Non-invasive blood pressure estimation and vessel monitoring based on photoacoustic plethysmography

Abstract

Some disclosed methods involve controlling, via a control system, a light source system to emit a plurality of light pulses into a biological tissue at a pulse repetition frequency, the biological tissue including blood and blood vessels at various depths within the biological tissue. Such methods can involve receiving, by the control system from a piezoelectric receiver, signals corresponding to acoustic waves emitted from portions of the biological tissue corresponding to photoacoustic emissions from the blood and blood vessels caused by the plurality of light pulses. Such methods can involve detecting, by the control system, heart rate waveforms in the signals, determining, by the control system, a first subset of the detected heart rate waveforms corresponding to venous heart rate waveforms, and determining, by the control system, a second subset of the detected heart rate waveforms corresponding to arterial heart rate waveforms.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to U.S. Patent Application No. 17 / 247,323, filed December 7, 2020, entitled “NON-INVASIVE BLOOD PRESSUREESTIMATION AND BLOOD VESSEL MONITORING BASED ON PHOTOACOUSTICPLETHYSMOGRAPHY”, which is incorporated herein by reference and for all purposes. Technical Field

[0003] This disclosure generally relates to non-invasive blood pressure estimation and vascular monitoring. Background Technology

[0004] Various sensing technologies and algorithms are being researched for a wide range of biomedical applications, including health and wellness monitoring. This push is partly a result of the limitations of traditional measurement devices in terms of availability for continuous, non-invasive, and continuous monitoring. For example, a blood pressure monitor is an example of a traditional blood pressure monitoring device that uses an inflatable cuff to apply counter-pressure to an area of ​​interest (e.g., around the subject's upper arm). The pressure applied by the inflatable cuff is designed to restrict arterial blood flow to provide measurements of systolic and diastolic blood pressure. This type of traditional blood pressure monitor inherently affects the subject's physiological state, which can introduce errors in blood pressure measurement. It also affects the subject's psychological state, which can itself manifest as changes in physiological state and thus introduce errors in blood pressure measurement. For example, such devices are often used primarily in isolated situations, such as when a subject visits a doctor's office or receives treatment in a hospital setting. Naturally, some subjects experience anxiety during such situations, and this anxiety can affect (e.g., increase) the user's blood pressure and heart rate.

[0005] For these and other reasons, such devices may not provide an accurate estimate or "picture" of blood pressure, and generally a user's health, over time. While implantable or other invasive devices can provide a better estimate of blood pressure over time, these invasive devices generally involve greater risks than non-invasive devices and are generally not suitable for ambulatory use. Summary of the Invention

[0006] The systems, methods, and apparatus disclosed herein each have several aspects, none of which individually assumes the desired properties disclosed herein.

[0007] One innovative aspect of the subject matter described in this disclosure can be implemented in an apparatus or in a system including the apparatus. The apparatus may include an ultrasonic receiver (e.g., a piezoelectric receiver), a light source system, and a control system. In some examples, the light source system may be configured to emit multiple light pulses at a pulse repetition frequency between 10 Hz and 1 MHz. In some embodiments, a mobile device (such as a wearable device) may be, or may include, at least a portion of, the apparatus.

[0008] The control system may include one or more general-purpose single-chip or multi-chip processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or combinations thereof. The control system may be configured to control a light source system to emit multiple light pulses at a pulse repetition frequency into biological tissue. The biological tissue may, for example, include blood and blood vessels at various depths within the biological tissue.

[0009] The control system can be configured to receive signals from a piezoelectric receiver corresponding to sound waves emitted from various parts of biological tissue. These sound waves may, for example, correspond to photoacoustic emissions from blood and blood vessels caused by the plurality of light pulses. The control system can be configured to detect heart rate waveforms in the signals. The control system can be configured to determine a first subset of the detected heart rate waveforms corresponding to venous heart rate waveforms. The control system can be configured to determine a second subset of the detected heart rate waveforms corresponding to arterial heart rate waveforms.

[0010] According to some implementations, the control system can also be configured to extract heart rate waveform features from the heart rate waveform. According to some such implementations, the control system can also be configured to perform blood pressure estimation based at least in part on the extracted heart rate waveform features.

[0011] In some examples, receiving signals from a piezoelectric receiver may involve obtaining a depth-discriminated signal by applying first to Nth acquisition time delays and receiving first to Nth signals during the first to Nth acquisition time windows, where N is an integer greater than one. In some such examples, each of the first to Nth acquisition time windows may occur after a corresponding acquisition time delay in the first to Nth acquisition time delays. According to some implementations, the control system may be configured to determine a first subset and a second subset of detected heart rate waveforms based at least in part on the depth-discriminated signal.

[0012] According to some embodiments, the control system may also be configured to extract a set of hemodynamic features from a second subset of the detected heart rate waveforms, and to perform a first blood pressure estimate based at least in part on the set of hemodynamic features. In some such embodiments, the control system may also be configured to determine arterial-venous phase shift (AVPS) data from the first subset and the second subset of the detected heart rate waveforms, and to perform a first blood pressure estimate based at least in part on the AVPS data.

[0013] In some examples, the control system may also be configured to extract heart rate waveform features from the heart rate waveform and to perform a second blood pressure estimate based at least in part on the extracted heart rate waveform features. In some such embodiments, the control system may also be configured to perform a third blood pressure estimate based at least in part on the first and second blood pressure estimates.

[0014] In some embodiments, the control system may also be configured to determine AVPS data from the heart rate waveform and to perform a first blood pressure estimate based at least in part on the AVPS data. In some such embodiments, the control system may also be configured to extract heart rate waveform features from the heart rate waveform and to perform a second blood pressure estimate based at least in part on the extracted heart rate waveform features. According to some such embodiments, the control system may also be configured to perform a third blood pressure estimate based at least in part on the first and second blood pressure estimates.

[0015] Other inventive aspects of the subject matter described in this disclosure can be implemented in methods (such as bioassay methods). The method may involve controlling a light source system via a control system to emit multiple light pulses at a pulse repetition frequency into biological tissue. In some instances, the biological tissue may include blood and blood vessels at various depths within the biological tissue. The method may involve receiving signals corresponding to acoustic waves emitted from various portions of the biological tissue from a piezoelectric receiver by the control system. In some instances, the acoustic waves may correspond to photoacoustic emissions from blood and blood vessels caused by the multiple light pulses. The method may involve detecting heart rate waveforms in these signals by the control system. The method may involve determining a first subset of detected heart rate waveforms corresponding to venous heart rate waveforms by the control system. The method may involve determining a second subset of detected heart rate waveforms corresponding to arterial heart rate waveforms by the control system.

[0016] In some examples, the method may involve the control system extracting heart rate waveform features from the heart rate waveform. The method may also involve the control system performing blood pressure estimation based at least in part on the extracted heart rate waveform features.

[0017] In some implementations, receiving signals from a piezoelectric receiver may involve obtaining a depth-discriminating signal by applying first to Nth acquisition time delays and receiving first to Nth signals during the first to Nth acquisition time windows, where N is an integer greater than one. In some such examples, each of the first to Nth acquisition time windows may occur after a corresponding acquisition time delay in the first to Nth acquisition time delays. According to some implementations, the method may involve determining a first subset and a second subset of detected heart rate waveforms based at least in part on the depth-discriminating signal.

[0018] According to some implementations, the method may involve extracting a set of hemodynamic features from a second subset of detected heart rate waveforms, and performing a first blood pressure estimate based at least in part on the set of hemodynamic features. In some such implementations, the method may involve determining AVPS data from a first subset and a second subset of detected heart rate waveforms, and performing a first blood pressure estimate based at least in part on the AVPS data.

[0019] In some examples, the method may involve extracting heart rate waveform features from a heart rate waveform and performing a second blood pressure estimate based at least in part on the extracted heart rate waveform features. In some such implementations, the method may involve performing a third blood pressure estimate based at least in part on a first blood pressure estimate and a second blood pressure estimate.

[0020] According to some embodiments, the method may involve a control system determining AVPS data from a heart rate waveform and performing a first blood pressure estimate by the control system based at least in part on the AVPS data. According to some such embodiments, the method may involve a control system extracting heart rate waveform features from a heart rate waveform and performing a second blood pressure estimate by the control system based at least in part on the extracted heart rate waveform features. According to some such embodiments, the method may involve a third blood pressure estimate by the control system based at least in part on the first and second blood pressure estimates.

[0021] Some or all of the methods described herein can be executed by one or more devices according to instructions (e.g., software) stored on a non-transitory medium. Such non-transitory media can include memory devices such as those described herein, including but not limited to random access memory (RAM) devices, read-only memory (ROM) devices, etc. Accordingly, some innovative aspects of the subject matter described herein can be implemented in one or more non-transitory media on which software is stored. The software can include instructions for controlling one or more devices to perform one or more disclosed methods.

[0022] One such method may involve controlling a light source system via a control system to emit multiple light pulses at a pulse repetition frequency into biological tissue. In some instances, the biological tissue may include blood and blood vessels at various depths within the biological tissue. The method may involve the control system receiving signals from a piezoelectric receiver corresponding to sound waves emitted from various portions of the biological tissue. In some instances, the sound waves may correspond to photoacoustic emissions from blood and blood vessels caused by the multiple light pulses. The method may involve the control system detecting heart rate waveforms in these signals. The method may involve the control system determining a first subset of the detected heart rate waveforms corresponding to venous heart rate waveforms. The method may involve the control system determining a second subset of the detected heart rate waveforms corresponding to arterial heart rate waveforms.

[0023] In some examples, the method may involve the control system extracting heart rate waveform features from the heart rate waveform. The method may also involve the control system performing blood pressure estimation based at least in part on the extracted heart rate waveform features.

[0024] In some implementations, receiving signals from a piezoelectric receiver may involve obtaining a depth-discriminating signal by applying first to Nth acquisition time delays and receiving first to Nth signals during the first to Nth acquisition time windows, where N is an integer greater than one. In some such examples, each of the first to Nth acquisition time windows may occur after a corresponding acquisition time delay in the first to Nth acquisition time delays. According to some implementations, the method may involve determining a first subset and a second subset of detected heart rate waveforms based at least in part on the depth-discriminating signal.

[0025] According to some implementations, the method may involve extracting a set of hemodynamic features from a second subset of detected heart rate waveforms and performing a first blood pressure estimate based at least in part on the set of hemodynamic features. In some such implementations, the method may involve determining AVPS data from a first subset and a second subset of detected heart rate waveforms and performing a first blood pressure estimate based at least in part on the AVPS data.

[0026] In some examples, the method may involve extracting heart rate waveform features from a heart rate waveform and performing a second blood pressure estimate based at least in part on the extracted heart rate waveform features. In some such implementations, the method may involve performing a third blood pressure estimate based at least in part on a first blood pressure estimate and a second blood pressure estimate.

[0027] According to some embodiments, the method may involve a control system determining AVPS data from a heart rate waveform and performing a first blood pressure estimate by the control system based at least in part on the AVPS data. According to some such embodiments, the method may involve a control system extracting heart rate waveform features from a heart rate waveform and performing a second blood pressure estimate by the control system based at least in part on the extracted heart rate waveform features. According to some such embodiments, the method may involve a third blood pressure estimate by the control system based at least in part on the first and second blood pressure estimates.

[0028] Details of one or more embodiments of the subject matter described in this disclosure are set forth in the accompanying drawings and the following description. Other features, aspects, and advantages will become apparent from this description, the drawings, and the claims. It should be noted that the relative dimensions in the following drawings may not be drawn to scale. Attached Figure Description

[0029] Figure 1A A plot of blood pressure signals versus time in a sample artery during a sample cardiac cycle is shown.

[0030] Figure 1B An example of a blood pressure monitoring device based on photoplethysmography (PPG) is shown.

[0031] Figure 1C An example of two superimposed graphs showing blood pressure changes during the cardiac cycle is shown.

[0032] Figure 1D An example of a blood pressure monitoring device based on photoacoustic volume plethysmography (which may be referred to herein as PAPG) is shown.

[0033] Figure 2 This is a block diagram illustrating example components of an apparatus according to some disclosed embodiments.

[0034] Figure 3 It is a flowchart that provides examples of some of the publicly disclosed operations.

[0035] Figure 4A An example of a range-gate window (RGW) selected for receiving sound waves emitted from different depth ranges is shown.

[0036] Figure 4B Examples are shown where multiple acquisition time delays are selected to receive sound waves emitted from different depths.

[0037] Figure 5A and Figure 5B An example of a device configured to receive sound waves emitted from different depths is shown.

[0038] Figure 6 It shows that it can execute Figure 3 An example of a cross-sectional view of the apparatus for the method.

[0039] Figure 7 Examples of venous heart rate waveforms and arterial heart rate waveforms are shown.

[0040] Figure 8 Examples of determining venous heart rate waveforms and arterial heart rate waveforms are shown.

[0041] Figure 9 This is a diagram representing aspects of the five disclosed PAPG algorithms.

[0042] Figure 10A and Figure 10B An example of a block showing the data acquisition and heart rate waveform (HRW) determination process is shown.

[0043] Figure 11 Examples of some of the blocks for the disclosed methods are shown.

[0044] Figure 12 It shows that it can be based on Figure 11 Examples of some implementations of the method for extracting heart rate waveform (HRW) features.

[0045] Figure 13 This is a flowchart illustrating an example block for blood pressure estimation based on hemodynamic characteristics.

[0046] Figure 14 This is a flowchart providing an example of blood pressure estimation based on both HRW analysis and hemodynamic analysis.

[0047] Figure 15 An example of a device that can be used in a system for estimating blood pressure based at least in part on pulse conduction time (PTT) is shown.

[0048] Figure 16 A cross-sectional side view of a portion of artery 1600 is shown, through which pulse 1602 propagates.

[0049] Figure 17A An example non-fixed (ambulatory) monitoring device 1700 designed to be worn on the wrist is shown according to some embodiments.

[0050] Figure 17B An example non-fixed monitoring device 1700 designed to be worn on a finger is shown according to some embodiments.

[0051] Figure 17CAn example non-fixed monitoring device 1700 designed to reside on an earpiece is shown according to some embodiments.

[0052] Similar reference numerals and naming conventions in the various figures indicate similar elements. Detailed Implementation

[0053] The following description is directed to certain embodiments to illustrate various aspects of this disclosure. However, those skilled in the art will readily recognize that the teachings herein can be applied in many different ways. Some of the concepts and examples provided in this disclosure are particularly applicable to blood pressure monitoring applications. However, some embodiments are also applicable to other types of biosensing applications, as well as other fluid flow systems. The described embodiments can be implemented in any device, apparatus, or system that includes devices as disclosed herein. Furthermore, it is contemplated that the described embodiments can be included in or associated with a variety of electronic devices, such as, but not limited to: mobile phones, Internet-enabled multimedia cellular phones, mobile TV receivers, wireless devices, smartphones, smart cards, wearable devices (such as wristbands, armbands, wrist straps, rings, headbands, patches, etc.). Devices, personal data assistants (PDAs), wireless email receivers, handheld or portable computers, netbooks, laptops, smartbooks, tablets, printers, copiers, scanners, fax machines, GPS receivers / navigators, cameras, digital media players, game control panels, wristwatches, clocks, calculators, television monitors, flat panel displays, electronic reading devices (e.g., e-readers), mobile health devices, computer monitors, automotive displays (including odometer and speedometer displays, etc.), cockpit controls and / or displays, camera view displays (such as rearview camera displays in vehicles), building structures, microwave ovens, refrigerators, stereo systems, cassette recorders or players, DVD players, CD players, VCRs, radios, portable memory chips, washing machines, dryers, washer / dryer units, parking meters, car doors, autonomous or semi-autonomous vehicles, drones, Internet of Things (IoT) devices, etc. Therefore, these teachings are not intended to limit them to the specific embodiments depicted and described with reference to the accompanying drawings; rather, these teachings have broad applicability, as will be apparent to those skilled in the art.

[0054] It should also be noted that, unless otherwise indicated, the conjunction “or” as used herein is intended to have an inclusive meaning where appropriate; that is, the phrase “A, B, or C” is intended to include the following possibilities: A alone; B alone; C alone; A and B but not C; B and C but not A; A and C but not B; and A, B, and C. Similarly, the phrase “at least one of…” in a list of items refers to any combination of these items, including a single member. As an example, the phrase “at least one of A, B, or C” is intended to cover the following possibilities: at least one of A; at least one of B; at least one of C; at least one of A and at least one of B; at least one of B and at least one of C; at least one of A and at least one of C; and at least one of A, at least one of B, and at least one of C.

[0055] Specific embodiments of the subject matter described herein can be implemented to achieve one or more of the following potential advantages. Some embodiments of the portable monitoring devices described herein are also designed to consume relatively little power, thereby enabling continuous wear and monitoring of biosignals of interest (such as blood pressure) for extended periods (e.g., hours, days, weeks, or even a month or longer) without external calibration, recharging, or other interruptions. Continuous monitoring provides greater prognostic and diagnostic value compared to isolated measurements, such as those obtained in a hospital or physician's office setting. Some embodiments of the portable or "non-fixed" monitoring devices described herein are also designed to have a small form factor and a housing that can be coupled to a subject (also referred to herein as a "patient," "person," or "user") for wearable, non-invasive, and non-restrictive non-fixed use. In other words, some embodiments of the mobile monitoring devices described herein do not restrict the free and unimpeded movement of the subject's arms or legs, thereby enabling continuous or periodic monitoring of cardiovascular characteristics (such as blood pressure) even when the subject is moving or otherwise physically active. Such devices not only do not interfere with the subject's daily or other desired activities, but this lack of interference also encourages continuous wear. In some implementations, it may be further expected that the subject may not be aware of when the (multiple) sensing devices of the non-fixed monitoring equipment actually perform the measurement.

[0056] Furthermore, some of the disclosed embodiments offer advantages over previously deployed non-invasive blood pressure monitoring devices, such as those based on photoplethysmography (PPG). PPG-based blood pressure monitoring devices are not optimal because PPG superimposes data corresponding to the blood volume of all irradiated vessels (arteries, veins, etc.), where each vessel exhibits unique blood volume changes over time, resulting in a mixed signal that is not closely correlated with blood pressure and is prone to drift. In contrast, some disclosed devices employ depth-discriminative photoplethysmography (PAPG) methods, which can distinguish arterial heart rate waveforms from venous heart rate waveforms and other heart rate waveforms. Blood pressure estimation based on depth-discriminative PAPG methods can be more accurate than blood pressure estimation based on PPG methods. Some disclosed methods have the additional potential advantage of employing more than one type of depth-discriminative PAPG-based blood pressure estimation method, thus providing potentially more reliable blood pressure estimates. Additionally or alternatively, some disclosed methods have the additional potential advantage of providing one or more PAPG-based blood pressure estimation methods based on pulse conduction time (PTT).

[0057] As used herein, the term "pulse pressure" refers to the difference between the systolic and diastolic blood pressure for a given cardiac cycle. Pulse pressure is generally unaffected by localized changes in hydrostatic pressure in arteries in the peripheral regions of a subject's body. As used herein, the term "transmural pressure" refers to the pressure difference between the pressure within an artery and the pressure immediately outside that artery at a specific time and location along that artery. Unlike pulse pressure, transmural pressure depends on hydrostatic pressure. For example, if the sensing device is coupled to the subject's wrist, changing the wrist height will result in a significant change in the transmural pressure measured at the wrist, while pulse pressure is generally relatively unaffected (assuming the subject's condition remains otherwise unchanged). As used herein, the term "absolute arterial pressure" refers to the actual pressure within an artery at a specific time and location along that artery. Generally, absolute arterial pressure is relatively consistent with transmural pressure, provided that no significant external pressure is applied to the artery (such as back pressure from an inflatable cuff or other external device). For many purposes and intentions, it may be assumed that transmural pressure is approximately equal to absolute arterial pressure, and thus, the terms “absolute arterial pressure” and “transmural pressure” are used interchangeably hereinafter where appropriate, unless otherwise stated. As used herein, the term “blood pressure” refers to the general term for pressure in the arterial system of a subject. Thus, the terms transmural pressure, absolute arterial pressure, pulse pressure, systolic pressure, and diastolic pressure may all be generally referred to as blood pressure hereinafter.

[0058] Figure 1AA plot 100 of blood pressure signals in a sample artery during an example cardiac cycle is shown. While plot 100 is a plot of blood pressure versus time, it also indicates the arterial stretch waveform. As indicated above, a plot of blood flow versus time would exhibit characteristics similar to the blood pressure versus time plot 100, although the specific shapes of these characteristics would differ slightly. As those skilled in the art will appreciate, each cardiac cycle 102 includes a systolic phase (“ventricular systole”) 104 and a diastolic phase (“ventricular diastole”) 106, during which the left ventricle of the heart contracts and pumps blood into the arterial system, and during diastole 106, the left ventricle relaxes and fills with blood in preparation for the next systolic phase. Since each cardiac cycle 102 generates a corresponding pressure pulse, the arterial stretch waveform associated with each pressure pulse also includes characteristics with systolic and diastolic properties. For example, systole 104 is characterized by a rapid rise in pressure to a local maximum or peak 108 (“systolic pressure”) in response to the injection of blood from the left ventricle during a given cardiac cycle 102. Conversely, diastole 106 is characterized by a significant drop in blood pressure to a local minimum 110 (“diastolic pressure”) during a given cardiac cycle 102 due to the relaxation of the left ventricle. In fact, the end of diastole 106 can generally be characterized by exponentially decaying blood pressure that asymptotically approaches a pressure 112 lower than typical diastolic pressure (referred to herein as “infinity pressure”) (blood pressure never reaches infinity pressure because the exponential decay is interrupted by the systole of the next cardiac cycle, as illustrated).

[0059] Figure 1B An example of a blood pressure monitoring device based on photoplethysmography (PPG) is shown. Figure 1B Examples of arteries, veins, arterioles, venules, and capillaries of the circulatory system are shown, including those within the finger 115. Figure 1B In the example shown, the electrocardiogram sensor has detected a proximal arterial pulse near the heart 116. Below are some examples of measuring arterial pulse conduction time (PTT) based on arterial pulses measured by two sensors, one of which may be an electrocardiogram sensor in some implementations.

[0060] according to Figure 1B The example shown includes a light source comprising one or more light-emitting diodes (LEDs) emitting light (in some examples, green, red, and / or near-infrared (NIR) light) that penetrates the tissue of the finger 115 in the irradiated area. The reflection from these tissues, detected by a photodetector, can be used to detect changes in blood volume in the irradiated area of ​​the finger 115 corresponding to a heart rate waveform.

[0061] like Figure 1BAs shown in heart rate waveform graph 118, capillary heart rate waveform 119 has a different shape and phase shift relative to arterial heart rate waveform 117. In this simplified example, the detected heart rate waveform 121 is a combination of capillary heart rate waveform 119 and arterial heart rate waveform 117. In some instances, the response of one or more other vessels may also be a portion of the heart rate waveform 121 detected by a PPG-based blood pressure monitoring device.

[0062] Figure 1C An example of two superimposed graphs showing blood pressure changes during the cardiac cycle is shown. Graph 123 corresponds to blood pressure measured by catheter, which is considered a sufficiently reliable method as the "ground truth" against which blood pressure estimation methods can be compared. In this example, graph 125 corresponds to blood pressure estimated by a PPG-based method. Figure 1C In the example shown, the area between curves 123 and 125 indicates the error in blood pressure estimation based on the PPG-based method.

[0063] By comparison Figure 1B Heart rate waveform curves 118 and Figure 1C The blood pressure curve graph shows that PPG-based blood pressure monitoring devices are not optimal because PPG superimposes data corresponding to the blood volume of all irradiated blood vessels, resulting in different and time-shifted blood volume changes for each vessel.

[0064] according to Figure 1B The example shown includes a light source comprising one or more LEDs emitting light (in some examples, green, red, and / or near-infrared (NIR) light) that penetrates the tissue of the finger 115 in the irradiated area. The reflection from these tissues, detected by a photodetector, can be used to detect changes in blood volume in the irradiated area of ​​the finger 115 corresponding to a heart rate waveform.

[0065] Figure 1D An example of a blood pressure monitoring device based on photoacoustic volume plethysmography (which may be referred to herein as PAPG) is shown. Figure 1D It shows Figure 1B The same example of arteries, veins, arterioles, venules, and capillaries within the finger 115 shown. In some examples, Figure 1D The light source shown may be, or may include, one or more LEDs or laser diodes. In this example, as in... Figure 1B As in the example, the light source emits light (in some examples, green, red, and / or near-infrared (NIR) light) that penetrates the tissue of the finger 115 in the irradiated area.

[0066] exist Figure 1DIn the example shown, blood vessels (and components of the blood itself) are heated by incident light from a light source and emit sound waves. In this example, the emitted sound waves include ultrasound. According to this embodiment, the sound wave emission is detected by an ultrasound receiver (in this example, a piezoelectric receiver). The photoacoustic emission from the irradiated tissue detected by the piezoelectric receiver can be used to detect changes in blood volume in the irradiated area of ​​the finger 115 corresponding to a heart rate waveform. In some examples, the ultrasound receiver may correspond to the reference below. Figure 2 The ultrasonic receiver 202 described.

[0067] Figure 1B PPG-based systems and Figure 1D One important difference between PAPG-based methods is that Figure 1D The sound wave ratio shown Figure 1B The reflected light waves shown travel much slower. Accordingly, based on Figure 1D The depth difference in the arrival time of the sound waves shown is possible, and based on Figure 1B The depth difference in the arrival time of the light waves shown may be impossible. This depth difference allows some of the disclosed embodiments to separate sound waves received from different blood vessels.

[0068] According to some such examples, this depth discrimination allows arterial heart rate waveforms to be distinguished from venous heart rate waveforms and other heart rate waveforms. Therefore, blood pressure estimation based on depth discrimination PAPG methods can be more accurate than that based on PPG-based methods. Some of the disclosed methods have the additional potential advantage of applying more than one type of blood pressure estimation method based on depth discrimination PAPG methods, thus providing potentially more reliable blood pressure estimates.

[0069] Figure 2 This is a block diagram illustrating example components of an apparatus according to some disclosed embodiments. In this example, apparatus 200 includes a biometric system. Here, the biometric system includes an ultrasound receiver 202, a light source system 204, and a control system 206. Although Figure 2 Not shown, but device 200 may include a substrate. In some examples, device 200 may include a pressure plate. Some examples are described below. Some embodiments of device 200 may include interface system 208 and / or display system 210.

[0070] This document discloses various examples of an ultrasonic receiver 202, some of which may include or be configured as (or configurable as) an ultrasonic transmitter, and some of which may not include or be configured as (or not configurable as) an ultrasonic transmitter. In some embodiments, the ultrasonic receiver 202 and the ultrasonic transmitter may be combined in an ultrasonic transceiver. In some examples, the ultrasonic receiver 202 may include a piezoelectric receiver layer, such as a PVDF polymer layer or a PVDF-TrFE copolymer layer. In some embodiments, a single piezoelectric layer may act as an ultrasonic receiver. In some embodiments, other piezoelectric materials, such as aluminum nitride (AlN) or lead zirconate titanate (PZT), may be used in the piezoelectric layer. In some examples, the ultrasonic receiver 202 may include an array of ultrasonic transducer components, such as a piezoelectric micromechanical ultrasonic transducer (PMUT) array, a capacitive micromechanical ultrasonic transducer (CMUT) array, etc. In some such examples, a piezoelectric receiver layer, a PMUT element in a single-layer PMUT array, or a CMUT element in a single-layer CMUT array can be used as both an ultrasonic transmitter and an ultrasonic receiver. According to some examples, ultrasonic receiver 202 may be or may include an ultrasonic receiver array. In some examples, device 200 may include one or more individual ultrasonic transmitter elements. In some such examples, the ultrasonic transmitter may include an ultrasonic plane wave generator.

[0071] In some examples, the light source system 204 may include an array of light-emitting diodes (LEDs). In some embodiments, the light source system 204 may include one or more laser diodes. According to some embodiments, the light source system may include at least one infrared, red, green, blue, white, or ultraviolet LED. In some embodiments, the light source system 204 may include one or more laser diodes. For example, the light source system 204 may include at least one infrared, red, green, blue, white, or ultraviolet laser diode. In some embodiments, the light source system 204 may include one or more organic LEDs (OLEDs).

[0072] In some embodiments, the light source system 204 can be configured to emit light of various wavelengths, selectable to achieve greater penetration into biological tissue and / or trigger photoacoustic emission primarily from specific types of material. For example, because near-infrared (near-IR) light is not absorbed as strongly by some types of biological tissue (such as melanin and vascular tissue) as relatively short wavelength light, in some embodiments, the light source system 204 can be configured to emit light of one or more wavelengths in the near-IR range to obtain photoacoustic emission from relatively deep biological tissue. In some such embodiments, the control system 206 can control the wavelength(s) of the light emitted by the light source system 204 in the range of 750 to 850 nm (e.g., 808 nm). However, heme absorbs near-IR light as much as it absorbs light with shorter wavelengths (e.g., ultraviolet, violet, blue, or green light). Near-IR light can produce suitable photoacoustic emission from some blood vessels (e.g., 1 mm or larger in diameter), but not necessarily from very small blood vessels. To achieve greater photoacoustic emission generally from blood, and particularly from smaller blood vessels, in some embodiments, the control system 206 may control the wavelength(s) of the light emitted by the light source system 204 to be in the range of 495 to 570 nm (e.g., 520 nm or 532 nm). Wavelengths in this range are more strongly absorbed by biological tissue and therefore may not penetrate deeply into biological tissue, but can produce relatively stronger photoacoustic emission in blood than near-IR light. In some examples, the control system 206 may control the wavelength(s) of the light emitted by the light source system 204 to preferably induce sound waves in blood vessels, other soft tissues, and / or bones. For example, an infrared (IR) light-emitting diode (LED) can be selected and emits short pulses of IR light to irradiate a portion of a target object and generate acoustic emission, which is subsequently detected by an ultrasound receiver 202. In another example, an IR LED and a red LED or other color (such as green, blue, white, or ultraviolet (UV)) can be selected and short pulses of light can be emitted sequentially from each light source, wherein an ultrasound image is obtained after light is emitted from each light source. In other embodiments, one or more light sources of different wavelengths may be fired sequentially or simultaneously to produce acoustic emissions detectable by an ultrasonic receiver. Image data from the ultrasonic receiver, obtained using light sources of different wavelengths and at different depths within the target object (e.g., discussed in detail below), can be combined to determine the location and type of material within the target object. Image contrast may occur because materials in the body generally absorb light of different wavelengths differently. Because materials in the body absorb light of specific wavelengths, these materials may differentially heat up and produce acoustic emissions when sufficiently short pulses of light have sufficient intensity. Depth contrast can be obtained using light of different wavelengths and / or different intensities for each selected wavelength.That is, continuous images can be obtained using varying light intensities and wavelengths on a fixed RGD (which can correspond to a fixed depth within the target object) to detect material and its location within the target object. For example, hemoglobin, blood glucose, and / or blood oxygen within blood vessels of a target object (such as a finger) can be detected photoacously.

[0073] According to some embodiments, the light source system 204 can be configured to emit light pulses having a pulse width of less than about 100 nanoseconds. In some embodiments, the light pulses can have a pulse width between about 10 nanoseconds and about 500 nanoseconds or more. According to some examples, the light source system can be configured to emit multiple light pulses at a pulse repetition frequency between 10 Hz and 100 kHz. Alternatively or further, in some embodiments, the light source system 204 can be configured to emit multiple light pulses at a pulse repetition frequency between about 1 MHz and about 100 MHz. Alternatively or further, in some embodiments, the light source system 204 can be configured to emit multiple light pulses at a pulse repetition frequency between about 10 Hz and about 1 MHz. In some examples, the pulse repetition frequency of the light pulses can correspond to the acoustic resonant frequency of the ultrasonic receiver and the substrate. For example, a set of four or more light pulses can be emitted from the light source system 204 at a frequency corresponding to the resonant frequency of the resonant acoustic cavity in the sensor stack, thereby allowing the received ultrasound to be built up and a higher resulting signal strength. In some embodiments, the light source system 204 may include filtered light or a light source having a specific wavelength for detecting the selected material. In some embodiments, the light source system may include light sources such as red, green, and blue LEDs of a display, which can be enhanced with light sources of other wavelengths (such as IR and / or UV) and with light sources of higher optical power. For example, high-power laser diodes or electronic flash units (e.g., LEDs or xenon flash units) with or without filters may be used for short-term irradiation of the target object.

[0074] Control system 206 may include one or more general-purpose single-chip or multi-chip processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or combinations thereof. Control system 206 may also include one or more memory devices (and / or be configured to communicate with one or more memory devices), such as one or more random access memory (RAM) devices, read-only memory (ROM) devices, etc. Accordingly, apparatus 200 may have a memory system including one or more memory devices, although the memory system in Figure 2Not shown in the diagram. Control system 206 can be configured to receive and process data from ultrasonic receiver 202, for example, as described below. If device 200 includes an ultrasonic transmitter, control system 206 can be configured to control that ultrasonic transmitter. In some embodiments, the functionality of control system 206 can be partitioned among one or more controllers or processors, such as between a dedicated sensor controller and an application processor in a mobile device.

[0075] Some embodiments of device 200 may include interface system 208. In some examples, interface system 208 may include a wireless interface system. In some embodiments, interface system 208 may include a user interface system, one or more network interfaces, one or more interfaces between control system 206 and memory system, and / or one or more interfaces between control system 206 and one or more external device interfaces (e.g., ports or application processors).

[0076] According to some examples, device 200 may include a display system 210 that includes one or more displays. For example, display system 210 may include one or more LED displays, such as one or more organic LED (OLED) displays.

[0077] Device 200 can be used in a variety of different contexts, many of which are disclosed herein. For example, in some embodiments, a mobile device may include device 200. In some embodiments, a wearable device may include device 200. For example, a wearable device may be a bracelet, armband, wristband, ring, headband, earplug, or patch.

[0078] Figure 3 It is a flowchart that provides some examples of the publicly disclosed operations. Figure 3 The blocks (and other flowchart blocks provided herein) can be, for example, made by Figure 2 The apparatus 200 or similar apparatus performs the operation. As with other methods disclosed herein, Figure 3 The methods outlined herein may include more or fewer blocks than indicated. Furthermore, the blocks in the methods disclosed herein are not necessarily executed in the indicated order. In some instances, Figure 3 One or more of the blocks shown can be executed concurrently.

[0079] Here, block 305 relates to controlling the light source system to emit light. In some such embodiments, the control system 206 of device 200 can control the light source system 204 to emit light. According to this embodiment, block 305 relates to controlling the light source system to emit multiple light pulses into biological tissue, including blood and blood vessels at various depths within the biological tissue. In some such examples, block 305 relates to controlling the light source system to emit multiple light pulses at a pulse repetition frequency. In some examples, the pulse repetition frequency may be in the range of 10 Hz to 1 MHz or in a range including 10 Hz and 1 MHz.

[0080] In some implementations, the control system may be configured to select one or more optical wavelengths of the plurality of optical pulses, for example, as described above. According to some examples, the control system may be configured to select the light intensity associated with one or more selected wavelengths. For example, the control system may be configured to select one or more optical wavelengths and the light intensity associated with each selected wavelength to generate acoustic emission from one or more portions of a target object. In some examples, the control system may be configured to select the one or more optical wavelengths to evaluate one or more characteristics of the target object, for example, to evaluate blood oxygen concentration. In some examples, block 305 may relate to controlling the light source system to emit light that penetrates the substrate and / or other layers of a device (such as device 200).

[0081] According to this embodiment, block 310 relates to receiving a signal corresponding to a sound wave from an ultrasonic receiver, the sound wave being emitted from various parts of a biological tissue in response to irradiation by light emitted by a light source system. In this embodiment, the sound wave corresponds to photoacoustic emission from blood and / or blood vessels of the biological tissue caused by multiple light pulses. In this example, the ultrasonic receiver is or includes a piezoelectric receiver. In some instances, a target object including biological tissue (such as a finger, wrist, or another body part) may be placed on the surface of the ultrasonic receiver or on the surface of a device including the ultrasonic receiver. In some embodiments, the ultrasonic receiver may be... Figure 2 The ultrasonic receiver 202 is shown in the figure and described above. In some examples, one or more coatings or acoustic matching layers (e.g., acoustic impedance for matching human skin) may reside on the surface of the ultrasonic receiver or on the surface of the device including the ultrasonic receiver (e.g., the surface of the cover glass or pressure plate of the device).

[0082] In this example, block 315 relates to detecting a heart rate waveform in a signal received from an ultrasound receiver. According to this embodiment, block 320 relates to determining a first subset of the detected heart rate waveforms corresponding to a venous heart rate waveform. In this example, block 325 relates to determining a second subset of the detected heart rate waveforms corresponding to an arterial heart rate waveform. Detailed examples of blocks 315, 320, and 325 are disclosed herein.

[0083] According to some examples, the control system can be configured to distinguish between venous heart rate waveforms and arterial heart rate waveforms by acquiring a depth-discrimination signal. Figure 4A An example of a range-gate window (RGW) selected for receiving sound waves emitted from different depth ranges is shown. The time delay or range-gate delay is obtained (its...). Figure 4B The value marked "RGD" is the starting time t of the photoexcitation signal 405 shown in graph 400. l The measurement begins. RGD can be selected, for example, to correspond to the time required for the photoacoustic emission to reach the receiver from the shallowest point of interest, as shown in the following reference. Figure 5A and Figure 5B As described. Accordingly, the RGD can depend on the specific arrangement of the apparatus used to receive the photoacoustic emission, including the thickness of the layers(s) between the target and the receiver and the sound velocity of the layers(s) between the target and the receiver. Figure 401 depicts the time following the RGD, during which the emitted sound waves can be received and sampled by the ultrasonic receiver during the acquisition time window of the RGW (also referred to as the range gate window or range gate width). In some embodiments, the RGW can be 10 microseconds. Other embodiments can have larger or smaller RGWs.

[0084] In some examples, the depth differentiation signal can be obtained by dividing the acoustic waves received during the RGW into multiple smaller time windows. Each time window can correspond to a depth range within the target object from which acoustic waves are received. In some examples, the depth range or thickness of each layer can be 0.5 mm. Assuming a sound velocity of 1.5 mm / microsecond, each 0.5 mm layer would correspond to a time slot of approximately 0.33 microseconds. However, the depth range can vary depending on the specific implementation.

[0085] According to some alternative examples, receiving a signal from a piezoelectric receiver involves obtaining a depth-discriminating signal by applying first to Nth acquisition time delays and receiving first to Nth signals during the first to Nth acquisition time windows, each of the first to Nth acquisition time windows occurring after a corresponding acquisition time delay in the first to Nth acquisition time delays, where N is an integer greater than one. The control system can be configured to determine a first subset and a second subset of detected heart rate waveforms, at least in part, based on the depth-discriminating signal.

[0086] Figure 4B Examples are shown where multiple acquisition time delays are selected to receive sound waves emitted from different depths. In these examples, each of the acquisition time delays (its in...) Figure 4B The distance gate delay (RGD) is the time t of the optical excitation signal 405 shown in graph 400. l The measurement begins. Graph 410 depicts the emitted sound waves that can be received by the ultrasonic sensor array at the acquisition time delay RGD1 and sampled during the acquisition time window RGW1 (also known as the distance gate window or distance gate width) (received wave (1) is an example). Such sound waves are generally emitted from a relatively shallow portion of the target object that is close to or located on the pressure plate of the bioassay system.

[0087] Figure 415 depicts the emitted sound waves (received wave (2) is an example) that are received by the ultrasonic sensor array at the acquisition time delay RGD2 (where RGD2 > RGD1) and sampled during the acquisition time window RGW2. Such sound waves are generally emitted from relatively deep parts of the target object.

[0088] Curve 420 depicts the acquisition time delay RGD n (where RGD) n Received at (>RGD2>RGD1) and acquired within the time window RGW n The emitted sound waves are sampled during this period (the received wave (n) is an example). Such sound waves are typically emitted from deeper parts of the target object. The distance gate delay is typically an integer multiple of the clock period. A clock frequency of 128 MHz has, for example, a clock period of 7.8125 nanoseconds, and the range of RGD can be from less than 10 nanoseconds to more than 2000 nanoseconds. Similarly, the distance gate width can also be an integer multiple of the clock period, but is typically much shorter than RGD (e.g., less than about 50 nanoseconds) to obtain the returned signal while maintaining good axial resolution. In some implementations, the acquisition time window (e.g., RGW) can be between 175 nanoseconds and 320 nanoseconds or more. In some examples, RGW can be more or less nanoseconds, for example, in the range of 25 nanoseconds to 1000 nanoseconds.

[0089] Figure 5A and Figure 5B An example of a device configured to receive sound waves emitted from different depths is shown. Figure 5A and Figure 5B The device shown is Figure 2 An example of the device 200 shown. As with other embodiments shown and described herein, Figure 5A and Figure 5B The component types, component arrangements, and component sizes shown are for illustrative purposes only.

[0090] According to this example, device 200 includes an ultrasonic receiver 202, a light source system 204 (which includes an LED in this example), and a control system (which in... Figure 5A and Figure 5B (Not shown in the image). According to this embodiment, the device 200 includes a beam splitter 501, with an LED mounted on one side 502 of the beam splitter 501. In this example, a finger 506 is placed on the adjacent side 504 of the beam splitter 501.

[0091] Figure 5A The image shows light emitted from the light source system 204, a portion of which is reflected by beam splitter 501 and enters finger 506. The distance gate delay for this and other embodiments can, for example, be selected to correspond to the time required for the photoacoustic emission to reach the receiver from the shallowest target of interest. For example, in the case of finger 506 and ultrasonic receiver 202 (… Figure 5A In one configuration of device 200 using a 12.7mm beamsplitter between the RX and RX, the signal from the finger surface will arrive in the time it takes for the sound wave to travel through the entire beamsplitter. Using the speed of sound of borosilicate glass, 5500 m / s, as the approximate speed of sound for the beamsplitter, and with a beamsplitter size of 12.7mm, this time becomes 12.7mm / 5500m / s or 2.3µs. Therefore, a distance-gate delay of 2.3µs corresponds to the surface of the finger 506. To travel 1mm into the finger 506, for example, using the current speed of sound of tissue, 1.5mm / µs, this time becomes 1mm / 1.5mm / µs or approximately 0.67µs. Therefore, a distance-gate delay of approximately 2.97µs (2.3µs + 0.67µs) will cause the ultrasonic receiver 202 to begin sampling the sound wave reflected from a depth of approximately 1mm below the outer surface of the finger 506.

[0092] Figure 5B The acoustic signals corresponding to the photoacoustic emissions from tissues (e.g., blood and blood vessels) within the finger 506, caused by light entering the finger 506, are shown. Figure 5BIn the example shown, the acoustic signals originate from different depths (depths 508a, 508b, and 508c) within the finger 506. Correspondingly, the travel times t1, t2, and t3 from depths 508a, 508b, and 508c to the ultrasonic receiver 202 are also different: in this example, t3 > t2 > t1. Therefore, multiple acquisition time delays can be selected to receive sound waves emitted from depths 508a, 508b, and 508c, for example, as... Figure 4B As shown in the diagram and described above.

[0093] Figure 6 It shows that it can execute Figure 3 An example of a cross-sectional view of the apparatus for the method. Figure 6 The device 200 shown is the one referenced above. Figure 2 Another example of the described device 200. As with other embodiments shown and described herein, Figure 6 The component types, component arrangements, and component sizes shown are for illustrative purposes only.

[0094] Figure 6 An example is shown where a target object (in this example, finger 506) is illuminated by incident light and subsequently emits sound waves. In this example, device 200 includes a light source system 204, which may include an array of light-emitting diodes and / or an array of laser diodes. In some embodiments, light source system 204 may be able to emit light of various wavelengths, selectable to trigger sound wave emission primarily from a particular type of material. In some instances, the incident light wavelength, multiple wavelengths, and / or (multiple) wavelength ranges may be selected to trigger sound wave emission primarily from a particular type of material, such as blood, blood vessels, other soft tissue, or bone. To achieve sufficient image contrast, the light source 604 of light source system 204 may need to have a higher intensity and optical power output than light sources generally used for illuminating displays. In some embodiments, a light source having a light output of 1–100 millijoules per pulse or greater (e.g., 10 millijoules per pulse) and a pulse width in the range of 100 nanoseconds to 600 nanoseconds may be suitable. In some embodiments, the pulse width of the emitted light may be between 10 nanoseconds and 700 nanoseconds.

[0095] In this example, incident light 611 has been transmitted from the light source 604 of the light system 204 through the sensor stack 605 and into the overlying finger 506. The individual layers of the sensor stack 605 may comprise one or more substrates of glass or other materials, such as plastic or sapphire that is substantially transparent to the light emitted by the light source system 204. In this example, the sensor stack 605 includes a substrate 610 coupled to the light source system 204, which, according to some embodiments, may be a backlight for a display. In alternative embodiments, the light source system 204 may be coupled to a front light. Accordingly, in some embodiments, the light source system 204 may be configured to illuminate both the display and a target object.

[0096] In this embodiment, substrate 610 is coupled to thin-film transistor (TFT) substrate 615 for ultrasonic receiver 202, which in this example includes an array of sensor pixels 602. According to this example, piezoelectric receiver layer 620 covers the sensor pixels 602 of ultrasonic receiver 202, and pressure plate 625 covers piezoelectric receiver layer 620. Accordingly, in this example, device 200 is able to transmit incident light 611 through one or more substrates of sensor stack 605, which includes ultrasonic receiver 202 having substrate 615 and pressure plate 625, which can also be considered a substrate. In some embodiments, the sensor pixels 602 of ultrasonic receiver 202 may be transparent, partially transparent, or substantially transparent, so that device 200 is able to transmit incident light 611 through the elements of ultrasonic receiver 202. In some embodiments, ultrasonic receiver 202 and associated circuitry may be formed on or within a glass, plastic, or silicon substrate.

[0097] According to some embodiments, device 200 may include an ultrasonic transmitter 627, such as Figure 6 The ultrasonic transmitter 627 is shown. The ultrasonic transmitter may or may not be part of the ultrasonic receiver 202, depending on the specific implementation. In some examples, the ultrasonic receiver 202 may include PMUT or CMUT elements capable of transmitting and receiving ultrasonic waves, and the piezoelectric receiver layer 620 may be replaced by an acoustic coupling layer. In some examples, the ultrasonic receiver 202 may include an array of pixel input electrodes and sensor pixels partially formed by a TFT circuit system, a piezoelectric receiver layer 620 overlaid with a piezoelectric material (such as PVDF or PVDF-TrFE), and an upper electrode layer (sometimes referred to as a receiver bias electrode) located on the piezoelectric receiver layer. Figure 6In the example shown, at least a portion of the device 200 includes an ultrasonic transmitter 627 that can be used as a plane wave ultrasonic transmitter. The ultrasonic transmitter 627 may, for example, include a piezoelectric transmitter layer, wherein transmitter excitation electrodes are disposed on each side of the piezoelectric transmitter layer.

[0098] Here, incident light 611 induces optical excitation within finger 506 and generates a resulting acoustic wave. In this example, the generated acoustic wave 613 includes ultrasound. The acoustic emission generated by absorbing the incident light can be detected by ultrasound receiver 202. A high signal-to-noise ratio can be obtained because the resulting ultrasound is caused by optical excitation rather than by reflection of the emitted ultrasound.

[0099] In this example, device 200 includes a control system, although Figure 6 The control system is not shown. According to some examples, the control system can be configured to distinguish venous heart rate waveforms and arterial heart rate waveforms by acquiring a depth-discriminating signal. According to some such examples, receiving a signal from a piezoelectric receiver involves acquiring a depth-discriminating signal by selecting an acquisition time window to receive sound waves emitted from different depth ranges within a target object (such as a finger, wrist, ear, etc.). In some examples, the depth-discriminating signal can be acquired by dividing the sound waves received during the RGW into multiple smaller time windows, for example, as described above. Each time window may correspond to a depth range within the target object from which sound waves are received. According to some alternative examples, receiving a signal from a piezoelectric receiver involves acquiring a depth-discriminating signal by applying a first to Nth acquisition time delay and receiving a first to Nth signal during the first to Nth acquisition time windows, each of the first to Nth acquisition time windows occurring after a corresponding acquisition time delay in the first to Nth acquisition time delay, where N is an integer greater than one. The control system can be configured to determine venous heart rate waveforms and arterial heart rate waveforms at least in part based on the depth-discriminating signal. Figure 3 The "first subset of detected heart rate waveforms" and the "second subset of detected heart rate waveforms".

[0100] Figure 7 Examples of venous heart rate waveforms and arterial heart rate waveforms are shown. Figure 7 In the figure, graph 700 shows the effect of using PAPG technology (as referenced above). Figures 3 to 6 Examples of arterial heart rate waveforms obtained using the PAPG technique described herein. In this example, depth-differential arterial heart rate waveforms have been obtained from depths between 4.0 mm and 4.5 mm within the finger.

[0101] exist Figure 7In Figure 710, an example of a venous heart rate waveform obtained using PAPG techniques such as those disclosed herein is shown. According to this example, depth-differentiated venous heart rate waveforms have been obtained from depths between 6.5 mm and 7.0 mm within the same finger. In the examples shown in Figures 700 and 710, the depth-differentiated PAPG waveforms do not cross-contaminate with waveforms from other depths (which would be the case if waveforms were obtained using PPG techniques).

[0102] Figure 705 shows an example of an arterial heart rate waveform obtained via catheter. Figure 715 shows an example of a venous heart rate waveform obtained via catheter. (See reference...) Figure 1C The heart rate waveforms obtained via catheters discussed are known to be highly reliable and are considered by the inventors to be the "true value," to which other techniques for obtaining heart rate waveforms can be compared. It can be seen that the "true value" arterial heart rate waveform in graph 705 is very similar to the arterial heart rate waveform obtained using the PAPG technique in graph 700. Similarly, it can be seen that the "true value" venous heart rate waveform in graph 715 is very similar to the venous heart rate waveform obtained using the PAPG technique in graph 710. This strongly demonstrates the feasibility of the disclosed PAPG method.

[0103] Referring to curves 700 and 705, it can be observed that the arterial heart rate waveform repeats a "step-down" pattern, as indicated by arrow 720. Referring to curves 710 and 715, it can be observed that the venous heart rate waveform repeats a "step-up" pattern, as indicated by arrow 725.

[0104] Figure 8 Examples of determining venous heart rate waveforms and arterial heart rate waveforms are shown. Figure 8 Signals 805a, 805b, and 805n are depicted, corresponding to sound waves generated by photoacoustic emissions from biological tissues, including blood and blood vessels. According to this example, there exists... Figure 8 Additional signals 805c, 805d, etc., not shown in the diagram.

[0105] The photoacoustic emission corresponding to signals 805a, 805b, and 805n is caused by multiple corresponding optical pulses 802a, 802b, and 802n. In this example, there exists Figure 8Additional optical pulses 802c, 802d, etc., not shown. In this example, optical pulses 802a, 802b, and 802n are time-intervaled by 10,000 microseconds or 0.01 seconds, corresponding to a pulse repetition frequency of 100 Hz. Other examples may involve different pulse repetition frequencies in the range between 10 Hz and 100 kHz (e.g., in the range between 50 Hz and 1000 Hz). According to some examples, optical pulses 802a, 802b, and 802n may have durations in the range of 2 nanoseconds to 5 microseconds. In some examples where the light source system 204 includes one or more laser diodes, optical pulses 802a, 802b, and 802n may have durations in the range of 50 nanoseconds to 500 nanoseconds. In some examples where the light source system 204 includes one or more lasers, optical pulses 802a, 802b, and 802n may have durations in the range of 5 nanoseconds to 100 nanoseconds.

[0106] In this example, signals 805a, 805b, and 805n are all received within 10 microseconds of the time it takes to transmit the corresponding light pulses 802a, 802b, and 802n (as indicated by dashed lines 806a, 806b, and 806n). However, the timescale used to represent signals 805a, 805b, and 805n differs from the timescale used to represent the 10,000 microsecond time interval between light pulses 802a, 802b, and 802n. In this example, signals 805a, 805b, and 805n are all received within a time interval corresponding to a single heart rate waveform.

[0107] Rectangles 807a and 809a represent samples of acoustic waves received after pulse 802a, during an RGW of the same duration, but after an RGD of different durations. The RGD corresponding to rectangle 807a is smaller than the RGD corresponding to rectangle 809a. The RGD corresponding to rectangle 807a is selected to receive acoustic waves generated by photoacoustic emissions from biological tissue at a depth of approximately 2.0 mm to 2.5 mm. The RGD corresponding to rectangle 809a is selected to receive acoustic waves generated by photoacoustic emissions from biological tissue at a depth of approximately 3.0 mm to 3.5 mm.

[0108] The height of each of rectangles 807a and 809a represents the absolute value of the difference between the maximum and minimum signal amplitudes received during the corresponding time interval (which may be referred to herein as the "peak-to-peak" or "peak-to-peak signal value"). Rectangles 807b and 807n represent samples of sound waves received after the times of pulses 802b and 802n, during the same RGW period as rectangle 807a, and after an RGD of the same duration as the RGD of rectangle 807a. Rectangles 809b and 809n represent samples of sound waves received after the times of pulses 802b and 802n, during the same RGW period as rectangle 809a, and after an RGD of the same duration as the RGD of rectangle 809a. The heights of rectangles 807b, 807n, and 809b and 809n represent the peak-to-peak values ​​received during the corresponding time interval.

[0109] Rectangle 810a corresponds to the peak-to-peak value of rectangle 807a. Similarly, rectangles 810b and 810n correspond to the peak-to-peak values ​​of rectangles 807b and 807n. It can be observed that the heights of rectangles 810a, 810b, and 810n decrease over time. This corresponds to... Figure 7 The "step-down" effect is shown in the arterial waveform annotations of graphs 700 and 705. Therefore, it can be concluded that rectangles 807a, 807b, and 807n correspond to samples of waveforms received from the artery.

[0110] Rectangle 812a corresponds to the peak-to-peak value of rectangle 809a. Similarly, rectangles 812b and 812n correspond to the peak-to-peak values ​​of rectangles 809b and 809n. It can be observed that the heights of rectangles 812a, 812b, and 812n increase over time. This corresponds to... Figure 7 The curves 710 and 715 illustrate the "step-like rise" effect of the vein waveform annotations. Therefore, it can be concluded that rectangles 809a, 809b, and 809n correspond to samples of the waveform received from the vein.

[0111] Figure 9 This is a diagram illustrating aspects of the five disclosed PAPG algorithms. As used herein, the term "algorithm" refers to a method or a set of two or more methods. An "algorithm" may or may not correspond to a specific mathematical formula or a specific sequence of mathematical formulas, depending on the specific implementation. Figure 9 The blocks (and the blocks in other diagrams presented herein) can be, for example, made by... Figure 2 The apparatus 200 or similar apparatus performs the operation. As with other methods disclosed herein, Figure 9 The methods outlined herein may include more or fewer blocks than indicated. Furthermore, the blocks in the methods disclosed herein are not necessarily executed in the indicated order. In some instances, Figure 9One or more of the blocks shown can be executed concurrently.

[0112] In this example, block 905 involves acquiring PAPG data and detecting heart rate (HR) waveforms. According to some examples, block 905 may involve performing the above-mentioned reference... Figure 3 One or more blocks of the described method. In some implementations, block 905 may involve obtaining a depth differentiation signal by performing one or more of the methods disclosed herein.

[0113] In some instances, block 905 may relate to controlling a light source system to emit multiple light pulses at a pulse repetition frequency into biological tissue, including blood and blood vessels at various depths within the biological tissue. Block 905 may relate to receiving signals from a piezoelectric receiver corresponding to sound waves emitted from various parts of the biological tissue. The sound waves may correspond to photoacoustic emissions from the blood and blood vessels caused by the multiple light pulses. Block 905 may relate to detecting a heart rate waveform in the signal.

[0114] In some examples, block 905 may involve obtaining a depth-discriminating signal by applying first to Nth acquisition time delays and receiving first to Nth signals during the first to Nth acquisition time windows, each of the first to Nth acquisition time windows occurring after a corresponding acquisition time delay in the first to Nth acquisition time delays, where N is an integer greater than one. Block 905 may involve determining, at least in part, a first subset of detected heart rate waveforms corresponding to venous heart rate waveforms and a second subset of detected heart rate waveforms corresponding to arterial heart rate waveforms based on the depth-discriminating signal.

[0115] In some implementations, block 905 may involve acquiring PAPG data and detecting heart rate waveforms at different heights relative to the user's heart. For example, block 905 may involve: acquiring a first set of PAPG data from the user's finger or wrist when the user's finger or wrist is at approximately the same height as the user's heart; acquiring a second set of PAPG data from the user's finger or wrist when the user's finger or wrist is at a height above the user's heart (e.g., when the user is standing or sitting upright with their arm extended above the user's head); and acquiring a third set of PAPG data from the user's finger or wrist when the user's finger or wrist is at a height below the user's heart (e.g., when the user is standing or sitting upright with their arm extended downwards).

[0116] Based on some examples, block 905 can refer to the above reference. Figure 8 As described or as referenced below Figure 10A and Figure 10B or Figure 11At least some of the described procedures. In this example, the result of block 905 is stored in the database at block 910. According to... Figure 9 As shown in the example, at least some results of block 905 can be used in the processes of blocks 915 and 920, and in some instances can be used in the process of block 925.

[0117] exist Figure 9 In the example shown, block 915 relates to extracting heart rate waveform features from a heart rate waveform and estimating blood pressure based at least in part on the extracted heart rate waveform features. Block 915 may relate to heart rate waveform segmentation and the detection of systolic and diastolic portions of the heart rate waveform. Block 915 may, for example, relate to peak and trough detection, which may be referred to herein as “benchmark point” detection. As used herein, the term “heart rate waveform feature” includes such detected benchmark points. According to some examples, block 915 may also relate to detecting various types of heart rate waveform features corresponding to the width of various portions of the heart rate waveform, and in some instances, metrics based on various combinations of such width values. See below for reference. Figure 11 Some detailed examples are described.

[0118] According to this example, block 920 relates to making at least one blood pressure estimate based on hemodynamic analysis. In some examples, block 920 may involve extracting a set of hemodynamic features from a second subset of a heart rate waveform corresponding to an arterial heart rate waveform and making a first blood pressure estimate based at least in part on this set of hemodynamic features. According to some implementations, block 920 may involve determining arterial-venous phase shift (AVPS) data from a first subset and a second subset of the heart rate waveform and making a first blood pressure estimate based at least in part on the AVPS data. Alternatively or additionally, in some examples, the blood pressure estimate may be based solely on the AVPS data. According to some examples, block 920 may involve making a first blood pressure estimate based at least in part on determining the area under one or more portions of a curve defined by the arterial heart rate waveform. Some detailed examples are described below.

[0119] According to some examples, block 925 relates to blood pressure estimation based on a combination of the methods in blocks 915 and 920. Some such examples may involve a first blood pressure estimation (block 920) based at least in part on a set of hemodynamic features, a second blood pressure estimation (block 915) based at least in part on extracted heart rate waveform features, and a third blood pressure estimation based at least in part on the first and second blood pressure estimates. However, in some examples, block 925 may involve a first blood pressure estimation (block 920) based at least in part on AVPS data, a second blood pressure estimation (block 915) based at least in part on extracted heart rate waveform features, and a third blood pressure estimation based at least in part on the first and second blood pressure estimates.

[0120] According to some examples, a third blood pressure estimate can be the average of a first blood pressure estimate and a second blood pressure estimate. In some such examples, the third blood pressure estimate can be a weighted average of the first and second blood pressure estimates. This average can be weighted, for example, based on the reliability perceived by the underlying methods of the first and second blood pressure estimates.

[0121] Figure 10A and Figure 10B An example of a block showing the data acquisition and heart rate waveform (HRW) determination process is shown. Figure 10A and Figure 10B At least some of the blocks can be, for example, made by Figure 2 The apparatus 200 or similar apparatus performs the operation. As with other methods disclosed herein, Figure 10A and Figure 10B The methods outlined in the document can include more or fewer blocks than indicated. Figure 10A and Figure 10B Based on the prototype process implemented by the inventor, but Figure 10A and Figure 10B At least some of the blocks can be used in commercial implementations. Furthermore, the blocks of the method disclosed herein are not necessarily executed in the indicated order. In some instances, Figure 10A and Figure 10B One or more of the blocks shown can be executed concurrently. In some examples, the "input hardware" referenced in block 1001 may include... Figure 5A and Figure 5B Version 200 of the device shown.

[0122] In this embodiment, block 1005 relates to obtaining PAPG data from different heights relative to the user's heart. According to this example, block 1005 involves obtaining a first set of PAPG data from the user's finger when the finger is at approximately the same height as the user's heart, a second set of PAPG data from the user's finger when the finger is above the user's heart (e.g., when the user is standing or sitting upright with their arm extended above their head), and a third set of PAPG data from the user's finger when the finger is below the user's heart (e.g., when the user is standing or sitting upright with their arm extended downwards). The PAPG data from the three different heights relative to the user's heart corresponds to the data structures labeled "Calibration Distance #1," "Calibration Distance #2," and "Calibration Distance #3" shown in block 1005.

[0123] In this example, the live or “real-time” signal-to-noise ratio (SNR) and / or finger position verification process 1010 is executed concurrently with the data acquisition process 1005. According to this implementation, the output of the fast data formatting block 1011 is provided to the determination block 1013, where the SNR and / or finger position can be evaluated. In some such examples, the determination block 1013 involves determining whether a human head-down wind (HRW) has been detected. In this example, if the determination block 1013 indicates a positive result, the process continues to block 1015, while if process 1010 indicates a negative result, a user prompt is provided in the block.

[0124] HRW generation block 1035 may relate to one or more methods for HRW determination and generation. Block 1037 relates to a “peak-to-peak” HRW generation process, as described herein, which may be performed as described elsewhere herein. In this example, block 1039 relates to HRW generation based on the Hilbert transform of detected sound waves corresponding to photoacoustic emissions from blood and blood vessels. The Hilbert transform returns a complex helical sequence, sometimes referred to as an analytic signal, from a real data sequence. This signal contains a real part and an imaginary part. The imaginary part is a version of the original real sequence with a 90° phase shift. Sines are thus transformed into cosines, and vice versa. The sequence after the Hilbert transform has the same amplitude and frequency content as the original sequence. The transform includes phase information that depends on the phase of the original sequence. The Hilbert transform is useful in calculating the instantaneous properties of time series, particularly amplitude and frequency. Block 1039 and other methods (which may include those of block 1041) involve evaluating the total energy returned in the signal corresponding to the detected sound wave. Some methods may involve evaluating peak energy, while others may involve integrating or summing the area under the curve represented by the detected sound wave. Some of these methods may involve absolute value trapezoidal detection techniques, which are a method of approximating the integration or summation of the area under the curve. In some examples, the absolute value trapezoidal detection technique begins with a sine wave in which the y-axis is centered at 0. The absolute value of the signal is determined such that any negative component / period is positive or greater than 0. After determining the absolute value, in some examples, numerical integration is applied via a trapezoidal method. This method approximates the integration over an interval by decomposing the area into trapezoids with areas that are easier to compute. The absolute value trapezoidal detection technique is applied within a specified interval / window (corresponding to a depth range within the target (e.g., a finger)). In some implementations, instead of applying the absolute value trapezoidal detection technique, an absolute mean detection method may be applied. Based on some of these examples, absolute mean detection methods involve determining the mean of the absolute values ​​of a signal within a region / window / depth range of interest.

[0125] exist Figure 10A and Figure 10BIn the example shown, HRW generation block 1035 involves evaluating and outputting HRW data related to depth-discriminative data and depth-integrated data. Depth-discriminative data can be obtained by sampling acoustic data via multiple RGDs to acquire data from multiple corresponding depths, for example, as discussed elsewhere herein. Depth-integrated data can be obtained by receiving acoustic data corresponding to multiple depths within the finger during RGW, generating an undifferentiated output including responses from multiple blood vessels, capillaries, etc. Accordingly, the depth-integrated data is similar to the data obtained via a PPG process. In this embodiment, the HRW is calculated based on both the depth-discriminative data and the depth-integrated data, and the HRW is output from HRW generation block 1035.

[0126] According to this example, HRW generation block 1035 includes metadata containing HRW data that is output and saved. In this example, the metadata includes data corresponding to the person from whom PAPG data has been obtained. This metadata may include age data, weight data, height data, body mass index data, gender data, data on medications being taken, and / or data on known health problems (particularly those involving the heart and / or circulatory system), etc. The metadata may or may not be used for blood pressure calculation purposes, depending on the specific implementation.

[0127] In this example, depth-discriminative HRW data is input into the automated arterial / venous HRW detection block 1045. In some examples, the automated arterial / venous HRW detection block 1045 may refer to the above reference. Figure 7 and Figure 8 Some or all of the processes described. In this embodiment, deep distinguishing HRW data is also input into a manual arterial / venous HRW detection block 1043. In this example, the manual arterial / venous HRW detection block 1043 involves a manual process for detecting arterial and venous HRW. The manual arterial / venous HRW detection block 1043 has been used during the research and development phase of the inventors' work to determine the "true" arterial and venous HRW. The manual arterial / venous HRW detection block 1043 is not intended to be a necessary component of a commercial product.

[0128] According to this embodiment, artery and vein diameter data are determined and output by HRW generation block 1035. Artery or vein portions that do not change their optical absorption during the cardiac cycle will not be displayed in the HRW, while those portions that do change their optical absorption during this time will be displayed. For example, the inner portion of an artery or vein may not change its optical absorption during the cardiac cycle. However, the outer portion, particularly the area just outside the vessel, abruptly surrounds the outer portion of the vessel as it stretches during the HRW. This process alters the optical absorption. Some embodiments have sufficient resolution and use a sufficiently narrow time window to distinguish vessels of various diameters. For example, an embodiment including a receiver with a resolution of 0.25 mm and a time window set to correspond to a tissue depth range of 0.25 mm can distinguish between arteries with a diameter of 0.5 mm and arteries with a diameter of 1.0 mm. Some embodiments utilize the same data to determine the extent of vessel stretching during the cardiac cycle. Accordingly, in this example, artery and vein stretching data are determined and output by HRW generation block 1035.

[0129] After determining the arterial and venous HRW, in this example, the arterial-venous phase shift (AVPS) is calculated in block 1047. According to some implementations, blood pressure estimation can then be performed, at least in part, based on the AVPS data.

[0130] In block 1050, AVPS data, metadata, depth-discriminative HRW, depth-integrated HRW, arterial extension data, vein extension data, arterial diameter data, and vein diameter data are stored. Block 1055 indicates the server location where such data can be stored, and provides examples of data locations 1 and 2 where data can be stored.

[0131] Figure 11 Examples of blocks representing some of the disclosed methods are shown. Figure 11 The blocks (and other flowchart blocks provided herein) can be, for example, made by Figure 2 The apparatus 200 or similar apparatus performs the operation. As with other methods disclosed herein, Figure 11 The methods outlined herein may include more or fewer blocks than indicated. Furthermore, the blocks in the methods disclosed herein are not necessarily executed in the indicated order. In some instances, Figure 11 One or more of the blocks shown can be executed concurrently.

[0132] In this example, block 1105 relates to filtering the input HRW data. In some examples, the input HRW data may be deeply integrated HRW data, while in other instances, the input HRW data may be deeply discriminative HRW data. In this example, the original signal is noisy and includes a breathing effect. According to this example, block 1105 relates to applying a bandpass filter with a passband of 0.1 Hz to 10 Hz to the input HRW data. Other examples may involve applying bandpass filters with different passbands. In this example, block 1105 relates to applying a DC offset to at least some of the input HRW data to eliminate the breathing effect.

[0133] In this embodiment, the filtered HRW data output from block 1105 is input to HRW averaging block 1110. HRW averaging can be beneficial due to the variability of HRW between heartbeats, at least in part because averaging helps eliminate random noise. According to some embodiments, filtering HRW data over tens of seconds can be averaged in block 1105 (e.g., 10 seconds of filtered HRW data, 20 seconds of filtered HRW data, 30 seconds of filtered HRW data, 40 seconds of filtered HRW data, 50 seconds of filtered HRW data, 60 seconds of filtered HRW data, etc.).

[0134] According to this example, block 1115 relates to HRW benchmark detection, including HRW peak and trough detection based on averaged HRW data. In this example, block 1115 relates to detecting contraction and diastolic troughs in the averaged HRW data. In this implementation, block 1120 relates to HRW segmentation. According to this example, block 1120 relates to segmenting the averaged HRW data into individual HRWs, at least in part based on the output of the HRW benchmark detection in block 1115.

[0135] In this implementation, block 1125 relates to extracting HRW features from the individual HRW segments output by block 1120. Examples of HRW features that can be extracted in block 1125 are shown in... Figure 12 It is shown in the figure and described below.

[0136] According to this example, block 1130 involves training a neural network based on the extracted features output by block 1125 to prepare for blood pressure estimation. Figure 11(This is shown as "BP estimation A"). In some examples (but not all), metadata (e.g., metadata about the person from whom the raw HRW signal was obtained) may also be input into the neural network. In some such examples, training the neural network may involve training the neural network to minimize a cost function based on the difference between (1) and (2): (1) a "true" blood pressure measurement of the person based on a technique known to be reliable (e.g., for people not suffering from the "white coat effect," a catheter-based technique or a cuff-based technique), and (2) a blood pressure estimate of the same person based on the extracted features output by block 1125. According to some examples, the neural network includes at least two hidden layers of neurons in addition to the input and output layers. In some such examples, the number of neurons in the input layer corresponds to the number of input HRW features.

[0137] In some alternative implementations of method 1100, block 1130 may involve applying another type of artificial intelligence, such as a machine learning process, which may be a supervised learning process, an unsupervised learning process, or a reinforcement learning process. In some such alternative implementations, block 1130 may involve applying a Bayesian machine learning process, a linear regression process, a logistic regression process, and so on.

[0138] In some alternative "runtime" examples of method 1100, the previously trained neural network can provide a blood pressure estimate in block 1130 based at least in part on the extracted features output by block 1125.

[0139] Figure 12 It shows that it can be based on Figure 11 Examples of some implementations of the method for extracting heart rate waveform (HRW) features. Figure 12 The horizontal axis represents time and the vertical axis represents signal amplitude. The cardiac cycle is indicated by the time between adjacent peaks of the HRW. The systolic and diastolic time intervals are indicated below the horizontal axis. During the systolic phase of the cardiac cycle, as the pulse propagates through a specific location along the artery, the arterial wall expands according to the pulse waveform and the elastic properties of the arterial wall. Accompanying this expansion is a corresponding increase in blood volume at that specific location or region, and with the increase in blood volume, one or more properties in that region change accordingly. Conversely, during the diastolic phase of the cardiac cycle, blood pressure in the artery decreases and the arterial wall constricts. Accompanying this constriction is a corresponding decrease in blood volume at that specific location, and with the decrease in blood volume, one or more properties in that region change accordingly.

[0140] Figure 12The HRW features shown relate to the width of the contraction and / or diastolic portions of the HRW curve at various "heights" (indicated by a percentage of maximum amplitude). For example, the SW50 feature is the width of the contraction portion of the HRW curve at a "height" of 50% of maximum amplitude. In some embodiments, the HRW features used for blood pressure estimation may include some or all of the HRW features SW10, SW25, SW33, SW50, SW66, SW75, DW10, DW25, DW33, DW50, DW66, and DW75. In other embodiments, additional HRW features may be used for blood pressure estimation. In some implementations, this additional HRW feature includes the sum and ratio of SW and DW at one or more "heights," such as (DW75+SW75), DW75 / SW75, (DW66+SW66), DW66 / SW66, (DW50+SW50), DW50 / SW50, (DW33+SW33), DW33 / SW33, (DW25+SW25), DW25 / SW25, and / or (DW10+SW10), DW10 / SW10. Other implementations may use additional HRW features for blood pressure estimation. In some implementations, this additional HRW feature includes sums, differences, ratios, and / or other calculations based on more than one "height," such as (DW75+SW75) / (DW50+SW50), (DW50+SW50 / (DW10+SW10), etc.

[0141] Figure 13 This is a flowchart illustrating an example block for blood pressure estimation based on hemodynamic characteristics. Figure 13 The blocks (and other flowchart blocks provided herein) can be, for example, made by Figure 2 The apparatus 200 or similar apparatus performs the operation. As with other methods disclosed herein, Figure 13 The methods outlined herein may include more or fewer blocks than indicated. Furthermore, the blocks in the methods disclosed herein are not necessarily executed in the indicated order. In some instances, Figure 13 One or more of the blocks shown can be executed concurrently.

[0142] According to this example, block 1305 involves obtaining the above reference. Figure 10A and Figure 10B The various types of data described. This data may include arterial HRW data, venous HRW data, overlaid HRW data (e.g., deeply integrated HRW data), arterial diameter data, venous diameter data, arterial depth data, venous depth data, AVPS data, and so on.

[0143] In some examples, the contraction / relaxation deterministic logic block 1330 can implement a machine learning process, which can be a supervised learning process, an unsupervised learning process, or a reinforcement learning process. In some examples, the contraction / relaxation deterministic logic block 1330 can implement a linear regression process. In other examples, the contraction / relaxation deterministic logic block 1330 can implement one or more other types of AI, such as neural networks.

[0144] During "runtime" operation, in some examples, AVPS data may be directly fed to the trained contraction / dilution determination logic block 1330. Other implementations may involve preprocessing the AVPS data before feeding it to the trained contraction / dilution determination logic block 1330. Such preprocessing may, for example, involve averaging, filtering, summing, determining minimum and / or maximum values, etc.

[0145] According to this example, hemodynamic feature extraction block 1310 receives data from block 1305 and determines multiple hemodynamic features. These hemodynamic features may include modified normalized pulse volume (mNPV) data, respiratory data, heart rate data, heart rate variability (HRV) data, vascular stiffness index data, pulse area ratio data, peak time data, AVPS data, etc. In the foregoing, mNPV can be defined as the ratio of the peak-to-peak amplitude of the PAPG pulse to the DC component of the pulse, or as a function of said ratio; vascular stiffness can be measured as the ratio of a person's height to the time delay between the systolic and diastolic peaks of the PAPG pulse; the pulse area ratio can be defined as the ratio of the area below the PAPG pulse between the inflection point and the end of the pulse to the area below the PAPG pulse between the inflection point and the beginning of the pulse; and peak time can be measured as the time from the pulse inflection point to the systolic peak. Although these and other hemodynamic features do not directly measure blood pressure, a relationship has been established between the hemodynamic features and systolic / diastolic blood pressure. A neural network can be trained to establish this relationship.

[0146] During the "runtime" operation, n hemodynamic features extracted by the hemodynamic feature extraction block 1310 can be directly fed into the trained systolic / diastolic determination logic block 1330. In some examples, the goal of the training process is to find the relationship between measured hemodynamic features and blood pressure on the training dataset by minimizing the error between the predicted blood pressure and the true blood pressure. Many methods exist to achieve this. Some examples include linear or nonlinear regression, neural networks, support vector machines (SVM), etc. Figure 13During the training process, represented by block 1315, n hemodynamic features are provided to block 1315 along with reliable “true” blood pressure measurements from block 1320. As shown by curve 1322, in some examples, training the systolic / diastolic determination logic block 1330 may involve a separate linear regression analysis based on each of the n hemodynamic features, or alternatively based on all the hemodynamic features. The polynomials Y1 to Y2 shown below curve 1322 are... n This is presented merely as an example. Some implementations may involve analysis based on one or more other types of functions (such as higher-order polynomial functions, Bessel functions, trigonometric functions, etc.). During the training process represented by block 1315, separate analyses (e.g., linear regression analysis) based on AVPS data and “true” blood pressure measurements from block 1320 can be performed.

[0147] exist Figure 13In the example shown, the trained systolic / diastolic determination logic block 1330 is configured to provide a blood pressure estimate based on n hemodynamic features extracted by the hemodynamic feature extraction block 1310 and another blood pressure estimate based on AVPS data. In this example, the blood pressure estimate is also based on calibration data 1325. In some examples, calibration data 1325 may include blood pressure measurements from a cuff-type or catheter-type blood pressure measurement device. Alternatively or additionally, in some embodiments, calibration data 1325 may include blood pressure estimates based on two or more heights of the body part from which ultrasound data is obtained (e.g., two or more heights of the arm when ultrasound data is obtained from the corresponding finger or wrist). Some such embodiments may also involve obtaining data from a sensor capable of measuring or estimating the height of the device by which ultrasound data is obtained (e.g., an embodiment of device 200 disclosed herein) relative to the subject's heart. In some examples, such height estimation device may be at least partially based on input from an optical sensor / camera. Some such examples may incorporate a trained neural network, or another implementation of artificial intelligence, configured to estimate the height of the device used to obtain ultrasound data relative to the subject's heart. In some examples, the height estimation device may be based at least in part on input from a pressure gauge or barometer. Alternatively or additionally, the height estimation device may be based at least in part on input from one or more accelerometers. In some such implementations, the accelerometers may be attached to the subject's upper arm, with the axis of the accelerometer aligned along the long axis of the humerus. Alternatively or additionally, some such implementations may include accelerometers attached to the subject's wrist or fingers. In some alternative implementations, the subject may be instructed to hold their arm in two or more specific positions, such as a position where the subject's arm is relaxed along the side of the subject's body, and another position where the subject's arm is extended at shoulder height.

[0148] Figure 14 This is a flowchart providing an example of blood pressure estimation based on both HRW analysis and hemodynamic analysis. Figure 14 The blocks (and other flowchart blocks provided herein) can be, for example, made by Figure 2 The apparatus 200 or similar apparatus performs the operation. As with other methods disclosed herein, Figure 14 The methods outlined herein may include more or fewer blocks than indicated. Furthermore, the blocks in the methods disclosed herein are not necessarily executed in the indicated order. In some instances, Figure 14 One or more of the blocks shown can be executed concurrently.

[0149] In this example, block 1405 relates to blood pressure estimation (BP estimation "A") based on HRW analysis. Block 1405 may, for example, relate to the above reference. Figure 11 At least some of the described operations or similar processes.

[0150] In this example, block 1410 relates to making another blood pressure estimate (BP estimate "B") based on hemodynamic analysis. Block 1410 can, for example, relate to the above reference. Figure 13 At least some of the procedures described, or similar processes. In some examples, BP estimation B may be based on multiple hemodynamic features, while in other examples, BP estimation B may be based solely on AVPS data and calibration data.

[0151] According to this example, block 1415 relates to making a third blood pressure estimate (BP estimate "C") based on BP estimate A and BP estimate B. In some such examples, BP estimate C can be the average (e.g., a weighted average) of BP estimate A and BP estimate B. The weighting of the weighted average can depend on the relative accuracy of the blood pressure estimate based on HRW analysis compared to the accuracy of the blood pressure estimate based on hemodynamic analysis. For example, if the accuracy of BP estimate A is considered to be twice that of BP estimate B, then the weighting of BP estimate A can be twice that of BP estimate B. In one such example, if BP estimate A is 120 / 80 and BP estimate B is 126 / 80, then BP estimate C could be ((120+120+126) / 3 = 122) / 80.

[0152] In other examples, block 1415 could involve performing a BP estimate C based on a combination or fusion of the methods used to generate BP estimate A and BP estimate B. There are many possible approaches to fusing different methods. In some examples, BP estimate C could be based on a neural network trained to perform estimations based on both HRW features and hemodynamic features. This type of fusion can be referred to as feature-level fusion. In some alternative examples, fusion can be performed at the result level. In this case, predicted systolic and diastolic blood pressures from different methods can be combined to output the resulting blood pressure, for example, as outlined in the simple example above.

[0153] Figure 15An example of a device that can be used in a system for estimating blood pressure at least in part based on pulse conduction time (PTT) is shown. As with the other figures provided herein, the number, type, and arrangement of elements are presented merely as examples. According to this example, system 1500 includes at least two sensors. In this example, system 1500 includes at least an electrocardiogram sensor 1505 and a device 1510 configured to be mounted on a finger of a person 1501. In this example, device 1510 is or includes means configured to perform at least some of the PAPG methods disclosed herein. For example, device 1510 may be or may include... Figure 2 The device 200 or similar device.

[0154] As indicated in graph 1520, PAT comprises two components: the pre-ejection phase (PEP, the time required to convert the electrical signal into mechanical pumping force and isovolumetric contraction to open the aortic valve) and PTT. The onset time of PAT can be estimated based on the QRS complex (the electrical signal characteristic of ventricular electrical stimulation). As shown in graph 1520, in this example, the onset of PAT can be calculated based on the R-wave peak measured by ECG sensor 1505, and the end of PAT can be detected via analysis of the signal provided by device 1510. In this example, the end of PAT is assumed to be the intersection of the tangent corresponding to the local minimum detected by device 1510 and the tangent of the maximum slope / first derivative of the sensor signal after the time of that minimum.

[0155] There are many known algorithms based on PTT and / or PAT for blood pressure estimation, some of which are summarized in Table 1 of Sharma, M. et al., Cuff-Less and Continuous Blood Pressure Monitoring: a Methodological Review (“Sharma”), Multidisciplinary Digital Publishing Institute (MDPI) Technology, 2017, 5, 21, and described in the corresponding text on pages 5–10, both of which are incorporated herein by reference.

[0156] Some previously disclosed methods involve calculating blood pressure based on PTT and / or PAT measured by a sensor system including a PPG sensor, according to one or more of the equations shown in Sharma's Table 1 or other known equations. As mentioned above, some disclosed PAPG-based implementations are configured to distinguish between arterial HRW and other HRW. Some implementations can provide a more accurate measurement of PTT and / or PAT compared to PTT and / or PAT measured by a PPG sensor. Therefore, even when the blood pressure estimation is based on previously known formulas, the disclosed PAPG-based implementations can provide a more accurate blood pressure estimate.

[0157] Other embodiments of system 1500 may not include electrocardiogram sensor 1505. In some such embodiments, device 1515, configured to be mounted on the wrist of person 1501, may be or may include means configured to perform at least some of the PAPG methods disclosed herein. For example, device 1515 may be or may include Figure 2 Device 200 or similar devices. According to some such examples, device 1515 may include a light source system and two or more ultrasonic receivers. See below for reference. Figure 17A An example is described. In some examples, device 1515 may include an array of ultrasonic receivers.

[0158] In some embodiments of system 1500 that does not include electrocardiogram sensor 1505, device 1510 may include a light source system and two or more ultrasound receivers. (Refer to below) Figure 17B An example is described.

[0159] Figure 16 A cross-sectional side view of a portion of artery 1600 is shown, through which pulse 1602 propagates. Figure 16 The block arrows in the diagram indicate the direction of blood flow and pulse propagation. As shown, the propagating pulse 1602 causes strain in the arterial wall 1604, which manifests as an increase in the diameter (and therefore the cross-sectional area) of the arterial wall (referred to as "distension"). The actual spatial length L of the propagating pulse along the artery (in the direction of blood flow) is typically comparable to the length of a limb, such as the distance from the subject's shoulder to their wrist or fingers, and is generally less than one meter (m). However, the length L of the propagating pulse can vary significantly between subjects, and for a given subject, the length L can vary significantly over time depending on various factors. The spatial length L of the pulse will generally decrease with increasing distance from the heart until the pulse reaches the capillaries.

[0160] As described above, certain embodiments relate to apparatus, systems, and methods for estimating blood pressure or other cardiovascular characteristics based on estimations of arterial extension waveforms. The terms “estimating,” “measuring,” “calculating,” “inferring,” “deducing,” “evaluating,” “determining,” and “monitoring” may be used interchangeably herein where appropriate, unless otherwise indicated. Similarly, terms derived from the roots of these terms may also be used interchangeably where appropriate; for example, the terms “estimate,” “measurement,” “calculation,” “inference,” and “determination” may also be used interchangeably herein. In some embodiments, the pulse wave velocity (PWV) of a propagating pulse can be estimated by measuring the pulse conduction time (PTT) of the pulse as it travels from a first physical location along the artery to a more distant second physical location along that artery. It will be understood that this PTT differs from the referenced above. Figure 15 The described PTT. However, either version of PTT can be used for blood pressure estimation purposes. Assuming the physical distance ΔD between the first and second physical locations is determinable, PWV can be estimated as the quotient of the physical spatial distance ΔD traveled by the pulse divided by the time (PTT) taken for the pulse to traverse that physical spatial distance ΔD. Generally, a first sensor located at the first physical location is used to determine the start time (also referred to herein as the "first time location") at which the pulse arrives or propagates through the first physical location. A second sensor at the second physical location is used to determine the end time (also referred to herein as the "second time location") at which the pulse arrives or propagates through the second physical location and continues propagating through the remainder of the arterial branch. In such examples, PTT represents the time distance (or time difference) between the first and second time locations (start time and end time).

[0161] The fact that arterial stretch waveform measurements are performed at two different physical locations means that the estimated PWV inevitably represents the average over the entire path distance ΔD traversed by the pulse between the first and second physical locations. More specifically, PWV generally depends on several factors, including blood density ρ, arterial wall stiffness E (or conversely elasticity), arterial diameter, arterial wall thickness, and blood pressure. Since both arterial wall elasticity and baseline resting diameter (e.g., the diameter at the end of ventricular diastole) vary significantly throughout the arterial system, the PWV estimate obtained from a PTT measurement is inherently an average (averaged over the entire path length ΔD between the two locations where the measurement was performed).

[0162] In conventional methods for obtaining pulse wave velocity (PWV), the onset time of the pulse is obtained at the heart using an electrocardiogram (ECG) sensor that detects electrical signals from the heart. For example, the onset time can be estimated based on the QRS complex (the electrical signal characteristic of electrical stimulation of the ventricles). In such methods, the end time of the pulse is typically obtained using a different sensor located at a second position (e.g., the finger). As those skilled in the art will appreciate, numerous arterial discontinuities, branches, and variations exist along the entire path from the heart to the finger. The stretches of PWV along the entire path from the heart to the finger can vary by an order of magnitude or more. Thus, PWV estimation based on such a long path length is unreliable.

[0163] In the various embodiments described herein, PTT estimation is obtained based on measurements associated with an arterial stretch signal (also referred to as “arterial stretch data” or more generally as “sensor data”), obtained by each of a first arterial stretch sensor 1606 and a second arterial stretch sensor 1608 located near first and second physical locations along the artery of interest, respectively. In some specific embodiments, the first arterial stretch sensor 1606 and the second arterial stretch sensor 1608 are advantageously located near the first and second physical locations, between which arterial properties of the artery of interest (such as wall elasticity and diameter) can be considered or assumed to be relatively constant. In this way, PWW calculated based on PTT estimation is more representative of the actual PWV along a specific segment of the artery. Furthermore, blood pressure P estimated based on PWV is more representative of the true blood pressure. In some embodiments, the amplitude of the interval ΔD between the first arterial extension sensor 1606 and the second arterial extension sensor 1608 (and thus the distance between the first and second locations along the artery) can range from about 1 centimeter (cm) to several tens of centimeters, long enough to distinguish the arrival of the pulse at the first physical location from the arrival of the pulse at the second physical location, but close enough to provide sufficient assurance of arterial consistency. In some specific embodiments, the distance ΔD between the first arterial extension sensor 1606 and the second arterial extension sensor 1608 can range from about 1 cm to about 30 cm, and in some embodiments less than or equal to about 20 cm, and in some embodiments less than or equal to about 10 cm, and in some specific embodiments less than or equal to about 5 cm. In some other embodiments, the distance ΔD between the first and second arterial extension sensors 1606 and 1608 can be less than or equal to 1 cm, for example, about 0.1 cm, about 0.25 cm, about 0.5 cm, or about 0.75 cm. For reference, a typical PWV can be about 15 meters per second (m / s). Using a non-fixed monitoring device in which the first and second arterial extension sensors 1606 and 1608 are spaced about 5 cm apart, and assuming that a PWV of about 15 m / s means a PTT of about 3.3 milliseconds (ms).

[0164] The magnitude of the distance ΔD between the first arterial extension sensor 1606 and the second arterial extension sensor 1608 can be pre-programmed into the memory within the monitoring device that integrates these sensors (e.g., as referenced above). Figure 2 The memory of the described control system 206, or a memory configured to communicate with the control system 206. As those skilled in the art will appreciate, in such embodiments, the spatial length L of the pulse can be greater than the distance ΔD from the first arterial extension sensor 1606 to the second arterial extension sensor 1608. Thus, although Figure 16The graphical pulse 1602 shown is depicted as having a spatial length L comparable to the distance between the first arterial extension sensor 1606 and the second arterial extension sensor 1608, but in reality, each pulse can typically have a spatial length L greater than and even much greater than the distance ΔD between the first arterial extension sensor 1606 and the second arterial extension sensor 1608 (e.g., about an order of magnitude or more).

[0165] Sensing architecture and topology

[0166] In some embodiments of the non-fixed monitoring device disclosed herein, the first arterial stretch sensor 1606 and the second arterial stretch sensor 1608 are both sensors of the same sensor type. In some such embodiments, the first arterial stretch sensor 1606 and the second arterial stretch sensor 1608 are identical sensors. In such embodiments, each of the first arterial stretch sensor 1606 and the second arterial stretch sensor 1608 utilizes the same sensor technology with the same sensitivity to arterial stretch signals caused by propagating pulses and has the same time delay and sampling characteristics. In some embodiments, each of the first arterial stretch sensor 1606 and the second arterial stretch sensor 1608 is configured for photoacoustic volumetric plethysmography (PAPG) sensing, for example, as disclosed elsewhere herein. Some such embodiments include a light source system and two or more ultrasound receivers, which may be... Figure 2 Examples of a light source system 204 and an ultrasound receiver 202. In some embodiments, each of the first arterial stretch sensor 1606 and the second arterial stretch sensor 1608 is configured to perform ultrasound sensing by transmitting an ultrasound signal and receiving a corresponding reflection. In some alternative embodiments, each of the first arterial stretch sensor 1606 and the second arterial stretch sensor 1608 may be configured for impedance plethysmography (IPG) sensing, also known in the biomedical context as bioimpedance sensing. In various embodiments, regardless of the type of sensor used, each of the first arterial stretch sensor 1606 and the second arterial stretch sensor 1608 is widely used to capture and provide arterial stretch data indicating the arterial stretch signal generated due to pulse propagation through the arterial portion adjacent to the respective sensor. For example, the arterial stretch data may be provided from the sensor to the processor in the form of a voltage signal generated or received by the sensor based on the ultrasound signal or impedance signal sensed by the respective sensor.

[0167] As described above, during the systolic phase of the cardiac cycle, as the pulse travels along the artery through a specific location, the arterial wall stretches according to the pulse waveform and the elastic properties of the arterial wall. This stretching is accompanied by a corresponding increase in blood volume at the specific location or region, and with this increase in blood volume, one or more properties in that region change accordingly. Conversely, during the diastolic phase of the cardiac cycle, blood pressure in the artery decreases and the arterial wall constricts. This constriction is accompanied by a corresponding decrease in blood volume at the specific location, and with this decrease in blood volume, one or more properties in that region change accordingly.

[0168] In the context of bioimpedance sensing (or impedance plethysmography), blood in an artery has a higher conductivity than that of the surrounding or adjacent skin, muscle, fat, tendons, ligaments, bone, lymph, or other tissues. The susceptance (and thus the dielectric constant) of blood also differs from that of other types of surrounding or nearby tissues. When a pulse propagates through a particular location, the corresponding increase in blood volume leads to an increase in conductivity at that location (and more generally, an increase in admittance, or equivalently, a decrease in impedance). Conversely, during the diastolic phase of the cardiac cycle, the corresponding decrease in blood volume leads to an increase in resistivity at that location (and more generally, an increase in impedance, or equivalently, a decrease in admittance).

[0169] Bioimpedance sensors typically function by applying an electrical excitation signal to a region of interest (ROI) via two or more input electrodes at an excitation carrier frequency and detecting the output signal (or multiple output signals) via two or more output electrodes. In some more specific embodiments, the electrical excitation signal is a current signal injected into the RIO via the input electrodes. In some such embodiments, the output signal is a voltage signal representing the voltage response of tissue in the RIO to the applied excitation signal. The detected voltage response signal is influenced by the different, and in some instances time-varying, electrical properties of the various tissues through which the injected excitation current signal passes. In some embodiments where the bioimpedance sensor is operable for monitoring blood pressure, heart rate, or other cardiovascular characteristics, the detected voltage response signal is amplitude- and phase-modulated by the time-varying impedance (or conversely, admittance) of the underlying arteries (which fluctuates in sync with the user's heartbeat, as described above). To determine various biological characteristics, information from the detected voltage response signal is typically demodulated from the excitation carrier frequency component using various analog or digital signal processing circuits (which may include both passive and active components).

[0170] In some examples of incorporating ultrasound sensors, the measurement of arterial extension may involve, for example, directing ultrasound waves to an artery in a limb via one or more ultrasound transducers. Such ultrasound sensors are also configured to receive reflected waves, at least in part based on the directed waves. The reflected waves may include scattered waves, specular reflections, or both. The reflected waves provide information about the arterial wall and thus the arterial extension.

[0171] In some embodiments, regardless of the sensor type used for the first arterial extension sensor 1606 and the second arterial extension sensor 1608, both the first arterial extension sensor 1606 and the second arterial extension sensor 1608 may be arranged, assembled, or otherwise included within a single housing of a single flow monitoring device. As described above, the housing and other components of the monitoring device may be configured such that when the monitoring device is attached to or otherwise physically coupled to a subject, the first arterial extension sensor 1606 and the second arterial extension sensor 1608 are respectively in contact with or adjacent to the user's skin at a first position and a second position spaced apart by a distance ΔD, and in some embodiments along a segment of artery properties that may be assumed to be relatively constant. In various embodiments, the housing of the flow monitoring device is a wearable housing or is incorporated into or integrated with a wearable housing. In some specific embodiments, the wearable housing includes (or is connected to) a physical coupling mechanism for removable, non-invasive attachment to the user. The housing may be formed using any of a variety of suitable manufacturing processes, including injection molding and vacuum forming, etc. Additionally, the housing may be made of any of a variety of suitable materials, including but not limited to plastics, metals, glass, rubber, and ceramics, or combinations of these or other materials. In certain embodiments, the housing and coupling mechanism enable completely non-fixed use. In other words, some embodiments of the wearable monitoring devices described herein are non-invasive, without physical obstruction, and generally do not restrict the free and unrestricted movement of the subject's arms or legs, thereby enabling continuous or periodic monitoring of cardiovascular characteristics (such as blood pressure) even when the subject is moving or otherwise engaging in physical activity. Thus, non-fixed monitoring devices facilitate and enable long-term wear and monitoring (e.g., for days, weeks, or a month or longer without interruption) of one or more biometrics of interest to obtain a better profile of such characteristics and, in general, a better profile of the user's health over the extended duration.

[0172] In some implementations, non-fixed monitoring devices can be positioned by wrapping a strap or band around the user's wrist, similar to a watch or fitness / activity tracker. Figure 17AAn example non-fixed monitoring device 1700 designed to be worn on the wrist is shown according to some embodiments. In the example shown, the monitoring device 1700 includes a housing 1702 integrally formed, coupled, or otherwise integrated with a wristband 1704. In some instances, a first arterial stretch sensor 1706 and a second arterial stretch sensor 1708 each include the above-referenced... Figure 2 The described example of an ultrasound receiver 202 is part of a light source system 204. In this example, a non-fixed monitoring device 1700 is coupled around the wrist such that a first arterial extension sensor 1706 and a second arterial extension sensor 1708 within the housing 1702 are each positioned along a segment of the radial artery 1710 (note that when the monitoring device is coupled to a subject, the sensors are generally hidden from view of the subject-facing external surface of the housing, but exposed on the inner surface of the housing so that the sensors can obtain measurements from the underlying arteries through the subject's skin). Furthermore, as shown, the first arterial extension sensor 1706 and the second arterial extension sensor 1708 are spaced apart by a fixed distance ΔD. In some other embodiments, the non-fixed monitoring device 1700 can be similarly designed or adapted for positioning around the forearm, upper arm, ankle, lower leg, thigh, or fingers (all of which are referred to below as "limbs") using straps or bands.

[0173] Figure 17B An example non-fixed monitoring device 1700, designed to be worn on a finger according to some embodiments, is shown. In some instances, first and second arterial stretch sensors 1706 and 1708 may each include the referenced above. Figure 2 An example of the ultrasonic receiver 202 described herein and part of the light source system 204.

[0174] In some other embodiments, the non-fixed monitoring devices disclosed herein can be positioned on a user's region of interest without the use of straps or bands. For example, the first arterial stretch sensor 1706 and the second arterial stretch sensor 1708, along with other components of the monitoring device, can be encapsulated in a housing that is secured to the user's skin in the region of interest using an adhesive or other suitable attachment mechanism (an example of a "patch" monitoring device).

[0175] Figure 17C An example of a non-fixed monitoring device 1700 designed to reside on an earplug is shown according to some embodiments. According to this example, the non-fixed monitoring device 1700 is coupled to the housing of an earplug 1720. In some instances, a first arterial stretch sensor 1706 and a second arterial stretch sensor 1708 may each include the referenced above. Figure 2 An example of the ultrasonic receiver 202 described herein and part of the light source system 204.

[0176] The features and aspects will be understood from the following example embodiments (“EEE”):

[0177] EEE1. A bioassay system, comprising:

[0178] A first sensor, the first sensor including a first piezoelectric receiver located at a first piezoelectric receiver position;

[0179] Second sensor;

[0180] A light source system comprising one or more light sources configured to emit light; and

[0181] The control system is configured to:

[0182] The control light source system emits multiple light pulses into biological tissue, which includes blood and blood vessels at various depths within the biological tissue;

[0183] A first signal corresponding to a first acoustic wave emitted from various parts of biological tissue is received from a first piezoelectric receiver, the first acoustic wave corresponding to a first photoacoustic emission from blood and blood vessels caused by at least a first subset of the plurality of light pulses;

[0184] Receive a second signal from the second sensor;

[0185] The pulse conduction time data are determined at least in part based on the first and second signals; and

[0186] Blood pressure estimation is based at least in part on pulse conduction time data.

[0187] EEE2. The bioassay system according to claimed EEE1, wherein the second sensor includes a second piezoelectric receiver located at the position of a second piezoelectric receiver, wherein the second signal corresponds to a second acoustic wave emitted from various parts of biological tissue, the second acoustic wave corresponding to photoacoustic emission from blood and blood vessels caused by at least a second subset of the plurality of light pulses, and wherein the control system is further configured to determine pulse conduction time data based at least in part on the first signal and the second signal.

[0188] EEE3. The bioassay system according to claimed EEE2, wherein the first piezoelectric receiver and the second piezoelectric receiver are components of a piezoelectric receiver array.

[0189] EEE4. The biometric system according to claimed EEE1, wherein the second sensor includes an electrocardiogram (ECG) sensor, wherein the second signal includes ECG sensor data from the ECG sensor, and wherein the control system is configured to determine pulse conduction time data based at least in part on the first signal and the ECG sensor data.

[0190] The various illustrative logics, logic blocks, modules, circuits, and algorithmic processes described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. The interchangeability of hardware and software has been described substantially in terms of functionality, and illustrated in the various illustrative components, blocks, modules, circuits, and processes described above. Whether this functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the entire system.

[0191] Hardware and data processing means for implementing the various illustrative logics, logic blocks, modules, and circuits described in conjunction with the aspects disclosed herein may be implemented or performed by a general-purpose single-chip or multi-chip processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, or any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration. In some embodiments, specific processes and methods may be performed by circuitry specific to a given function.

[0192] In one or more aspects, the described functionality may be implemented in hardware, digital electronic circuit systems, computer software, firmware (including the structures disclosed herein and their structural equivalents), or any combination thereof. Embodiments of the subject matter described herein may also be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a computer storage medium for execution by a data processing apparatus or for controlling the operation of a data processing apparatus.

[0193] If implemented in software, the functionality can be stored or transmitted as one or more instructions or code on or on a computer-readable medium (such as a non-transitory medium). The processes of the methods or algorithms disclosed herein can be implemented in a processor-executable software module that can reside on a computer-readable medium. Computer-readable media include computer storage media and communication media, including any medium capable of transferring a computer program from one place to another. Storage media can be any available medium accessible to a computer. By way of example and not limitation, non-transitory media can include RAM, ROM, EEPROM, CD-ROM or other optical disc storage devices, disk storage devices or other magnetic storage devices, or any other medium that can be used to store the required program code in the form of instructions or data structures, and is accessible to a computer. Furthermore, any connection can be appropriately referred to as a computer-readable medium. As used herein, disks and optical discs include compact discs (CDs), laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray discs, where disks typically magnetically copy data, while optical discs optically copy data using lasers. Combinations of the above should also be included within the scope of computer-readable media. Furthermore, the operation of a method or algorithm may reside as one or any combination or set of code and instructions on a machine-readable and computer-readable medium that can be incorporated into a computer program product.

[0194] Various modifications to the embodiments described in this disclosure will be apparent to those skilled in the art, and the general principles defined herein can be applied to other implementations without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not intended to be limited to the embodiments shown herein, but should be given the broadest scope consistent with the claims, the principles disclosed herein, and the novel features. Where applicable, the word “exemplary” is used exclusively herein to mean “serving as an example, instance, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0195] Some features described in this specification in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually in multiple embodiments or in any suitable sub-combination. Furthermore, although features may be described above as functioning in certain combinations and even initially claimed in this way, in some cases, one or more features from the claimed combination may be removed from the combination, and the claimed combination may be for sub-combinations or variations thereof.

[0196] Similarly, although operations are depicted in a specific order in the accompanying drawings, this should not be construed as requiring such operations to be performed in the specific order shown or in a sequential manner, or to perform all the illustrated operations to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system components in the embodiments described above should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products. Additionally, other embodiments are within the scope of the appended claims. In some cases, the actions referenced in the claims can be performed in a different order and still achieve the desired result.

[0197] It should be understood that, unless features in any of the particular described embodiments are explicitly identified as incompatible with each other, or the surrounding context suggests that they are exclusive and not easily combined in a complementary and / or supporting sense, the entirety of this disclosure contemplates and envisions that specific features of those complementary embodiments may be selectively combined to provide one or more comprehensive, but slightly different, technical solutions. Therefore, it should be further understood that the above description has been given by way of example only, and modifications to the details are possible within the scope of this disclosure.

[0198] Various modifications to the embodiments described in this disclosure will readily be apparent to those skilled in the art, and the general principles defined herein can be applied to other embodiments without departing from the spirit or scope of this disclosure. Therefore, the appended claims are not intended to limit themselves to the embodiments shown herein, but should be given the broadest scope consistent with this disclosure, the principles disclosed herein, and the novel features thereof.

[0199] Furthermore, certain features described in this specification in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually in multiple embodiments or in any suitable sub-combination. Moreover, although features may be described above as functioning in certain combinations and even initially claimed in this way, in some cases, one or more features from the claimed combination may be removed from the combination, and the claimed combination may be for sub-combinations or variations thereof.

[0200] Similarly, although operations are depicted in a specific order in the accompanying drawings, this should not be construed as requiring such operations to be performed in the specific order shown or in a sequential manner, or to perform all the illustrated operations to achieve the desired result. Furthermore, the drawings may schematically depict one or more example processes in the form of a flow diagram. However, other operations not depicted may be incorporated into the schematically illustrated example processes. For example, one or more additional operations may be performed before, after, simultaneously with, or between any of the illustrated operations. Moreover, each operation in the described and illustrated operations may itself include and be collectively referred to as a plurality of sub-operations. For example, each of the aforementioned operations may involve the execution of a process or algorithm. Furthermore, in some embodiments, the various operations in the described and illustrated operations may be performed in combination or in parallel. Similarly, the separation of various system components in the above embodiments should not be construed as requiring such separation in all embodiments. Thus, other embodiments fall within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired result.

Claims

1. A bioassay system, comprising: piezoelectric receiver; The light source system is configured to emit multiple light pulses at a pulse repetition frequency of more than 10 Hz to less than 1 MHz; as well as The control system is configured to: The light source system is controlled to emit multiple light pulses into biological tissue at the pulse repetition frequency, the biological tissue including blood and blood vessels at various depths within the biological tissue; The signal corresponding to the sound waves emitted from various parts of the biological tissue is received from the piezoelectric receiver, the sound waves corresponding to the photoacoustic emission from the blood and the blood vessels caused by the plurality of light pulses; Detect the heart rate waveform in the signal; Determine the first subset of detected heart rate waveforms corresponding to venous heart rate waveforms; as well as Identify a second subset of the detected heart rate waveforms that correspond to the arterial heart rate waveform. Specifically, determining the first subset and the second subset of the detected heart rate waveforms as venous heart rate waveforms and arterial heart rate waveforms, respectively, includes: determining the peak-to-peak value of the signal in each of a plurality of acquisition time windows after the time of each of the plurality of light pulses; determining the first subset of the detected heart rate waveforms as the venous heart rate waveform in response to determining that the plurality of peak-to-peak values ​​corresponding to the plurality of acquisition time windows increase over time; and determining the second subset of the detected heart rate waveforms as the arterial heart rate waveform in response to determining that the plurality of peak-to-peak values ​​decrease over time.

2. The bioassay system according to claim 1, wherein, The control system is also configured to: Extract heart rate waveform features from the detected heart rate waveform; as well as Blood pressure estimation is based at least in part on the extracted heart rate waveform features.

3. The bioassay system according to claim 1, wherein, Receiving the signal from the piezoelectric receiver involves obtaining a depth-discriminating signal by applying a first to Nth acquisition time delay and receiving the first to Nth signals during the first to Nth acquisition time windows, each of the first to Nth acquisition time windows appearing after a corresponding acquisition time delay in the first to Nth acquisition time delay, where N is an integer greater than one.

4. The bioassay system according to claim 3, wherein, The control system is configured to determine, at least in part, a first subset of detected heart rate waveforms and a second subset of detected heart rate waveforms based on the depth difference signal.

5. The bioassay system according to claim 1, wherein, The control system is also configured to: Extract a set of hemodynamic features from the second subset of the detected heart rate waveforms; and The first blood pressure estimate is made at least in part based on the hemodynamic feature set.

6. The bioassay system according to claim 5, wherein, The control system is also configured to: Arterial-venous phase shift (AVPS) data are determined from the first subset and the second subset of the detected heart rate waveforms; and The first blood pressure estimate is made at least in part based on the AVPS data.

7. The bioassay system according to claim 6, wherein, The control system is also configured to: Extract heart rate waveform features from the detected heart rate waveform; A second blood pressure estimate is performed, at least in part, based on the extracted heart rate waveform features; and The third blood pressure estimate is made at least in part based on the first blood pressure estimate and the second blood pressure estimate.

8. The bioassay system according to claim 1, wherein, The control system is also configured to: Determining arterial-venous phase shift (AVPS) data from detected heart rate waveforms; and The first blood pressure estimate is made at least in part based on the AVPS data.

9. The bioassay system according to claim 8, wherein, The control system is also configured to: Extract heart rate waveform features from the detected heart rate waveform; A second blood pressure estimate is performed, at least in part, based on the extracted heart rate waveform features; and The third blood pressure estimate is made at least in part based on the first blood pressure estimate and the second blood pressure estimate.

10. A bioassay method, comprising: The control system controls the light source system to emit multiple light pulses at a pulse repetition frequency into biological tissue, which includes blood and blood vessels at various depths within the biological tissue; The control system receives signals from a piezoelectric receiver corresponding to sound waves emitted from various parts of the biological tissue, the sound waves corresponding to photoacoustic emissions from the blood and blood vessels caused by the plurality of light pulses; The heart rate waveform in the signal is detected by the control system. The control system determines a first subset of the detected heart rate waveforms corresponding to the venous heart rate waveforms. as well as The control system determines a second subset of the detected heart rate waveforms corresponding to the arterial heart rate waveform. Specifically, determining the first subset and the second subset of the detected heart rate waveforms as venous heart rate waveforms and arterial heart rate waveforms, respectively, includes: determining the peak-to-peak value of the signal in each acquisition time window of a plurality of acquisition time windows after the time of each of the plurality of light pulses; determining the first subset of the detected heart rate waveforms as the venous heart rate waveform in response to determining that the plurality of peak-to-peak values ​​corresponding to the acquisition time windows increase over time; and determining the second subset of the detected heart rate waveforms as the arterial heart rate waveform in response to determining that the plurality of peak-to-peak values ​​decrease over time.

11. The bioassay method according to claim 10, further comprising: The control system extracts heart rate waveform features from the detected heart rate waveform; as well as Blood pressure is estimated by the control system based at least in part on the extracted heart rate waveform features.

12. The bioassay method according to claim 10, wherein, Receiving the signal from the piezoelectric receiver involves obtaining a depth-discriminating signal by applying a first to Nth acquisition time delay and receiving the first to Nth signals during the first to Nth acquisition time windows, each of the first to Nth acquisition time windows appearing after a corresponding acquisition time delay in the first to Nth acquisition time delay, where N is an integer greater than one.

13. The bioassay method according to claim 12, further comprising: The control system determines, at least in part, the first subset of detected heart rate waveforms and the second subset of detected heart rate waveforms based on the depth difference signal.

14. The bioassay method according to claim 10, further comprising: The control system extracts a set of hemodynamic features from the second subset of the detected heart rate waveforms; as well as The first blood pressure estimate is performed by the control system based at least in part on the set of hemodynamic features.

15. The bioassay method according to claim 14, further comprising: The control system determines arterial-venous phase shift (AVPS) data from a first subset of detected heart rate waveforms and a second subset of detected heart rate waveforms. as well as The first blood pressure estimate is performed by the control system based at least in part on the AVPS data.

16. The bioassay method according to claim 15, further comprising: The control system extracts heart rate waveform features from the detected heart rate waveform; The control system performs a second blood pressure estimation based at least in part on the extracted heart rate waveform features; and The control system performs a third blood pressure estimate based at least in part on the first blood pressure estimate and the second blood pressure estimate.

17. The bioassay method according to claim 10, further comprising: The control system determines the arterial-venous phase shift (AVPS) data from the detected heart rate waveform; as well as The first blood pressure estimate is made by the control system based at least in part on the AVPS data.

18. The bioassay method according to claim 17, further comprising: The control system extracts heart rate waveform features from the detected heart rate waveform; The control system performs a second blood pressure estimation based at least in part on the extracted heart rate waveform features; and The control system performs a third blood pressure estimate based at least in part on the first blood pressure estimate and the second blood pressure estimate.

19. One or more non-transitory media having software stored thereon, the software including instructions for controlling one or more devices to perform a bioassay method, the bioassay method comprising: The control system controls the light source system to emit multiple light pulses at a pulse repetition frequency into biological tissue, which includes blood and blood vessels at various depths within the biological tissue; The control system receives signals from a piezoelectric receiver corresponding to sound waves emitted from various parts of the biological tissue, the sound waves corresponding to photoacoustic emissions from the blood and blood vessels caused by the plurality of light pulses; The heart rate waveform in the signal is detected by the control system. The control system determines a first subset of the detected heart rate waveforms corresponding to the venous heart rate waveforms. as well as The control system determines a second subset of the detected heart rate waveforms corresponding to the arterial heart rate waveform. Specifically, determining the first subset and the second subset of the detected heart rate waveforms as venous heart rate waveforms and arterial heart rate waveforms, respectively, includes: determining the peak-to-peak value of the signal in each acquisition time window of a plurality of acquisition time windows after the time of each of the plurality of light pulses; determining the first subset of the detected heart rate waveforms as the venous heart rate waveform in response to determining that the plurality of peak-to-peak values ​​corresponding to the acquisition time windows increase over time; and determining the second subset of the detected heart rate waveforms as the arterial heart rate waveform in response to determining that the plurality of peak-to-peak values ​​decrease over time.

20. One or more non-temporary media according to claim 19, wherein, The bioassay method further includes: The control system extracts heart rate waveform features from the detected heart rate waveform; and Blood pressure is estimated by the control system based at least in part on the extracted heart rate waveform features.

21. One or more non-temporary media according to claim 19, wherein, Receiving the signal from the piezoelectric receiver involves obtaining a depth-discriminating signal by applying a first to Nth acquisition time delay and receiving the first to Nth signals during the first to Nth acquisition time windows, each of the first to Nth acquisition time windows appearing after a corresponding acquisition time delay in the first to Nth acquisition time delay, where N is an integer greater than one.

22. One or more non-temporary media according to claim 21, wherein, The biometric method further includes: determining, at least in part, a first subset of detected heart rate waveforms and a second subset of detected heart rate waveforms by the control system based on the depth difference signal.

23. One or more non-temporary media according to claim 19, wherein, The bioassay method further includes: The control system extracts a set of hemodynamic features from the second subset of the detected heart rate waveforms; and The first blood pressure estimate is performed by the control system based at least in part on the set of hemodynamic features.

24. One or more non-temporary media according to claim 23, wherein, The bioassay method further includes: The control system determines arterial-venous phase shift (AVPS) data from a first subset of detected heart rate waveforms and a second subset of detected heart rate waveforms; and The first blood pressure estimate is performed by the control system based at least in part on the AVPS data.

25. One or more non-temporary media according to claim 24, wherein, The bioassay method further includes: The control system extracts heart rate waveform features from the detected heart rate waveform; The control system performs a second blood pressure estimation based at least in part on the extracted heart rate waveform features; and The control system performs a third blood pressure estimate based at least in part on the first blood pressure estimate and the second blood pressure estimate.

26. One or more non-temporary media according to claim 19, wherein, The bioassay method further includes: The control system determines arterial-venous phase shift (AVPS) data from the detected heart rate waveform; and The first blood pressure estimate is made by the control system based at least in part on the AVPS data.

27. One or more non-temporary media according to claim 26, wherein, The bioassay method further includes: The control system extracts heart rate waveform features from the detected heart rate waveform; The control system performs a second blood pressure estimation based at least in part on the extracted heart rate waveform features; and The control system performs a third blood pressure estimate based at least in part on the first blood pressure estimate and the second blood pressure estimate.

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