-worn analyte-selective sensors, such as continuous glucose monitors, are sensitive electrochemical systems that are configured to sense an analyte, or plurality of analytes, in a selective fashion with a high-degree of accuracy. This accuracy can be unduly influenced by various external stimuli, which gives rise to undesired perturbations of the signal or signals transduced from said analyte-selective sensors, thereby introducing error in measurement and undermining the ultimate accuracy achievable with such devices. In this vein, even the most proficient analyte-selective sensors often succumb to the influence of external perturbations, which may be chemical, electrical, or mechanical in origin. The current innovation is aimed at mitigating the preponderance of undue physical, chemical, and otherwise exogenous influences upon the fidelity of the measurement of the target analyte or plurality of analytes. This is achieved via implementation of at least one of an analyte-selective sensor and at least one of an analyte-invariant sensor, whereby the said analyte-selective sensor features a selective recognition element and said analyte-invariant sensor lacks said selective recognition element but is otherwise identical in construction and constituency to said analyte-selective sensor. Using a mathematical transformation, algorithm, or combination thereof, the common-mode signal appearing at both the analyte-selective and analyte-invariant sensors may be minimized, mitigated, or eliminated entirely, thereby resulting in an analyte signal of greater fidelity and/or accuracy.
In order to mitigate non-analyte-derived signal perturbations incident upon a body-worn, microneedle array-based analyte sensor, the said device is configured to feature at least one of an analyte-selective sensor and at least one of an analyte-invariant sensor, both located on unique microneedle constituents of the array, as show in
In another embodiment of the present invention, as shown in
In yet another embodiment of the present invention, as shown in
As shown in
The devices and methods presented are capable of the determination of analytes that comprise at least one of a biomarker, chemical, biochemical, metabolite, electrolyte, ion, hormone, neurotransmitter, vitamin, mineral drug, therapeutic, toxin, enzyme, protein, nucleic acid, aptamer, DNA, and RNA. Furthermore, these systems employ microneedle arrays containing at least two projections capable of insertion into the viable epidermis or dermis of a user, wherein each projection possesses an extent between 200 and 2000 micrometers from proximal to distal extremities. The electrode constituent discussed above is confined to the distal region of the aforementioned protrusions and includes a metal, semiconductor, or polymeric surface. The selective recognition element discussed includes at least one of an enzyme, aptamer, antibody, capture probe, ionophore, catalyst, biocatalyst, DNA, RNA, organelle, or cell and is configured to produce a chemical, biochemical, mediator, resistance change, electrical signal, conductance change, impedance change, or absorbance change upon exposure to the analyte. The abovementioned membrane is at least one of a polymer, hydrophilic layer, biocompatible layer, diffusion-limiting layer, hydrogel, film, and coating.
Other novel and utilitarian features of the invention includes its intrinsic ability to negate the effect of cross-talk due to diffusive transport of product from analyte-selective to analyte-invariant sensor. The invention also reduces the influence of the analyte depletion region or diffusion layer effects, which serves to limit the quantity of analyte that can diffuse to an analyte-selective sensor.
Assuming that the analyte-selective sensor is sensitive to non-analyte signal perturbations (in addition to the analyte signal) and that the analyte-invariant sensor is purely a function of the non-analyte signal perturbation (i.e. not influenced by the analyte signal), the true analyte signal is isolated by differential measurement:
True Analyte Signal=Analyte selective Sensor Signal−Analyte invariant Sensor Signal
The above relation is implemented in a simple digital signal processing routine (such as a subtractor/difference engine) executed in device firmware or software. It can, likewise, be realized in simple analog hardware, such as a differential amplifier.
The common-mode signal that appears at both the analyte-selective and analyte-invariant sensors is extricated using a number of methods. Firstly, it is subtracted from the analyte signal by means of the subtractive relation:
True Analyte Signal=[Analyte selective Sensor Signal+Common Mode Signal]−[Analyte invariant Sensor Signal+Common Mode Signal
The above is realized in a simple analog signal processing routine via a differential amplifier.
Assuming that the common-mode signal is not additive, but rather present in its entirety at the analyte-invariant sensor and as a modulation of the signal tendered by the analyte-selective sensor, the common-mode signal is ratiometrically extricated by the relation:
Convolutional methods may be employed to extricate the pure analyte-selective signal component from other sources of noise. Assume the measured signal [m(x)] from the analyte-selective sensor represents the convolution of the component of the signal that is purely analyte-derived [a(x)] and a component imparted by sources of errant signal measures that are non-analyte in origin [n(x)], as measured by the analyte-invariant sensor:
m(x)=a(x)*n(x)
Fourier- or wavelet-based decomposition of both the analyte-selective and analyte-invariant signals can be employed to spectrally discriminate between the analyte signal and the undue effect of any non-analyte-derived signal perturbations:
In order to place equal weights on the spectral components, a normalization can be employed:
M
NORM
jω)=ANORM(jω)NNORM(jω)
Or recasting:
A
NORM(jω)=MNORM(jω)/NNORM(jω)
Hence the spectrally-pure tone arising from the analyte-selective signal is computed using the above relation. The inverse Fourier- or wavelet-transform is now employed to return to the time or data series domain:
The signal-to-noise ratio (SNR) engendered by such a system is computed as the logarithm (in base 10) of the ratio of analyte-selective sensor signal to the analyte-invariant sensor signal:
This enables the calculation of the noise figure (NF) of the system:
Where the SNRi is the signal-to-noise ratio of the system at a specified analyte level and SNRo is the measured signal-to-noise ratio embodied by a particular measurement.
The common-mode rejection ratio (CMRR) is computed as the logarithm (in base 10) of the ratio of analyte-selective sensor signal to the analyte-invariant sensor signal:
Given the ability to measure the impact of non-analyte-derived signals, the following list of routines might be employed to compensate the non-analyte effects from the signal or produce an optimal estimate of the analyte concentration:
Adaptive Filters:
In most signal processing applications5-7, the non-analyte effect is assumed to be additive, this is due to the fact that multiplicative models represent a greater challenge to solve. In these approaches, the general model at each discrete sample n is:
s(n)=a(n)+i(n)+e(n)
where s(n) is the total detected signal, a(n) is the desired analyte signal, i(n) is the additive contribution due to non-analyte contribution, and e(n) is the filter residual. In order to solve the equation above, an adaptive filter would adjust the coefficients of a time-varying filter W(n) to regress the non-analyte signal into s(n). The cost function is defined as:
min(norm(W′i−s,2)).
At sample n, the filter residual is:
e(n)=s(n)−Σj=1pwji(n−j),
where p is the order of the filter. The residual is minimized to find the correlation between the reference interference signal and s(n) and the output may be classified as the ‘cleaned’ signal plus uncorrelated white noise. A wide variety of algorithms exists to solve this regression problem in real time such as: Recursive Least Squares (RLS); Least Mean Squares (LMS); Kalman Filter (KF); and Kernel Adaptive filtering (KAF).
A method 200 for the mitigation of a non-analyte-derived signal perturbation incident upon a body-worn, microneedle array-based analyte sensor is shown in
Another method 205 method for the mitigation of a non-analyte-derived signal perturbation incident upon a body-worn, analyte sensor is shown in
Yet another method 210 for the mitigation of a non-analyte-derived signal perturbation incident upon a body-worn, analyte sensor system is shown in
The electrochemical analog front end preferably includes: a Texas Instruments UMP91000 Sensor AFE System, configurable AFE potentiostat for low-power chemical sensing applications; a Texas Instruments LMP91200 configurable AFE for low-power chemical sensing applications; or an Analog Devices ADuCM350 16-Bit Precision, low power meter on a chip with Cortex-M3 and connectivity. The wireless transceiver is preferably is a BLUEGIGA BLE-113A BLUETOOTH Smart Module, or a Texas Instruments CC2540 SimpleLink BLUETOOTH Smart Wireless MCU with USB. The accompanying mobile device is preferably an ANDROID™- or iOS™-based smartphone, Samsung GALAXY GEAR, or an APPLE WATCH™.
The microneedle array electrochemical biosensor transduces biochemical signals from the interstitial fluid into useful electrical signals.
The electrochemical analog front end preferably performs at least one or more of the following: applies a fixed potential or time-varying potential to the microneedle array to induce an electrochemical reaction, thereby giving rise to a flow of current; applies a fixed current or time-varying current to the microneedle array to induce an electrochemical reaction, thereby giving rise to an electrical potential; measures a time-varying open-circuit potential generated by an electrochemical reaction or ionic gradient; measures a frequency-dependent impedance generated by an electrochemical or bio-affinity reaction at the microneedle transducer; and measures a specific resistance or conductance generated by an electrochemical or bio-affinity reaction at the microneedle transducer.
The electrochemical analog front end is preferably dynamically configured to achieve any one of the above-numerated embodiments. Likewise, the inputs are preferably arrayed to operate sequentially or in parallel to expand the sensing capabilities of the system.
The wireless transceiver wirelessly relays electrical signals generated by the electrochemical analog front end to a mobile or wearable device using any one of a number of standardized wireless transmission protocols (Bluetooth, WiFi, NFC, RFID, Zigbee, Ant+). Optionally, the electrical signal generated by the analog front end can be amplified, filtered, and/or undergo analog-to-digital-conversion and further signal processing prior to being relayed by the wireless transceiver.
The mobile or wearable device displays sensor readings to the user in an easily-understood format, and performs any additional signal processing necessary.
As shown in
The method steps of the potentiostat operation are as follows:
The Analog Front End/Potentiostat Operation. The potentiostat/AFE unit consists of either two (
The Analog Front End and Applied Reference/Working Bias. In the system shown in
The difference amplifier stage 75 is shown in
The Filtering step. The outputs generated from the difference amplifier pair are subsequently subjected to a filtering circuit to remove extraneous noise. Oscillations or random fluctuations in the signal can be present due to a number of reasons, including ground bias, RF interference, mains power oscillation, input impedance mismatch (from the 3 electrode sensor), or from other sources.
The Analog to Digital Converter step. The filtered signals are lastly incident upon an analog to digital converter (“ADC”), either located in an external integrated circuit (“IC”), or co-located within a microcontroller or other IC, and converted into a representative digital signal. Increased sampling resolution may be implemented to gain additional sensitivity and minimize quantization error.
The Collection Algorithm step. To further reduce noise, a time averaged value for both positive and negative bias lines will be collected and computed by a microcontroller/microprocessor over a period of a few seconds (subsequent to digitization by the ADC). The active bias amplifier (applied voltage/current) will have the value of the inactive bias amplifier (ground offset) subtracted in order to remove any present bias in the device. Due to this process, a shielding cage is not required to reach picoampere levels of sensitivity. The inactive bias amplifier, time average data collection, and filtering schemes will provide a stable and scalable output into the microcontroller/processor at all times.
The input of the electrochemical cell or sensor, the analyte, is measured by controlled-potential techniques (amperometry, voltammetry, etc). The output of the sensing system, consisting of a measured voltage and calculated current value (determination of current flowing through working and counter electrodes of electrochemical cell or sensor), corresponds to the concentration of the analyte in the sample.
The sampling and measurement algorithm is designed to minimize sources of noise that are not compensated or otherwise removed using the circuit hardware. As shown in the block diagram 90 of
The main sources of noise are: floating ground and ground drift; mains power; and high frequency interference.
The floating ground and ground drift are compensated by various means. Floating ground (DC noise) is compensated by the presence of the paired difference amplifiers. Ground drift is compensated by averaging multiple samples. If measuring a positive bias/current, the negative output will be equal to the floating ground. Subtracting the negative output from the positive will remove noise caused by ground drift. The opposite can be performed when measuring a negative bias/current. The subtraction step should be performed at each sample rather than using averages of multiple readings.
Mains Power is also compensated in various ways. Noise arising due to mains power when either connected to an AC power line or induced by proximity to other AC line-powered equipment is compensated by selection of the algorithm sampling period. Sampling should never be performed at the same delay as the period of the line power cycle (16 or 20 ms for 60 Hz and 50 Hz power systems, respectively) or any multiple thereof (i.e. 32 to 40 ms for a multiple of two, etc). If sampling delay is less than the line power cycle (16-20 ms), at least one cycle (at 50-60 Hz) must be captured by multiple samples. For proper statistical analysis, enough samples must be collected to establish an adequate estimate of the standard deviation and mitigate power line harmonics. For a 95% confidence interval for Type 1 (false positive) and Type 2 (false negative) errors, for example, at least 13 samples must be measured. This is application-specific but a minimum of 10 samples is recommended. The maximum sample number is application-dependent (the likelihood of sudden changes due to external factors, such as movement in the case of a body worn sensor).
High frequency interference, noise due to wireless transmission and other high frequency signals, is eliminated fully by hardware filtering, notably low pass filtering.
Neural Networks:
A wide variety of neural networks (NNs) may be used to both fuse multichannel signal measurements and also remove the undesired signals. The input to the NN comprises the input measurements and the network is trained a priori on desired signal measurement (i.e. interstitial glucose values). The network is trained, using either supervised or unsupervised learning methods, to develop a mathematical model mapping between signal (i.e. electrical current), temperature, non-analyte and other sources of interference and the target desired analyte signal. Different forms of deep and shallow neural networks might be built with combination of following layers: Recurrent Neural Networks; Convolutional Neural Networks.
Convex Optimization:
In a number of embodiments, real-time convex optimization is employed to deconvolve the undesired effects by constructing regression cost functions that have additional penalizing factors in their cost function in order to apply prior knowledge of smoothness or other frequency-based knowledge of the interreference signals.
Projection Techniques:
Projection techniques such as linear and nonlinear (kernel) Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are also employed in selected embodiments for blind source separation. In this case, the input matrix X contains all the signals, including analyte-selective and non-analyte-selective signals, as well as any extraneous signal readouts, such as temperature. These approaches create a rotation matrix A that maximizes the variance (in case of PCA) and independence (in case of ICA) that results in separation of the input sources.
Continuous Wavelet Transform:
Continuous Wavelet Transform (CWT) of the analyte-selective and analyte-invariant sensor measurements are computed in certain embodiments so that a two-dimensional corresponding time-frequency of non-analyte and ‘contaminated’ analyte signal measurements can be constructed. The corresponding coefficients of frequencies that are correlated between the reference non-analyte and contaminated analyte signals in time are set to zero to remove the said effects.
Non-analyte-derived signal perturbations observable in analyte-selective sensors can claim origin from a plethora of physio-chemical processes, some of which are endogenous to the biological milieu while others arise due to exogenous effects instigated by the wearer of said sensors. Indeed, body-worn analyte-selective sensors often succumb to pressure-induced signal irregularities due to the inadvertent application of pressure or force onto the said sensor enclosure or housing; these are referred to as pressure-induced sensor attenuations (PISAs). This oftentimes is caused by induced changes in perfusion to the sensor or localized depletion of the analyte of interest or co-factor, such as oxygen. The disruption of the diffusion layer (nanometers to millimeters in extent) is also a cause of said PISA events since the sensing operation enabled by said analyte-selective sensors is diffusion-limited in nature. The execution of an analyte-invariant measure enables the identification of these instances, especially in the acute phase, as it is generally understood that the response of said analyte-invariant sensor is largely immune to said PISA events. Moreover, all electrochemical sensors undergo a non-Faradaic process immediately following excitation with an electrical stimulus wherein the ensuing signal response is not proportional to analyte concentration by the Cottrell relation, but rather the charging of the double-layer capacitance through the solution resistance. This is always manifested upon excitation of an electrochemical sensor with a voltage or current stimulus and decays to negligible levels in a finite time according to the RsCdl time constant, where Rs is the solution resistance and Cdl is the double layer capacitance. An analyte-invariant sensor undergoing the same non-Faradaic signal decay as an analyte-selective sensor may be employed in a differential configuration to extricate the true analyte signal from the non-Faradaic signal response. Similarly, implanted analyte-selective sensors require a certain time duration prior to measurement of accurate representations of analyte levels, which referred to as ‘warm-up time’ or ‘burn-in’. The said warm-up or burn-in process is a complex physio-chemical interaction, which is governed by an interplay between hydration of the sensor membrane(s), establishment of equilibrium between the sensor membrane(s) and the surrounding interstitial medium, and adsorption of the circulating endogenous proteins (occupying the interstitial space) on the sensing surface of said analyte-selective sensor. An analyte-invariant sensor undergoing the same warm-up process as an analyte-selective sensor may be employed in a differential configuration to extricate the true analyte signal from the non-Faradaic signal response and hence yield measurements in a more timely fashion following sensor application, as shown in
The preferred embodiments of the current invention include the removal of said non-analyte signal perturbation(s) in the system's analog front end, sensor front end, embedded computer, microprocessor, microcontroller, in a wirelessly-connected mobile device such as a smartphone, smartwatch, or tablet, or in a Cloud service. In other embodiments, the geometry and/or constituency of the analyte-invariant sensor is identical to that of the analyte-selective sensor with the exception that the biorecognition element (i.e. enzyme, antibody, aptamer) is absent. In yet other embodiments, the geometry and/or constituency of the analyte-invariant sensor is identical to that of the analyte-selective sensor with the exception that the biorecognition element (i.e. enzyme, antibody, aptamer) is inactive or has been rendered inactive during the manufacturing process. In yet other embodiments, the system contains a plurality of analyte-selective sensors and a single analyte-invariant sensor. In yet other embodiments, the system contains a plurality of analyte-selective sensors, each selective towards a unique analyte, and at least one analyte-invariant sensor. In yet other embodiments, readout from the analyte-invariant sensor is utilized to extricate and remove a temperature dependency of the analyte-selective sensor. In yet other embodiments, readout from the analyte-invariant sensor is utilized to extricate and remove interference from co-circulating analytes to which the analyte-selective sensor might exhibit partial sensitivity. In yet other embodiments, the current method of mitigation of non-analyte signal perturbations incident upon analyte-selective sensors is employed in a sensor fusion algorithm to improve reliability and/or accuracy of the measure of the analyte(s) of interest. In yet other embodiments, the analyte-selective and analyte-invariant sensors occupy the same microneedle constituent within a microneedle array.
An ARRAY is a microneedle or microneedle array-based electrochemical, electrooptical, or fully electronic device configured to measure an endogenous or exogenous biochemical agent, metabolite, drug, pharmacologic, biological, or medicament in the dermal interstitium, indicative of a particular physiological or metabolic state in a physiological fluid of a user. Specifically, said microneedle array contains a plurality of microneedles, possessing vertical extent between 200 and 2000 μm, configured to selectively quantify the levels of at least one analyte located within the viable epidermis or dermis and in the vicinity of the papillary plexus, subpapillary plexus, or dermal plexus. Said microneedle array is contained and/or mounted to an enclosure or housing containing a power source, electronic measurement circuitry, a microprocessor, and a wireless transmitter. Sensor is configured with a skin-facing adhesive (sensor adhesive) intended to adhere the said sensor for the desired wear duration.
Analyte-selective sensor (SELECTIVE SENSOR) is an electrode on the surface of at least one microneedle of said microneedle array, a selective recognition element disposed on said electrode and configured to generate a product arising from the interaction of said selective recognition element and an analyte indicative of a particular physiological or metabolic state in a physiological fluid of a user, and a membrane disposed on said selective recognition element. Said analyte is comprised of at least one endogenous or exogenous biochemical agent, metabolite, drug, pharmacologic, biological, or medicament.
Analyte-invariant sensor (INVARIANT SENSOR) is an electrode on the surface of at least one microneedle of said microneedle array distinct from SELECTIVE SENSOR and a membrane disposed on said electrode.
An Algorithm (ALGORITHM) is a mathematical transformation applied to the electrical response generated at the SELECTIVE SENSOR as a function of the electrical response generated at the INVARIANT SENSOR to remove the common-mode signal present at both said sensors.
In a method, a measurement is recorded at SELECTIVE SENSOR. A qualitative or quantitative determination of the level of a target biomarker, chemical, biochemical, metabolite, electrolyte, ion, hormone, neurotransmitter, vitamin, mineral, drug, therapeutic, toxin, enzyme, protein, nucleic acid, DNA, or RNA circulating within a physiological fluid of a user. Next, an ALGORITHM is applied to the measurements recorded at SELECTIVE SENSOR and INVARIANT SENSOR. A mathematical transformation is thus applied to the electrical response generated at the SELECTIVE SENSOR as a function of the electrical response generated at the INVARIANT SENSOR to remove the common-mode signal present at both said sensors. Said algorithm can comprise of at least one of a difference operation, denoising operation, regression, deconvolution, Fourier decomposition, background subtraction, Kalman filtering, and Maximum Likelihood Estimation.
Inputs of the invention include an analyte measurement and an analyte-invariant measurement. The analyte measurement is a qualitative or quantitative determination of the level of a target biomarker, chemical, biochemical, metabolite, electrolyte, ion, hormone, neurotransmitter, vitamin, mineral, drug, therapeutic, toxin, enzyme, protein, nucleic acid, DNA, or RNA circulating within a physiological fluid of a user. Measurement is provided by SELECTIVE SENSOR. The analyte-invariant measurement is a qualitative or quantitative determination of any endogenous or exogenous, stochastic or non-stochastic, physical and/or chemical processes incident upon analyte-selective electrochemical sensors that are non-analyte-related in origin. These processes often serve to corrupt the measurement signal tendered by said analyte-selective sensors. Measurement is provided by an INVARIANT SENSOR.
The output of the invention is an analyte measurement with common mode signal removed, which is a qualitative or quantitative measurement of the endogenous levels of a particular analyte of interest.
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From the foregoing it is believed that those skilled in the pertinent art will recognize the meritorious advancement of this invention and will readily understand that while the present invention has been described in association with a preferred embodiment thereof, and other embodiments illustrated in the accompanying drawings, numerous changes modification and substitutions of equivalents may be made therein without departing from the spirit and scope of this invention which is intended to be unlimited by the foregoing except as may appear in the following appended claim. Therefore, the embodiments of the invention in which an exclusive property or privilege is claimed are defined in the following appended claims.
The Present application claims priority to U.S. Provisional Patent Application No. 63/048,614, filed on Jul. 6, 2020, and U.S. Provisional Patent Application No. 63/111,057, filed on Nov. 8, 2020, and the present application is a continuation-in-part application of U.S. patent application Ser. No. 17/073,331, filed on Oct. 17, 2020 which claims priority to U.S. Provisional Patent Application No. 62/927,049, filed on Oct. 28, 2019, now expired, is a continuation-in-part application of a U.S. patent application Ser. No. 16/824,700, filed on Mar. 20, 2020, which claims priority to U.S. Provisional Patent Application No. 62/823,628, filed on Mar. 25, 2019, now expired, and is a continuation-in-part application of U.S. patent application Ser. No. 16/666,259, filed on Oct. 28, 2019, which is a continuation application of U.S. patent Ser. No. 16/152,372, filed on Oct. 4, 2018, now U.S. patent Ser. No. 10/492,708 issued on Dec. 3, 2019, which is a continuation application of U.S. patent Ser. No. 15/590,105, filed on May 9, 2017, now U.S. patent Ser. No. 10/092,207, issued on Oct. 9, 2018, which claims priority to U.S. Provisional Patent Application No. 62/336,724, filed on May 15, 2016, now expired, each of which is hereby incorporated by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63048614 | Jul 2020 | US | |
| 63111057 | Nov 2020 | US | |
| 62927049 | Oct 2019 | US | |
| 62823628 | Mar 2019 | US | |
| 62336724 | May 2016 | US |
| Number | Date | Country | |
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| Parent | 16152372 | Oct 2018 | US |
| Child | 16666259 | US | |
| Parent | 15590105 | May 2017 | US |
| Child | 16152372 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 17073331 | Oct 2020 | US |
| Child | 17367274 | US | |
| Parent | 16824700 | Mar 2020 | US |
| Child | 17073331 | US | |
| Parent | 16666259 | Oct 2019 | US |
| Child | 17073331 | US |
