Systems and methods for compensation of optical signals

Abstract

An example method of configuring an optical sensor for physiological measurements includes: determining an initial configuration of an optical sensor, determining an initial signal quality index using the optical sensor configured in the initial configuration, selecting a candidate configuration of the optical sensor, determining a candidate signal quality index using the optical sensor configured in the candidate configuration, and determining an optimized configuration of the optical sensor based on at least the candidate signal quality index and the initial signal quality index.

Description

Attorney Docket: 11687-018WO1 SYSTEMS AND METHODS FOR COMPENSATION OF OPTICAL SIGNALS CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. provisional patent application No.63 / 749,955, filed on January 27, 2025, and titled “SYSTEMS AND METHODS FOR COMPENSATION OF OPTICAL SIGNALS,” the disclosure of which is expressly incorporated herein by reference in its entirety.BACKGROUND

[0002] A photoplethysmogram (PPG) is an optical measure of blood volume changes in tissue. In a PPG device, light illuminates a subject’s skin, and the amount of light transmitted or reflected is measured. By measuring the amount of light transmitted or reflected, the volume of blood in the subject’s tissue can be estimated. This volume of blood can be used to estimate different physiological parameters. One such parameter of the circulatory system is blood pressure.

[0003] Improvements to optical measurements can improve patient health by allowing more accurate non-invasive measurements of blood pressure and other physiological parameters.SUMMARY

[0004] In some aspects, implementations of the present disclosure include a method including: (a) determining an initial configuration of an optical sensor; (b) determining an initial signal quality index using the optical sensor configured in the initial configuration; (c) selecting a candidate configuration of the optical sensor; (d) determining a candidate signal quality index using the optical sensor configured in the candidate configuration; and (e) determining an optimized configuration of the optical sensor based on at least the candidate signal quality index and the initial signal quality index.

[0005] In some aspects, implementations of the present disclosure include a method, wherein steps (c)-(e) are iteratively repeated.

[0006] In some aspects, implementations of the present disclosure include a method, wherein determining an optimized configuration of the optical sensor includes selecting between the initial configuration of the optical sensor and the candidate configuration of the optical sensor based on the candidate signal quality index and the initial signal quality index.Attorney Docket: 11687-018WO1

[0007] In some aspects, implementations of the present disclosure include a method, wherein the optical sensor includes a photodiode.

[0008] In some aspects, implementations of the present disclosure include a method, wherein the initial configuration and the candidate configuration of the optical sensor include a drive value for the optical sensor.

[0009] In some aspects, implementations of the present disclosure include a method, wherein the optical sensor includes a photodiode.

[0010] In some aspects, implementations of the present disclosure include a method, wherein the optical sensor includes a plurality of photodiodes.

[0011] In some aspects, implementations of the present disclosure include a method, wherein the optical sensor includes a PPG sensor.

[0012] In some aspects, implementations of the present disclosure include a method, further including configuring the sensor in the optimal configuration.

[0013] In some aspects, implementations of the present disclosure include a method of optical sensor calibration, including: receiving a photoplethysmogram measured at a skin surface; estimating a calibration component of the photoplethysmogram caused by an optical property of the skin surface; and outputting a corrected photoplethysmogram, wherein the corrected photoplethysmogram does not include the component caused by an optical property of the skin surface.

[0014] In some aspects, implementations of the present disclosure include a method, further including receiving a color measurement of the skin surface, and wherein the optical property includes color.

[0015] In some aspects, implementations of the present disclosure include a method, wherein the color measurement includes a spectrophotometer measurement.

[0016] In some aspects, implementations of the present disclosure include a method, wherein the color measurement includes a spectroradiometer measurement.

[0017] In some aspects, implementations of the present disclosure include a method, further including receiving a fluid measurement, and wherein the optical property includes surface moisture.

[0018] In some aspects, implementations of the present disclosure include a method, wherein the fluid measurement includes a bioimpedance measurement.

[0019] In some aspects, implementations of the present disclosure include a system for compensated optical measurement, the system including: an optical sensor, wherein theAttorney Docket: 11687-018WO1 optical sensor is configured to measure a light intensity; an optical property sensor, wherein the optical property sensor is configured to measure an optical property.

[0020] In some aspects, implementations of the present disclosure include a system, wherein the optical sensor includes a PPG sensor.

[0021] In some aspects, implementations of the present disclosure include a system, wherein the optical property sensor includes a spectrophotometer or spectroradiometer.

[0022] In some aspects, implementations of the present disclosure include a system, wherein the optical property sensor includes a bioimpedance sensor.

[0023] It should be understood that the above-described subject matter may also be implemented as a computer-controlled apparatus, a computer process, a computing system, or an article of manufacture, such as a computer-readable storage medium.

[0024] Other systems, methods, features and / or advantages will be or may become apparent to one with skill in the art upon examination of the following drawings and detailed description. It is intended that all such additional systems, methods, features and / or advantages be included within this description and be protected by the accompanying claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The components in the drawings are not necessarily to scale relative to each other.Like reference numerals designate corresponding parts throughout the several views.

[0026] FIG. 1 A illustrates a method of configuring an optical sensor, according to implementations of the present disclosure.

[0027] FIG. IB illustrates a method of outputting a corrected photoplethysmogram, according to implementations of the present disclosure.

[0028] FIG. 2 illustrates a wearable system including an optical sensor, according to implementations of the present disclosure.

[0029] FIG. 3 illustrates a block diagram of a system configured to control a set of LEDs and collect data using a set of photodetectors, according to implementations of the present disclosure.

[0030] FIG. 4 illustrates a method of configuring LEDs or other optical sensors to obtain a best response, according to implementations of the present disclosure.

[0031] FIG. 5 illustrates an example computing device.

[0032] FIG. 6 illustrates an example block diagram showing layers of an example implementation stack, according to implementations of the present disclosure.Attorney Docket: 11687-018WO1

[0033] FIG. 7 illustrates a block diagram of a method of operating a device including multiple LEDs and photodetectors, according to implementations of the present disclosure.

[0034] FIG. 8 illustrates selecting a max of a composite sum, according to implementations of the present disclosure.DETAILED DESCRIPTION

[0035] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. Methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present disclosure. As used in the specification, and in the appended claims, the singular forms “a,” “an,” “the” include plural referents unless the context clearly dictates otherwise. The term “comprising” and variations thereof as used herein is used synonymously with the term “including” and variations thereof and are open, nonlimiting terms. The terms “optional” or “optionally” used herein mean that the subsequently described feature, event or circumstance may or may not occur, and that the description includes instances where said feature, event or circumstance occurs and instances where it does not. Ranges may be expressed herein as from "about" one particular value, and / or to "about" another particular value. When such a range is expressed, an aspect includes from the one particular value and / or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent "about," it will be understood that the particular value forms another aspect. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint. While implementations will be described for wearable devices using PPG sensors and signals, it should be understood that implementations of the present disclosure include systems, devices, and methods for estimating and / or determining any combination of physiological measurements.

[0036] As used herein, the terms "about" or "approximately" when referring to a measurable value such as an amount, a percentage, and the like, are meant to encompass variations of ±20%, ±10%, ±5%, or ±1% from the measurable value.

[0037] Implementations of the present disclosure provide improvements to performing optical measurements of a skin surface, including PPG measurements. Optical measurements of a skin surface can include illuminating the skin surface and measuringAttorney Docket: 11687-018WO1 the light reflected and / or absorbed by the skin surface. One such measurement is PPG, which commonly involves illuminating the skin surface with one or more LEDs, and using the reflected / absorbed light to measure blood volume and other parameters of the circulatory system.

[0038] Existing optical sensors are limited by variations in different patient’s skin tones.For example, different skin colors can absorb different amounts of light, so that two patients may have the same underlying physiology (e.g., blood pressure, pulse, blood oxygen, etc.) but have different PPG signals when measured. Moreover, the skin surface of the same patient can change over time. For example, a patient may sweat, coating the skin in reflective water and oil, which can also result in different absorption / reflection measurements that are not related to changes in the patient’s blood pressure, pulse, blood oxygen, etc.

[0039] Implementations of the present disclosure can solve these and other challenges with conventional optical sensors. As described herein, an example implementation of the present disclosure can include controlling a PPG sensor to optimize the performance of the PPG sensor. As further described herein, another example implementation of the present disclosure can include measuring skin moisture, skin color, and / or any other optical property of the skin. These and other implementations of the present disclosure described herein allow PPG signals to be reliably used for measurement, diagnosis, and treatment across a wide range of individuals.

[0040] With reference to FIG. 1 A, an example method is shown according to implementations of the present disclosure.

[0041] At step 110 the method includes determining an initial configuration of an optical sensor. The optical sensor can optionally be a photodiode or a set of photodiodes.Optionally, the configuration of the optical sensor can include drive values for the photodiode, where the drive values can be power levels, duty cycles, wavelengths, or other parameters for controlling the optical sensor (e.g., controlling one or more LEDs used as part of the optical sensor).

[0042] At step 120 the method includes determining an initial signal quality index using the optical sensor configured in the initial configuration.

[0043] At step 130 the method includes selecting a candidate configuration of the optical sensor.

[0044] At step 140 the method includes determining a candidate signal quality index using the optical sensor configured in the candidate configuration.Attorney Docket: 11687-018WO1

[0045] At step 150 the method includes determining an optimized configuration of the optical sensor based on at least the candidate signal quality index and the initial signal quality index. Optionally, the optimized configuration of the optical sensor can be selected from between the initial configuration of the optical sensor and the candidate configuration of the optical sensor based on the candidate signal quality index and the initial signal quality index. Optionally, the optical sensor can then be configured in the optimal configuration, for example by changing any drive values of the optical sensor to control the one or more LEDs of the optical sensor to operate the optical sensor so that the signal quality, accuracy, etc. is optimized.

[0046] It should be understood that any combination of the steps of the method in FIG.1A can be iteratively repeated any number of times. For example, steps 130, 140, 150 can be iteratively repeated to select an optimized configuration by testing a range of candidate configurations.

[0047] With reference to FIG. IB, an example method is shown according to implementations of the present disclosure.

[0048] At step 160 the method includes receiving a photoplethy smogram measured at a skin surface. Optionally, the method can further include receiving an additional optical measurement or measurements at the skin surface. For example the additional optical measurement can include a color measurement (e.g., spectrophotometer or spectroradiometer measurement). Alternatively or additionally, the additional optical measurement can be a fluid measurement (e.g., bioimpedance measurement), which can optionally be used to estimate skin moisture and / or reflectivity.

[0049] At step 170 the method includes estimating a calibration component of the photoplethysmogram caused by an optical property of the skin surface. The calibration component can be used to correct the PPG signal for the effects of the color and / or reflectivity of the skin. For example, the same illumination may be reflected more by wet, light skin and absorbed more by dry, dark skin, even when there is no difference in the underlying physiology. By estimating a calibration component that can include effects of color and / or moisture, the PPG signal can be corrected so that it represents the effects of the patient’s underlying physiology (e.g., heart rate, blood oxygen, blood pressure, etc.) and not the properties of the patient’s skin. This improves the reliability of medical devices that use PPG to sense underlying physiology of a patient, and can currently fail to adequately measure the underlying physiology of users with different skin tones, moistures, etc.Attorney Docket: 11687-018WO1

[0050] At step 180 the method includes outputting a corrected photoplethysmogram, wherein the corrected photoplethysmogram does not include the component caused by an optical property of the skin surface. Thus, the corrected photoplethysmogram can be a useful input to devices configured to measure heart rate, blood oxygen, blood pressure, etc. because the corrected photoplethysmogram minimizes or eliminates artifacts in the data caused by different skin properties.

[0051] With reference to FIG. 2, an example system according to implementations of the present disclosure is shown. Optionally, the system can be implemented as a wearable device 210. The wearable device 210 can include a controller 205. Optionally, the controller 205 can include any or all of the components of the computing device 500 described with reference to FIG. 5. The example system can be configured to implement any of the methods described with reference to FIGS. 1 A-1B or FIG. 5, for example by using the controller 205.

[0052] FIG. 2 illustrates an example of the wearable device 210 deployed on a skin surface of a subject. The example vascular bed is beneath the skin and includes an artery. Optionally, the vascular bed can include any vascular tissue (veins, capillaries, etc.) In the example shown in FIG. 2, the optical sensor 222 is a PPG sensor as described herein that illuminates the skin, vascular bed, and artery and measures reflected light over time (a PPG signal) and can thereby detect that the artery is changing in size as the heart pumps blood through the artery.

[0053] In some implementations, the system shown in FIG. 2 is configured to receive a plurality of physiological signals measured by the plurality of physiological sensors 220, and output a corrected optical measurement (e.g., a PPG measurement) that can correct for the effects of different skin tones, skin moisture, and / or other variations in the optical properties of the skin of a patient.

[0054] Still with reference to FIG. 2, the present disclosure contemplates the use of optical property sensors in addition to the optical sensor 222 to monitor optical properties of the skin. Optionally, the optical property sensor(s) can include a spectrophotometer or spectroradiometer 224 which can be configured to measure the color of the skin. The color of the skin can affect which wavelengths of light (e.g., light from the optical sensor 222) are absorbed and reflected, which can affect the signal received by the optical sensor. Implementation of the present disclosure can compensate the PPG signal or other optical measurement to compensate for the color of the skin (e.g., the skin tone).Attorney Docket: 11687-018WO1

[0055] Alternatively or additionally, the optical property sensor(s) can include a bioimpedance sensor 226. Implementations of the present disclosure can correlate the impedance of the skin, as measured by a bioimpedance sensor, with the amount of moisture on the surface of the skin. Moisture on the surface of the skin can correlate with skin reflectivity, which in turn can affect optical measurements (e.g., the intensity of light received at a photodiode) of the surface of the skin. For example, the underlying blood pressure or other cardiovascular condition of a patient may not change even as the patient’s skin becomes sweaty or wet over time. Accordingly, implementations of the present disclosure can compensate for skin moisture to correct a PPG signal or other optical measurement for the presence or absence of skin moisture.

[0056] The example system can further include a computing device 250 operably coupled to the wearable device 210. It should be understood that the computing device 250 and wearable device 210 can be coupled using any combination of wireless and / or wired connections, including Bluetooth, Wi-Fi, cellular / LTE, etc. Additionally, it should be understood that the computing device can be part of the wearable device in some implementations of the present disclosure. Alternatively or additionally, the computing device can be separate from the wearable device (e.g., a server). Optionally, the computing device 250 can be a cloud server.

[0057] The computing device 250 can include at least one processor and memory (not shown), where the memory includes computer-executable instructions that cause the processor to implement any of the methods described herein with reference to FIGS. 1 A and IB.

[0058] In some implementations, the system can further include a portable electronic device 230 operably coupled to the wearable device 210, where the portable electronic device 230 is configured to: receive the plurality of physiological signals from the plurality of physiological sensors 220; and transmit the plurality of physiological signals to the computing device 250. Optionally, the computing device 250 can transmit the physiological measurements to the portable electronic device 230.

[0059] In some implementations, the portable electronic device 230 can include a display (not shown), and the portable electronic device 230 can be configured to display the physiological measurement on a graphical user interface (GUI).

[0060] Example System

[0061] A block diagram of an example implementation is shown in FIG. 3. A set of LEDs 310 transmit light. The set of LEDs 310 can be configured to illuminate skinAttorney Docket: 11687-018WO1 and / or other tissue of a patient. A set of photodetectors 320 are configured to receive light from the set of LEDs 310. For example, the light can be scattered, reflected, etc. by the skin and / or other tissue of the patient in such a way that at least a portion of the illumination from the set of LEDs 310 is incident on the set of photodetectors 320.

[0062] It should be understood that any number of LEDs can be in the set of LEDs (e.g., 1, 2, 3, etc.) and that any number of photodetectors can be in the set of photodetectors 320 (e.g., 1, 2, 3, etc.).

[0063] The set of photodetectors 320 can be operably coupled to an analysis engine 330.The analysis engine 330 can optionally include a computing device (e.g., the computing device 500 shown in FIG. 5). The analysis engine 330 can be configured to output an optical measurement (e.g., a PPG signal). The output can be used as an input to any other device, for example a wearable PPG monitor or other system. The output can further be input into an application layer 340 configured to control the set of LEDs. Optionally, a single computing device can implement both the analysis engine 330 and the application layer 340. As described with reference to the methods of FIGS. 1A, IB, and 4, herein, the methods of the present disclosure can optionally be implemented by the analysis engine 330 and application layer 340 to both control the set of LEDs and output a best / optimized optical signal (e.g., PPG signal).

[0064] Example Method

[0065] With reference to FIG. 4, an example flowchart of an example method is shown according to implementations of the present disclosure. Information can be received from an application layer (e.g., from a computing device or network). Non-limiting examples of the types of information that can be received include demographics, skin tone classification, and / or any other physiological or health information.

[0066] The method can determine and apply control values for operating one or more optical devices (e.g., LEDs of a PPG sensor). Optionally, determining the control values can include iterating across different combinations of control values. For example, combinations can be searched in serial order, sweeps, random orders / sweeps, multifocal orders / sweeps, and / or based on user inputs.

[0067] Based on the control values selected, response values for the iterations can be collected and input to an analysis engine. The analysis engine can be configured to select the best response of the response values. The best response can be selected as the next control. An output can be locked as the reference value, and each output can be compared to the reference. Optionally, an additional sweep can be performed near the output value,Attorney Docket: 11687-018WO1 and a new best response can be determined as a new reference. It should be understood that the steps of the flowchart of FIG. 4 can be performed any number of times to iteratively select best responses for control. Alternatively or additionally, the steps of the flowchart of FIG. 4 can be performed at specified intervals (e.g., hourly, daily, monthly, etc.) to accommodate changing patient parameters over time.

[0068] Additional Examples

[0069] FIG. 6 illustrates an example block diagram showing layers of an example implementation stack, according to implementations of the present disclosure. The example implementation stack can include an application layer (e.g., user interface and / or configuration settings). The application layer can be configured to run on an operating system (OS) layer. Optionally the OS layer can include library components (e.g., software libraries configured to extend the functionality of programming languages or the operating system). Optionally, a hardware abstraction layer is configured to receive signals from a firmware layer that performs signal analysis. Optionally, the firmware layer is configured to receive signal data (e.g., digital information representing signals) from one or more sensors of a hardware layer. The systems and methods described herein can include any or all of these layers. For example, the hardware layer can include the sensors described herein, and the method steps described herein can be performed using any combination of the firmware layer, hardware abstraction layer, OS layer and / or application layer.

[0070] FIG. 7 illustrates a block diagram of a method of operating a device including multiple LEDs and photodetectors, according to implementations of the present disclosure. Any number of LEDs 702 can be configured to illuminate skin so that the light from the LEDs passes through and / or is reflected by the skin before reaching any number of photodetectors 704. The photodetectors 704 can be configured to generate an output electrical current, and the respective output electrical currents of any / all the photodetectors 704 can be measured into respective output current values summed to generate a composite sum. Optionally, the method of summing includes applying a weight to each current value and multiplying that weight by the current value. The composite sum can optionally represent the best configuration of LEDs for a measurement using LEDs (e.g., a PPG signal).

[0071] Optionally any number of composite sums can be determined based on different configurations of LEDs. FIG. 8 illustrates selecting a max of a composite sum, according to implementations of the present disclosure. Optionally, the “max” composite sumAttorney Docket: 11687-018WO1 represents a configuration of the LEDs that will have the highest quality (e.g., most accurate) output signal at the photodetectors.

[0072] Example Computing Device

[0073] It should be appreciated that the logical operations described herein with respect to the various figures may be implemented (1) as a sequence of computer implemented acts or program modules (i.e., software) running on a computing device (e.g., the computing device described in FIG. 5), (2) as interconnected machine logic circuits or circuit modules (i.e., hardware) within the computing device and / or (3) a combination of software and hardware of the computing device. Thus, the logical operations discussed herein are not limited to any specific combination of hardware and software. The implementation is a matter of choice dependent on the performance and other requirements of the computing device. Accordingly, the logical operations described herein are referred to variously as operations, structural devices, acts, or modules. These operations, structural devices, acts and modules may be implemented in software, in firmware, in special purpose digital logic, and any combination thereof. It should also be appreciated that more or fewer operations may be performed than shown in the figures and described herein. These operations may also be performed in a different order than those described herein.

[0074] Referring to FIG. 5, an example computing device 500 upon which the methods described herein may be implemented is illustrated. It should be understood that the example computing device 500 is only one example of a suitable computing environment upon which the methods described herein may be implemented. Optionally, the computing device 500 can be a well-known computing system including, but not limited to, personal computers, servers, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, network personal computers (PCs), minicomputers, mainframe computers, embedded systems, and / or distributed computing environments including a plurality of any of the above systems or devices. Distributed computing environments enable remote computing devices, which are connected to a communication network or other data transmission medium, to perform various tasks. In the distributed computing environment, the program modules, applications, and other data may be stored on local and / or remote computer storage media.

[0075] In its most basic configuration, computing device 500 typically includes at least one processing unit 506 and system memory 504. Depending on the exact configuration and type of computing device, system memory 504 may be volatile (such as randomAttorney Docket: 11687-018WO1 access memory (RAM)), non-volatile (such as read-only memory (ROM), flash memory, etc.), or some combination of the two. This most basic configuration is illustrated in FIG.5 by dashed line 502. The processing unit 506 may be a standard programmable processor that performs arithmetic and logic operations necessary for operation of the computing device 500. The computing device 500 may also include a bus or other communication mechanism for communicating information among various components of the computing device 500.

[0076] Computing device 500 may have additional features / functionality. For example, computing device 500 may include additional storage such as removable storage 508 and non-removable storage 510 including, but not limited to, magnetic or optical disks or tapes. Computing device 500 may also contain network connect! on(s) 516 that allow the device to communicate with other devices. Computing device 500 may also have input device(s) 514 such as a keyboard, mouse, touch screen, etc. Output device(s) 512 such as a display, speakers, printer, etc. may also be included. The additional devices may be connected to the bus in order to facilitate communication of data among the components of the computing device 500. All these devices are well known in the art and need not be discussed at length here.

[0077] The processing unit 506 may be configured to execute program code encoded in tangible, computer-readable media. Tangible, computer-readable media refers to any media that is capable of providing data that causes the computing device 500 (i.e., a machine) to operate in a particular fashion. Various computer-readable media may be utilized to provide instructions to the processing unit 506 for execution. Example tangible, computer-readable media may include, but is not limited to, volatile media, nonvolatile media, removable media and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. System memory 504, removable storage 508, and non-removable storage 510 are all examples of tangible, computer storage media. Example tangible, computer-readable recording media include, but are not limited to, an integrated circuit (e.g., field-programmable gate array or application-specific IC), a hard disk, an optical disk, a magneto-optical disk, a floppy disk, a magnetic tape, a holographic storage medium, a solid-state device, RAM, ROM, electrically erasable program read-only memory (EEPROM), flash memory or other memory technology, CD- ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices.Attorney Docket: 11687-018WO1

[0078] In an example implementation, the processing unit 506 may execute program code stored in the system memory 504. For example, the bus may carry data to the system memory 504, from which the processing unit 506 receives and executes instructions. The data received by the system memory 504 may optionally be stored on the removable storage 508 or the non-removable storage 510 before or after execution by the processing unit 506.

[0079] It should be understood that the various techniques described herein may be implemented in connection with hardware or software or, where appropriate, with a combination thereof. Thus, the methods and apparatuses of the presently disclosed subject matter, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium wherein, when the program code is loaded into and executed by a machine, such as a computing device, the machine becomes an apparatus for practicing the presently disclosed subject matter. In the case of program code execution on programmable computers, the computing device generally includes a processor, a storage medium readable by the processor (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. One or more programs may implement or utilize the processes described in connection with the presently disclosed subject matter, e.g., through the use of an application programming interface (API), reusable controls, or the like. Such programs may be implemented in a high level procedural or object-oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language and it may be combined with hardware implementations.

[0080] Definitions:

[0081] PPG Sensors: A photoplethysmogram (PPG) sensor can include one or more light sources (e.g., LEDs) and / or one or more light sensors (e.g., photodiodes, photoresistors, imaging sensors, etc.). By illuminating tissue of a patient (e.g., the surface of the skin) and measuring the absorbed light, a PPG sensor can measure light absorbance in the tissue. It should be understood that a PPG sensor is a type of optical sensor, so that references to “optical sensors” herein include PPG sensors, but it should also be understood that optical sensors include sensors that receive any kind of optical data, in and are not limited to sensors that collect PPG measurements. As described herein, PPGAttorney Docket: 11687-018WO1 sensors can be configured to take physiological measurements including blood volume, blood volume changes, and / or oxygen saturation.

[0082] PPG Signals: A PPG signal as used herein can refer to any measurement of light absorbance in any tissue. PPG signals can include measures of blood volume, blood volume changes, and / or oxygen saturation, for example.

[0083] Wearable Device(s): A device configured to be worn on / around any body part.Example types of wearable devices contemplated by the present disclosure include rings, wristbands, adhesive patches / straps / bandages, clamps (e.g., finger clamps), chest straps, etc. Example locations where wearable devices can be attached include any limb (wrists, fingers, fingertips, hands, feet, legs, arms), and / or any location on a patient’s head, neck, and / or torso, according to various implementations of the present disclosure.Additionally, the present disclosure contemplates that a wearable device and / or system including a wearable device can optionally be implemented using multiple sensors configured to be attached to different locations on the patient’s body (e.g., a first sensor configured to be worn on a fingertip, and a second sensor configured to be worn on the wrist). Additionally, the present disclosure contemplates that wearable devices can include computing devices that may not be located in the same position as the sensor(s). For example, a wearable device configured as a fingertip clamp or adhesive bandage may be in operable communication with a computing device that is not attached to the fingertip of the patient (e.g., in a wrist band or portable electronic device)

[0084] Physiological Measurement: Physiological measurements include any measurement of a patient’s physiology. Non-limiting examples of physiological measurements include blood pressure, body temperature, pulse rate, breathing rate, ECG (electrocardiography), ICG (impedance cardiography), galvanic skin response (GSR), skin conductance, motion sensors (e.g., accelerometers), etc. Physiological measurements can be stored as physiological data that can be processed using the computer- implemented methods described herein.

[0085] Physiological Signals and Waveform(s): Physiological signals can include any signals from any sensor configured to perform a physiological measurement. For example, if the sensor is a PPG sensor, the physiological signals can include the outputs of one or more photodiodes (or any other light / imaging sensor) used to measure light absorption for the PPG measurement. “Physiological waveforms” or “waveforms” refer to time-varying physiological signals. Physiological signals and / or waveforms as usedAttorney Docket: 11687-018WO1 herein can further refer to features of the physiological signals and / or their waveforms (e.g., peak amplitude, and / or frequency).

[0086] Ground-Truth Physiological Measurement: As used herein, a ground-truth physiological measurement is a physiological measurement used as a ground truth value to train the machine learning models described herein. Optionally, ground truth physiological measurements can be collected using conventional / standardized instruments. For example, a blood pressure cuff (sphygmomanometer) and / or invasive measurement of the pressure of an arterial line can be used to record ground truth blood pressure values that can be used as training data for the machine learning models described herein.

[0087] Physiological Sensor(s): Any sensor configured to measure the physiology of a patient. Example physiological sensors include: optical sensors, PPG sensors, electrocardiogram (ECG) electrodes and sensors (including any number of leads), temperature sensors, SpO2 (oxygen saturation) sensors, impedance cardiography (ICG) electrodes, accelerometers and / or gyroscopes, ultrasonic transducers, skin conductance sensors, phonocardiogram sensors, ballistocardiography sensors, piezoelectric / piezoresistive sensors, ultrasound sensors, arterial tonometry sensors, B- Type Natriuretic Peptide (BNP) sensors, and / or capacitive respiration sensors.

[0088] Patient Health State or Health State: As used herein, the “patient health state” or “health state” can include any information related to the health or demographics of a patient. For example, the patient health state can include a disease state (e.g., the presence of, absence of, and / or progression of a disease). Alternatively or additionally, the patient health state can include an activity profile of the patient. As yet another example, the patient health state can include physiological parameters of the patient including age, gender, weight, body mass index (BMI), medical history, ethnicity, health history, any other demographic information, and / or any combination thereof.

[0089] Activity Profile: As used herein, the activity profile can include any or all of: how active the patient is, physical activity, food intake profile(s), liquid intake profile(s) and / or when the patient is active.

[0090] Environmental Data: As used herein, “environmental data” can refer to any information about the environment that the patient is in, or may experience. Non-limiting examples of environmental data include location, altitude, weather condition(s), time zone changes or temperature, and / or any combination thereof.Attorney Docket: 11687-018WO1

[0091] Artificial Intelligence and Machine Learning

[0092] The term “artificial intelligence” is defined herein to include any technique that enables one or more computing devices or computing systems (i.e., a machine) to mimic human intelligence. Artificial intelligence (Al) includes, but is not limited to, knowledge bases, machine learning, representation learning, and deep learning. The term “machine learning” is defined herein to be a subset of Al that enables a machine to acquire knowledge by extracting patterns from raw data. Machine learning techniques include, but are not limited to, logistic regression, support vector machines (SVMs), decision trees, Naive Bayes classifiers, and artificial neural networks. The term “representation learning” is defined herein to be a subset of machine learning that enables a machine to automatically discover representations needed for feature detection, prediction, or classification from raw data. Representation learning techniques include, but are not limited to, autoencoders. The term “deep learning” is defined herein to be a subset of machine learning that enables a machine to automatically discover representations needed for feature detection, prediction, classification, etc. using layers of processing. Deep learning techniques include, but are not limited to, artificial neural network or multilayer perceptron (MLP).

[0093] Machine learning models include supervised, semi-supervised, and unsupervised learning models. In a supervised learning model, the model learns a function that maps an input (also known as feature or features) to an output (also known as target or target) during training with a labeled data set (or dataset). In an unsupervised learning model, the model learns a function that maps an input (also known as feature or features) to an output (also known as target or target) during training with an unlabeled data set. In a semi-supervised model, the model learns a function that maps an input (also known as feature or features) to an output (also known as target or target) during training with both labeled and unlabeled data.

[0094] Neural Networks

[0095] An artificial neural network (ANN) is a computing system including a plurality of interconnected neurons (e.g., also referred to as “nodes”). This disclosure contemplates that the nodes can be implemented using a computing device (e.g., a processing unit and memory as described herein). The nodes can be arranged in a plurality of layers such as input layer, output layer, and optionally one or more hidden layers. An ANN having hidden layers can be referred to as deep neural network or multilayer perceptron (MLP). Each node is connected to one or more other nodes in the ANN. For example, each layerAttorney Docket: 11687-018WO1 is made of a plurality of nodes, where each node is connected to all nodes in the previous layer. The nodes in a given layer are not interconnected with one another, i.e., the nodes in a given layer function independently of one another. As used herein, nodes in the input layer receive data from outside of the ANN, nodes in the hidden layer(s) modify the data between the input and output layers, and nodes in the output layer provide the results. Each node is configured to receive an input, implement an activation function (e.g., binary step, linear, sigmoid, tanH, or rectified linear unit (ReLU) function), and provide an output in accordance with the activation function. Additionally, each node is associated with a respective weight. ANNs are trained with a dataset to maximize or minimize an objective function. In some implementations, the objective function is a cost function, which is a measure of the ANN’S performance (e.g., error such as LI or L2 loss) during training, and the training algorithm tunes the node weights and / or bias to minimize the cost function. This disclosure contemplates that any algorithm that finds the maximum or minimum of the objective function can be used for training the ANN.Training algorithms for ANNs include, but are not limited to, backpropagation. It should be understood that an artificial neural network is provided only as an example machine learning model. This disclosure contemplates that the machine learning model can be any supervised learning model, semi-supervised learning model, or unsupervised learning model. Optionally, the machine learning model is a deep learning model. Machine learning models are known in the art and are therefore not described in further detail herein.

[0096] A convolutional neural network (CNN) is a type of deep neural network that has been applied, for example, to image analysis applications. Unlike a traditional neural networks, each layer in a CNN has a plurality of nodes arranged in three dimensions (width, height, depth). CNNs can include different types of layers, e.g., convolutional, pooling, and fully-connected (also referred to herein as “dense”) layers. A convolutional layer includes a set of filters and performs the bulk of the computations. A pooling layer is optionally inserted between convolutional layers to reduce the computational power and / or control overfitting (e.g., by downsampling). A fully-connected layer includes neurons, where each neuron is connected to all of the neurons in the previous layer. The layers are stacked similar to traditional neural networks. GCNNs are CNNs that have been adapted to work on structured datasets such as graphs.Attorney Docket: 11687-018WO1

[0097] Other Supervised Learning Models:

[0098] A logistic regression (LR) classifier is a supervised classification model that uses the logistic function to predict the probability of a target, which can be used for classification. LR classifiers are trained with a data set (also referred to herein as a “dataset”) to maximize or minimize an objective function, for example a measure of the LR classifier’s performance (e.g., error such as LI or L2 loss), during training. This disclosure contemplates that any algorithm that finds the minimum of the cost function can be used. LR classifiers are known in the art and are therefore not described in further detail herein.

[0099] An Naive Bayes’ (NB) classifier is a supervised classification model that is based on Bayes’ Theorem, which assumes independence among features (i.e., presence of one feature in a class is unrelated to presence of any other features). NB classifiers are trained with a data set by computing the conditional probability distribution of each feature given label and applying Bayes’ Theorem to compute conditional probability distribution of a label given an observation. NB classifiers are known in the art and are therefore not described in further detail herein.

[0100] A k-NN classifier is a supervised classification model that classifies new data points based on similarity measures (e.g., distance functions). k-NN classifiers are trained with a data set (also referred to herein as a “dataset”) to maximize or minimize an objective function, for example a measure of the k-NN classifier’s performance, during training. This disclosure contemplates that any algorithm that finds the maximum or minimum of the objective function can be used. k-NN classifiers are known in the art and are therefore not described in further detail herein.

[0101] A majority voting ensemble is a meta-classifier that combines a plurality of machine learning classifiers for classification via majority voting. In other words, the majority voting ensemble’s final prediction (e.g., class label) is the one predicted most frequently by the member classification models. Majority voting ensembles are known in the art and are therefore not described in further detail herein.

Claims

Attorney Docket: 11687-018WO1CLAIMS WHAT IS CLAIMED:

1. A method compri sing :(a) determining an initial configuration of an optical sensor;(b) determining an initial signal quality index using the optical sensor configured in the initial configuration;(c) selecting a candidate configuration of the optical sensor;(d) determining a candidate signal quality index using the optical sensor configured in the candidate configuration; and(e) determining an optimized configuration of the optical sensor based on at least the candidate signal quality index and the initial signal quality index.

2. The method of claim 1, wherein steps (c)-(e) are iteratively repeated.

3. The method of claim 1 or claim 2, wherein determining an optimized configuration of the optical sensor comprises selecting between the initial configuration of the optical sensor and the candidate configuration of the optical sensor based on the candidate signal quality index and the initial signal quality index.

4. The method of any one of claims 1-3, wherein the optical sensor comprises a photodiode.

5. The method of any one of claims 1-4, wherein the optical sensor comprises a plurality of photodiodes.

6. The method of any one of claims 1-5, wherein the initial configuration and the candidate configuration of the optical sensor comprises a drive value for the optical sensor.

7. The method of any one of claims 1-6, wherein the optical sensor comprises a PPG sensor.

8. The method of any one of claims 1-7, further comprising configuring the sensor in the optimal configuration.

9. A method of optical sensor calibration, comprising:receiving a photopl ethy smogram measured at a skin surface;Attorney Docket: 11687-018WO1 estimating a calibration component of the photoplethysmogram caused by an optical property of the skin surface; andoutputting a corrected photoplethysmogram, wherein the corrected photoplethysmogram does not include the component caused by an optical property of the skin surface.

10. The method of claim 9, further comprising receiving a color measurement of the skin surface, and wherein the optical property comprises color.

11. The method of claim 10, wherein the color measurement comprises a spectrophotometer measurement.

12. The method of claim 10, wherein the color measurement comprises a spectroradiometer measurement.

13. The method of any one of claims 9-12, further comprising receiving a fluid measurement, and wherein the optical property comprises surface moisture.

14. The method of claim 13, wherein the fluid measurement comprises a bioimpedance measurement.

15. A system for compensated optical measurement, the system comprising:an optical sensor, wherein the optical sensor is configured to measure a light intensity;an optical property sensor, wherein the optical property sensor is configured to measure an optical property of a skin surface;a controller operably coupled to the optical sensor and the optical property sensor, wherein the controller is configured to:implement the methods of any one of claims 1-14.

16. The system of claim 15, wherein the optical sensor comprises a PPG sensor.

17. The system of claim 15 or claim 16, wherein the optical property sensor comprises a spectrophotometer or spectroradiometer.Attorney Docket: 11687-018WO118. The system of claim 15 or claim 16, wherein the optical property sensor comprises a bioimpedance sensor.

Images