Blood pressure monitoring equipment based on light volume change tracing method

Abstract

A blood pressure monitoring device includes a wristband, a housing, and a display. The strap portion is adapted to secure the device to the wrist without obstructing blood flow. At least one sensor modality is disposed within the housing for obtaining sensor data from an artery in the wrist. In a preferred embodiment, a photoplethysmography sensor is incorporated in a housing to generate PPG waveform data. A processor within the housing is operable to calculate an average arterial pressure based on the extracted and calculated characteristics of the sensor data, and optionally to calculate diastolic and systolic blood pressures. Related methods and systems are also described.

Description

Cross-references to related applications

[0001] This application claims priority to provisional patent application No. 63 / 466,089, filed on May 12, 2023, entitled “PHOTOPLETHYSMOGRAPHY-BASED BLOODPRESSURE MONITORING DEVICE”. Background of the Invention

[0002] This invention relates to blood pressure measurement, and more specifically, to a non-invasive, non-compression measurement of mean arterial pressure.

[0003] Mean arterial pressure (MAP) is an individual's average blood pressure during a single cardiac cycle. MAP is considered the perfusion pressure felt by the body's organs. If MAP remains low for an extended period, vital organs will not receive enough oxygen.

[0004] MAP can be measured directly using invasive monitoring methods, such as endovascular pressure sensors. However, endovascular devices can cause problems such as embolism, nerve damage, infection, bleeding, and / or damage to the vessel wall. Additionally, implantation of endovascular leads requires highly skilled physicians, such as surgeons, electrophysiologists, or interventional cardiologists.

[0005] Additionally, at a normal resting heart rate, the mean arterial pressure (MAP) can be approximated by measuring systolic blood pressure (SBP) and diastolic blood pressure (DBP) and applying a formula where the lower (diastolic) blood pressure is doubled and added to the higher (systolic) blood pressure, and then this composite sum is divided by 3. [1] MAP ≈ (2 × DBP + SBP) / 3 SBP and DBP can be measured using traditional blood pressure cuff devices. However, these devices are undesirable because of blocked blood vessels. Furthermore, due to the occlusive nature of these types of devices, they cannot be worn for extended periods. Therefore, cuff-based devices are not well-suited for continuous blood pressure monitoring.

[0006] Attempts have been made to measure blood pressure without a cuff using pulse arrival time (PAT) and pulse transit time (PTT). Both PAT and PTT measure the time delay of the pulse from the heart to the finger, and this time delay has been shown to correlate with systolic and diastolic blood pressure. See, for example, Mukkamala et al., Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice, IEEE Trans Biomed Eng. 2015 Aug; 62(8):1879-901. See also U.S. Patent No. 10,722,131 to Banet.

[0007] Pulse arrival time (PAT) and pulse transit time (PTT) are typically measured using a conventional vital signs monitor, which includes separate modules to determine the electrocardiogram (ECG) and values ​​for pulse oxygen saturation (SpO2). To obtain ECG values, multiple electrodes are usually attached to the patient's chest to determine the time-dependent component of the ECG waveform, characterized by a sharp spike known as a "QRS complex." The QRS complex indicates the initial depolarization of the heart's ventricles and informally marks the onset of the heartbeat and subsequent pressure pulse.

[0008] To obtain SpO2, a bandage- or clothespin-shaped sensor is attached to the patient's finger and includes an optical system that operates in a spectral region specific to detecting and quantifying the amount of hemoglobin in the lower arteries. The optical module typically includes a first light source and a second light source (e.g., light-emitting diodes (or LEDs)) that transmit light radiation at red (λ—600nm–700nm) and infrared (λ—800nm–1200nm) wavelengths, respectively.

[0009] A photodetector measures radiation emitted from an optical system as it passes through the patient's finger. Other body parts, such as the ear, forehead, and nose, can also be used instead of the finger. During measurement, a microprocessor analyzes both the red and infrared radiation measured by the photodetector to determine time-dependent waveforms corresponding to different wavelengths; each waveform is called a photoplethysmogram (PPG). The PPG displays the change in arterial blood volume with each heartbeat, based on the amount of radiation absorbed along the light path between the LED and the photodetector. SpO2 values ​​can be calculated from the PPG waveforms. The time-dependent characteristics of the PPG waveforms indicate changes in pulse rate and volume absorbance in the underlying arteries (e.g., in the finger) caused by the propagating pressure pulse.

[0010] A typical PAT measurement determines the time interval between the maxima on the QRS complex (indicating the peak of ventricular depolarization) and a portion of the PPG waveform (indicating the arrival of the pressure pulse). PAT depends primarily on arterial compliance, the distance the pressure pulse travels (very close to the length of the patient's arm), and blood pressure. Taking into account patient-specific properties such as arterial compliance, PAT-based blood pressure measurements typically use a conventional blood pressure cuff "calibration." Typically, during the calibration process, a blood pressure cuff is applied to the patient for one or more blood pressure measurements, and then the cuff is removed. Looking ahead, the calibration measurement, along with changes in PAT, is used to determine a patient's blood pressure and blood pressure variability. PAT is generally inversely related to blood pressure; that is, a decrease in PAT indicates an increase in blood pressure.

[0011] The aforementioned systems have several drawbacks, including: for example, they require the placement of electrodes and sensors at multiple different locations on the patient; they require the use of two different types of devices (i.e., ECG electrode sets and readers, and pulse oximetry devices); they carry the risk of increased waveform detection errors due to the need for ECG; and they require finger clips, which are inconvenient to wear for extended periods.

[0012] In light of the above, there is a need for more reliable, robust, and convenient blood pressure monitoring devices. Invention Overview

[0013] A blood pressure monitoring device for calculating a user's mean arterial pressure includes a housing and a strap adapted to hold the housing against a patient's wrist. A sensor is disposed within the housing and aligned with a capillary artery in the wrist when the housing is fitted against the wrist. A processor is disposed within the housing and operable to: calculate multiple features based on data generated by the sensor; and calculate the mean arterial pressure (MAP) based on these multiple features.

[0014] In one embodiment, a method for monitoring a person's mean arterial pressure (MAP) includes: activating at least two PPG probes aligned with capillaries in a person's wrist to generate velocity data; and automatically calculating on a processor: multiple features from the PPG velocity data; and a user MAP based on the multiple features.

[0015] Optionally, SBP and DBP can be calculated based on these multiple features.

[0016] In an embodiment, the method further includes evaluating signal quality, wherein evaluating signal quality includes: calculating a reference template, comparing the beat morphology of each pulse with the reference template, identifying low-quality features based on the comparison step, and excluding low-quality features from a plurality of features used in the MAP calculation step.

[0017] In an embodiment, the method further includes calculating a defined threshold MAP range after the MAP calculation step, and recalculating the MAP based on the defined threshold MAP range.

[0018] In an embodiment, the mean and median values ​​may be used to determine the defined threshold MAP range based on the calculated BP error and confidence level.

[0019] In one embodiment, a display on the housing presents blood pressure information to the user.

[0020] In one embodiment, a blood pressure monitoring system for calculating a user's mean arterial pressure includes: a housing and a window adapted to remain against the patient's skin; at least two PPG probes disposed within the housing; and a processor. The processor is disposed and operable to: calculate multiple features based on velocity data generated by the two or more PPG probes; and calculate the mean arterial pressure (MAP) based on the multiple features.

[0021] In one embodiment, a light emitter guides light through a window toward the artery. In another embodiment, the light emitter is integrated within the PPG probe. In other embodiments, a light emitter, independent of the PPG probe, is arranged in a housing and guides light through a window toward the artery. PPG velocity data is based at least in part on the absorption of light by the artery and the blood flow through it.

[0022] In the embodiments, several features include viscosity, heart rate, and blood oxygenation.

[0023] In the embodiments, multiple features include: diastolic velocity, systolic velocity, systolic volume, diastolic volume, diastolic distance, systolic distance, heart rate, diastolic time and / or systolic time.

[0024] In an embodiment, the processor may also be operable to calculate diastolic blood pressure (DBP) based on velocity data, and optionally, to calculate systolic blood pressure based on the calculated MAP and DBP.

[0025] In one embodiment, the blood pressure monitoring system includes a trained model for determining the mean maximum (MAP) based on multiple features extracted from velocity data.

[0026] In one embodiment, the blood pressure monitoring system also includes a console, and the processor is enclosed within the console. The housing, window, and at least two PPG probes can be combined as a handheld tool, which is connected to the console via an umbilical cord.

[0027] In one embodiment, the blood pressure monitoring system is arranged in the form of a thin patch, and optionally, the system includes an adhesive layer that bonds the patch to the skin.

[0028] In one embodiment, the sensor is aimed at different locations within the same anatomical region of the body. In another embodiment, the target locations are less than 110 mm apart, or between 40 mm and 60 mm apart. In some embodiments, the target locations are less than 50 mm apart, more preferably less than 35 mm apart. Examples of different anatomical regions of the body to which all sensors are aimed include the fingers, wrists, upper arms, thighs, chest, neck, and ears.

[0029] In one embodiment, one sensor is positioned against a blood vessel near the wrist, while another sensor is positioned against a blood vessel along the forearm. The sensors may be spaced 10-20 cm apart, or in some embodiments, approximately 10-15 cm apart.

[0030] In this embodiment, the sensors do not simultaneously measure data from different anatomical sites of the body. For example, in this embodiment, the system's sensors are not simultaneously pointed at both the chest and the wrist. In this embodiment, the BP monitoring system's sensors are pointed at only one anatomical body site or another anatomical body site, and detect volumetric flow rate data from only one body site.

[0031] In an embodiment, the processor is operable to prompt the user with a reading related to actual blood pressure (e.g., an oscillometric compression cuff) and to calculate a patient-specific scaling factor (e.g., P) based on the reading related to actual blood pressure. f ), and where MAP is based on a patient-specific scaling factor.

[0032] In some embodiments, the data generated by the sensor modality is velocity data and volumetric flow rate data through the patient's blood vessels, and the processor is programmed and operable to calculate the patient's BP value based on the volumetric flow rate data (or features extracted or calculated from the volumetric flow rate data). In some embodiments, the BP value is calculated based on the patient's volumetric flow rate data, and a BP database is not used to correlate pressure with the sensor data.

[0033] To avoid being bound by theory, calculating BP values ​​based on patient volumetric flow rate data itself is more accurate than matching BP values ​​to PPG signals using a database, as errors can occur during database generation. Errors can arise due to differences in personnel, hardware, and software between users and hospitals. Given the time scale, even small time differences can significantly affect BP value calculations. Therefore, in some embodiments of the invention, a database of BP values ​​(correlated with sensor signals) is avoided.

[0034] Advantages of the embodiments of the present invention Embodiments of the present invention are able to determine pressure values ​​without measuring elevation or changes in elevation.

[0035] Embodiments of the present invention are able to determine pressure values ​​without measuring expansion or changes in expansion.

[0036] Embodiments of the present invention are able to determine pressure values ​​without measuring air pressure or changes in air pressure.

[0037] Embodiments of the present invention are able to determine pressure values ​​without measuring information at multiple anatomical regions.

[0038] Embodiments of the present invention are able to determine pressure values ​​without using ECG data.

[0039] The embodiments of the present invention are able to continuously monitor BP pressure values ​​without compression.

[0040] Embodiments of the present invention are able to determine pressure values ​​based on photovolume change mapping velocity data generated by a non-invasive wearable bracelet-like device.

[0041] Further descriptions, features, and advantages of the invention will become apparent from the following detailed description, together with the accompanying drawings. Brief description of the attached diagram

[0042] Figure 1 This is a front perspective view of a blood pressure monitoring device according to an embodiment of the present invention; Figure 2 yes Figure 1 An enlarged rear view of a portion of the blood pressure monitoring device shown; Figure 3 yes Figure 1 An enlarged front view of a portion of the blood pressure monitoring device shown; Figure 4 A front view of a blood pressure monitoring device placed on the left wrist according to an embodiment of the present invention is shown; Figure 5 This is a block diagram of a blood pressure monitoring device according to an embodiment of the present invention; Figure 6 This is a schematic illustration of another blood pressure monitoring device according to an embodiment of the present invention; Figure 7 is from Figure 12 The illustrated PPG waveform recorded by the blood pressure monitoring device is shown. Figure 8 This is a flowchart illustrating an overview of a method for calculating blood pressure based on PPG information according to an embodiment of the present invention; Figure 9 This is a flowchart illustrating another method for calculating blood pressure based on PPG information according to an embodiment of the present invention; Figure 10 This is a block diagram of a PPG system including a microcontroller and sensors according to an embodiment of the present invention; Figure 11 This is a block diagram of another PPG data acquisition system including a microcomputer and electronic devices according to an embodiment of the present invention; Figure 12 This is a flowchart illustrating another method for calculating blood pressure based on PPG information according to an embodiment of the present invention; Figure 13 This is a flowchart illustrating another method for calculating blood pressure based on PPG information according to an embodiment of the present invention; and Figures 14-15 These are illustrations of the calculated blood pressure datasets in tabular and graphical formats, respectively. Detailed description of the invention

[0043] Before describing the invention in detail, it will be understood that the invention is not limited to the specific variations set forth herein, as various changes or modifications can be made to the described invention without departing from the spirit and scope of the invention, and equivalent substitutions can be made. As will be apparent to those skilled in the art upon reading this disclosure, each of the various embodiments described and illustrated herein has discrete components and features that can be readily separated from or combined with features of any of the other several embodiments without departing from the scope or spirit of the invention. Furthermore, modifications can be made to adapt specific circumstances, materials, composition of substances, processes, actions of processes, or steps to the objectives, spirit, or scope of the invention. All such modifications are intended to fall within the scope of the claims herein.

[0044] The methods described herein can be implemented in any logically possible order of the listed events, as well as in the order in which the events are listed. Furthermore, where a range of values ​​is provided, it will be understood that every intermediary value between the upper and lower limits of that range, and any other specified value or intermediary value within that range, is included within the invention. At the same time, it is considered that any optional features of variations of the described invention may be presented and claimed independently or in combination with any one or more features described herein.

[0045] All existing topics (such as publications, patents, patent applications, and hardware) mentioned herein are incorporated herein by reference in their entirety, unless such topic may conflict with the subject matter of this invention (in which case the content of this document shall prevail).

[0046] The U.S. Patent No. 20220225885, filed by Jeffrey Loh on December 28, 2021, entitled “Non-Invasive Non-Compressive Blood Pressure Monitoring Device,” is incorporated herein by reference in its entirety for all purposes.

[0047] References to singular items include the possibility of multiple identical items. More specifically, as used herein and in the appended claims, unless the context clearly indicates otherwise, the singular forms “a,” “an,” “said,” and “the” include plural objects. It should also be noted that claims may be drafted to exclude any optional elements. Thus, this statement is intended to serve as a prior basis for the use of such exclusive terms such as “unique,” ​​“only,” etc., or the use of negative limiting terms, in conjunction with the description of the claim elements.

[0048] Overview Figure 1 A blood pressure monitoring device 100 according to an embodiment of the present invention is shown. The blood pressure monitoring device 100 is shown having a strap 120, a buckle 124, and a display 140 arranged on a housing 142. The dimensions of the strap and housing are determined to be operable to securely fasten to a person's wrist (not shown) without compressing, blocking, dilating, or otherwise interfering with the user's vascular system.

[0049] refer to Figure 2 The back of housing 142 shows multiple pairs of sensors or probes 110, 112, 114 for obtaining blood flow information, which, as discussed further herein, allows for the automatic calculation of blood pressure values. In an embodiment, a thin window or protective layer is disposed above the sensors on the back of the housing. The window may be made of a material that allows sound waves and / or electromagnetic waves to pass through it.

[0050] refer to Figure 3 The display 140 is operable to display various blood pressure information, including but not limited to heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and optionally date and time. Optionally, one or more buttons 170 are located on the housing for controlling the device to perform various functions, such as calibration mode, positioning (or sensor positioning adjustment) mode, and / or monitoring mode, as discussed further herein.

[0051] Figure 4 A blood pressure monitoring device 100 according to an embodiment of the present invention is shown, which is fastened to a user's left wrist (LW). When fastened, the rear side of the housing rests flat against the outside of the left wrist.

[0052] In an embodiment, the device includes a positioning mode or module operable to select which sensor or sensor combination corresponds to the blood pressure monitoring mode. In another embodiment, the device is programmed to automatically evaluate which sensor or sensor combination is optimal based on which sensor or sensor combination exhibits the best signal reception (pickup). Optionally, (e.g., if signal sensing is insufficient), the device will prompt the user to move / adjust the device's position along the user's skin until an optimal signal is detected, as discussed further herein. Thus, the positioning mode can provide an optimal sensor combination for each location, as well as optimal positioning considering each sensor combination available to the device.

[0053] Additionally, in this embodiment, during blood pressure monitoring mode, the device is operable to automatically and periodically check the signal strength of each sensor and select the sensor combination with the best signal. This step is used to continuously ensure that the optimal sensors are used for blood pressure monitoring.

[0054] System Architecture Figure 5 This is a block diagram of a blood pressure monitoring device 100 according to an embodiment of the present invention. The device includes a plurality of PPG sensors 110, 112, PPG electronics 192, and a main printed circuit board 150 supporting a CPU and memory. As further discussed herein, the CPU, memory, and electronics are operable to control the probes and evaluate the PPG signals generated by the probes. Preferably, 2-10 PPG probes are arranged in the housing such that several probes are closely proximate to the artery to be inquired when the device is fastened to the user's wrist. In an embodiment, the housing comprises, as shown in the diagram... Figure 2 The diagram shows a configuration of 2-6 PPG sensors.

[0055] Figure 5Also shown is a power source (preferably a rechargeable battery) 130, an output (e.g., a display 140 or a speaker 162), a communication interface 160 (preferably a wireless near-field communication module (such as Bluetooth®)), a port 132 for charging the battery and / or transmitting data to and from the device via a charging cable (such as a USB charging cable), and an input 170 (e.g., a button or a touchscreen), all of which communicate with each other.

[0056] Preferably, port 132 has a low-profile design suitable for connection to a standard charging cable interface (e.g., a 2-pin magnetic, clip-on, or USB-C connector).

[0057] The memory stores data, information, and computer programs containing instructions for the CPU or other components of the blood pressure monitoring device 100. The type of information stored can vary and includes, but is not limited to, raw data of sensor signals, models and algorithms for processing the data, processed sensor signals, extracted features, patient personal information, vital signs (SVP), DBP, HR, and MAP. Examples of memory include volatile (e.g., RAM) and non-volatile memory types. In embodiments, the system includes a flash memory device for storing and recording new data. In fact, unless expressly excluded by any appended claims, the invention is intended to include various types of memory, processors, and circuitry.

[0058] Device 100 is operable to alert a user based on an assessment of information. The device alerts the user if the information is outside a predetermined range. Examples of alerts include audible alarms via audio component 162, visual graphics displayed on display 140, text messages or emails sent to the user or hospital care, etc. Examples of information types that will generate an alert when the information is outside a predetermined range include, but are not limited to, battery or power levels, vital signs values, MAP, SBP, or DBP values.

[0059] Optionally, in this embodiment, information is transmitted to a portable computing device, such as a smartphone, tablet, or laptop. Additionally, in this embodiment, a (local or remote) server is programmed and operable to communicate with the portable computing device. Data can be recorded, stored, evaluated, and compiled by the server for backup and preservation, and for further updating or training the BP model. Updated firmware, software, algorithms, models, and applications can also be downloaded from the server to a remote device and then to the PPG BP monitoring system described herein.

[0060] MAP determined using photovolume change plotting (PPG) method In an embodiment of the present invention, PPG information obtained from a PPG sensor is used to determine the MAP.

[0061] refer to Figures 6-7B The diagram illustrates a PPG system 700 for deriving MAP and exemplary recorded PPG waveforms 800, 800'. The PPG system 700 is shown having a first PPG sensor 710 and a second PPG sensor 712, which are positioned above one or more capillaries in the patient's or user's arm. The first PPG sensor 710 is spaced a fixed distance (X) from the second PPG sensor 712. Each PPG sensor is operable to send and receive light entering the patient's arm within a few millimeters. (See above for reference.) Figure 4 As described, the PPG sensor can be contained within housing 730. The PPG sensor can be arranged side-by-side with other sensors (e.g., Doppler, photoacoustic, other sensors). In embodiments, the system includes a combination of different types of sensors. An exemplary PPG sensor is the Valencell BW 4.0 manufactured by Valencell in Raleigh, North Carolina.

[0062] exist Figure 6 In the illustrated embodiment, system 700 includes PPG electronics 740. PPG electronics 740 (optionally in the form of one or more PCBs) may include one or more processors, memory and storage devices, AD converters, communication modules (e.g., for hardwire or wireless), and power or interface connections.

[0063] In operation, the PPG sensor can work together with PPG electronics to generate and record data as blood travels through blood vessels (e.g., RA or other capillaries). Figure 7A , Figure 7B The PPG wave is shown in the diagram. As further described herein, the device is operable to extract and calculate multiple features from the blood pulse wave as it passes through each of the PPG sensors 710, 712.

[0064] A review of PPG sensor methods refer to Figure 8 This paper presents an overview of a method 850 for calculating blood pressure according to an embodiment of the present invention.

[0065] Step 860 describes activating the PPG sensor. This can be done by the user (i.e., turning it on). Figure 6 The PPG sensors 710 and 712 shown are used to perform this step. When in, as in... Figure 1 When the housing 100 shown contains a sensor, the user can activate the sensor via the switch button 170.

[0066] In one embodiment, the system is programmed to continuously or periodically activate the sensor. In another embodiment, a BP application stored on a user's portable computing device is operable to activate the sensor and / or create a BP monitoring schedule that controls when the sensor is activated. Unlike compressed BP monitors, embodiments of the present invention can continuously activate and monitor the BP in real time (e.g., every 60 seconds or less, or more preferably every 30 seconds or less).

[0067] Step 870 describes calculating PPG wave characteristics based on data generated by the PPG sensor. This step can be performed by sending data generated by the PPG sensor and PPG electronics to a processor operable to extract PPG waveform characteristics (e.g., from data generated by the PPG sensor and PPG electronics respectively). Figure 7A and Figure 7B The waveforms 800 and 800' shown are used to extract and compute various features. Waveform 800 is an example of data generated from a first PPG sensor 710 aligned with capillary 702. A second PPG sensor, fixedly spaced from the first sensor within housing 100, is operable to generate a second waveform (800'). The processor is operable to extract and compute various features from the first waveform 800 and the second waveform 800', and to compute features characterizing the changes between the two waveforms. Examples of extracted features include, but are not limited to, pulse wave start, systolic peak, diastolic peak, pulse wave end, and the time of occurrence of each feature. In one embodiment, given a PPG waveform input, the processor is operable to automatically detect these features based on, for example, the assumption that the highest amplitude and the second highest amplitude are the systolic peak and the diastolic peak, respectively, and that the pulse end / pulse start is the point of minimum amplitude. In other embodiments, the processor applies a trained model to extract these features based on the input waveform.

[0068] As described above, multiple features are automatically calculated based on the extracted or detected features. Examples of the calculated features include, but are not limited to, heart rate, systolic and diastolic duration, systolic and diastolic velocity, systolic and diastolic volume, and systolic and diastolic distances traveled, as combined below. Figure 9 Further description.

[0069] Step 880 describes the calculation of mean arterial pressure (MAP) based on the calculated wave characteristics. See below for reference. Figure 9 The steps are performed by the processor according to computer-readable instructions stored in memory, as described in detail.

[0070] Step 890 describes the calculation of diastolic blood pressure (DBP). This step can be executed by a processor according to computer-readable instructions stored in memory, as referenced below. Figure 9 As described in detail.

[0071] Step 892 describes the calculation of systolic blood pressure (SBP). This step can be executed by a processor according to computer-readable instructions stored in memory, as referenced below. Figure 9 As described in detail.

[0072] refer to Figure 9 This illustrates an embodiment of the invention based on a PPG sensor (e.g., Figure 6 Detailed method 900 for calculating blood pressure from PPG information generated by the PPG sensors 710, 712 shown.

[0073] Step 910 describes the detection of features from the PPG sensor data. In an embodiment, the first PPG sensor 710 records a PPG waveform 800. The PPG waveform 800 exhibits various wave characteristics, including: pulse wave start (a1), pulse wave systolic peak (b), pulse wave diastolic peak (c), and pulse wave end / start (a2). As described above, the processor is operable to automatically detect the various features and record the value and time of each feature.

[0074] Similarly, such as Figure 6 As shown, a second PPG sensor 712, fixedly spaced at a distance (X) from the first PPG sensor 710, records the corresponding PPG waveform (800'). The device identifies corresponding features from the second wave, including: pulse wave start (a1'), pulse wave systolic peak (b'), pulse wave diastolic peak (c'), and pulse wave end / start (a2'). As described above, the processor is operable to automatically detect various features and record the value and time of each feature. For example, the pulse wave start point can be defined by the point of maximum slope between adjacent pulses.

[0075] Step 930 describes the calculation of the systolic velocity and the diastolic velocity. This step is performed by the processor, where the velocity is equal to distance divided by time.

[0076] refer to Figure 6 The distance between the sensors is fixed and equal to X. In the embodiments, X ranges from 1mm to 20mm, more preferably from 15mm to 35mm, and optionally from 25mm to 50mm.

[0077] Furthermore, the travel time of the pulse wave systolic peak between sensors can be calculated from the recorded PPG waveform and is equal to (b'-b). In this embodiment, the time (b'-b) ranges from approximately 5 ms to 40 ms, more preferably from 10 ms to 20 ms. Therefore, contraction rate =

[0078] Similarly, we calculate the diastolic velocity, where the time it takes for the peak diastolic pulse wave to travel between sensors is (c'-c). Therefore, Diastolic velocity =

[0079] Step 950 describes how to calculate the systolic volume and diastolic volume based on the corresponding velocity.

[0080] The systolic volume is equal to the systolic distance traveled multiplied by the area (A) of the artery, where the systolic distance traveled by blood is equal to the systolic velocity multiplied by time, and the area (A) can be determined by ultrasound or other means. As mentioned above, we know the velocity and time. Therefore, The systolic distance of blood travel =

[0081] And the systolic volume is equal to the systolic distance traveled multiplied by the area of ​​the artery (A), or Volume during contraction =

[0082] Similarly, the diastolic volume is equal to the diastolic distance traveled multiplied by the area of ​​the artery (A), where the diastolic distance traveled by blood is equal to the velocity multiplied by the time. As described above, we know the velocity and the time.

[0083] Therefore, the diastolic distance of the journey is: Diastolic distance =

[0084] And the diastolic volume is equal to the diastolic distance traveled multiplied by the area of ​​the artery (A), or Diastolic volume =

[0085] Step 960 describes the calculation of mean arterial pressure (MAP). This step is performed automatically on a programmed processor, where MAP is equal to cardiac output (CO) multiplied by systemic vascular resistance (SVR), and Where CO equals heart rate (HR) multiplied by stroke volume (SV), and stroke volume (SV) equals systolic volume (calculated in this embodiment according to step 950 above) multiplied by the scaling factor P. f ,or Stroke volume = systolic volume * P f =

[0086] Where the proportionality constant P f The calculation can be performed as described in this article. Therefore, cardiac output (CO) can be determined according to the following formula: CO=

[0087] HR can be estimated based on the time of a pulse wave or (a2-a1), or 60 / (a2-a1) beats / minute.

[0088] We also learned that systemic vascular resistance (SVR) equals pressure change (Δp) divided by volumetric flow rate (vol). f ).

[0089] The pressure change (Δp) can be calculated based on the systolic and diastolic velocities described above. In embodiments, the pressure change (Δp) is approximately equal to the velocity change (or Δv) between the systolic and diastolic velocities. In some embodiments, this is based on the Poiseuille equation (e.g., ΔP = 4Δv) or the Bernoulli equation (e.g., ΔP = Δv). 2 We use this to estimate the pressure change (Δp). In the latter case, and after substituting the velocity into the equation, the pressure change (Δp) is approximately equal to the following: Pressure change (Δp) = Volumetric flow rate (vol) f The average systolic velocity can be approximated as the area of ​​the artery (A) multiplied by the average systolic velocity, where the average systolic velocity equals (systolic velocity + diastolic velocity) / 2, or the average systolic velocity =

[0090] and, Volumetric flow rate (Vol) f )=

[0091] Systemic vascular resistance (SVR) can now be simplified as follows: SVR = Pressure Change / Vol f = =

[0092] Inserting CO and SVR into the equations for MAP provides: MAP = CO × SVR =

[0093] =

[0094] HR, al, b, b', c, and c' are automatically detected from the PPG waveform, and x equals the fixed distance between the sensors, as described above.

[0095] The initial calculation of P is performed by calibrating the blood pressure device to the measured blood pressure reading using a clinically acceptable BP measurement device (e.g., a conventional oscillometric compression cuff device as described above). fBy inputting the individual's current MAP blood pressure reading from a compression blood pressure device, the MAP algorithm listed above can be used to derive P. f .

[0096] Step 970 describes the calculation of diastolic blood pressure (DBP). This step is performed automatically on a programmed processor.

[0097] Diastolic blood pressure (DBP) = Diastolic output (DO) SVR, where DO equals HR diastolic stroke volume.

[0098] As mentioned above, HR can be measured directly by a PPG sensor, and SVR can be calculated as described above. Diastolic volume can be calculated using the following equation: Diastolic stroke volume (DSV) = diastolic volume * proportionality factor (Pd), or DSV=

[0099] Inserting HR, SVR, and diastolic stroke volume into the diastolic blood pressure (DBP) equation provides: DBP=

[0100] =

[0101] Pd is the diastolic proportionality factor, and it can initially be calculated by calibrating a blood pressure device to the measured blood pressure reading using a clinically acceptable BP measurement device (e.g., a conventional oscillometric compression cuff device as described above). Pd can be derived using the DBP algorithm listed above by inputting the current diastolic blood pressure reading from an individual using a compression blood pressure device.

[0102] Step 980 describes the calculation of systolic blood pressure (SBP). This step is performed automatically on a programmed processor.

[0103] SBP can be approximated by the following equation: MAP = DBP + (SBP - DBP) / 3.

[0104] This means SBP = (3 MAP) - (2 DBP), where MAP and DBP can be calculated as described above.

[0105] Computational model While exemplary models for automatically calculating blood pressure values ​​(including, for example, MAP, SBP, DBP) based on sensor data have been described above, a wide variety of models can be used to calculate blood pressure based on features extracted from recorded waveforms. In embodiments, machine learning or AI models are trained and used to estimate blood pressure values ​​based on one or more of the features described above. Examples of suitable models include, but are not limited to, artificial neural networks (e.g., trained CNNs). In embodiments, CNNs are trained on user data to correlate various extracted features (such as those described above) with blood pressure.

[0106] Function approximation using machine learning (e.g., deep neural networks) has been described in various publications (e.g., Jonas Adler et al., “Solving ill-posed inverse problems using iterative deep neural networks”, Vol. 33, No. 12, Inverse Problems, 2017). Function approximation models can be trained on data collected by simultaneously recording values ​​on different subject groups using the novel PPG blood pressure monitoring device and blood pressure monitor described herein. The extracted features are correlated with the actual measured BP values. Ultimately, it is expected that the trained model will not require calibration for each user (e.g., to determine P...). f or P d ).

[0107] Figure 10 Prototype Implementation Figure 10 An embodiment of a PPG signal acquisition system 10 according to an embodiment of the present invention is shown. The system 10 is shown as including a microprocessor board 1 (e.g., an UNO R3 board manufactured by Arduino Srl), which functions as a microprocessor to collect and transmit PPG signals from the PPG sensor assembly 7.

[0108] PPG sensor assembly 7 is shown as including a plate, PPG sensor 8, and PPG sensor 9. In an embodiment, each PPG sensor 8, 9 of the assembly has the following characteristics: a) Diameter = 0.625" (~16mm); b) Total thickness = 0.125" (~3mm); c) Cable length = 24" (~609mm) (or smaller, or can be cut to the desired length); d) Voltage = 3V to 5V; e) Current consumption at 5V = ~4mA; f) An ambient light sensor (e.g., APDS-9008 from Avago); and g) Green light source (e.g., AM2520ZGC09 from Kingbright).

[0109] However, it should be understood that the present invention can be performed using various PPG sensor assemblies, except where limited by any of the appended claims.

[0110] The microprocessor board 1 can be powered through jack 2 (e.g., a USB port). The sensor board 7 is shown receiving its power through connections 3 and 4 (e.g., a 5V pin and a ground pin). Optionally, a rechargeable battery (not shown) is arranged to be connected to the microprocessor board 1, and jack 2 can be used to charge the battery.

[0111] PPG sensors 8 and 9 are also shown connected to the microprocessor board at pins 5 and 6, respectively. PPG signals are sent to the board via these pins and converted by an analog-to-digital converter (ADC) on the microprocessor. In this embodiment, the ADC is capable of representing analog voltages using 1,024 digital levels. The ADC converts voltage readings into bits of information that the microprocessor can understand. The digitized information is transmitted to the onboard processor and memory, and optionally via jack or port 2 to a portable computing device or personal computer (i.e., a PC).

[0112] Figure 10 The implementation shown provides a convenient method for obtaining PPG data from two locations on the wrist. PPG signals from sensors 8 and 9 are transmitted to a processor and stored (e.g., as a CSV file). This data is then input into the algorithm module or hub described herein for calculating MAP and other vital signs.

[0113] Figure 11 Miniaturized implementation of PPG acquisition system Figure 11 This is an embodiment of a miniaturized PPG signal acquisition system 50, which includes an integrated chip sensor 60, a microcontroller unit 70, an algorithm hub 72, a memory 80, and a display 90.

[0114] Integrated chip sensors can operate in conjunction with the PPG sensors described herein to receive analog signals from each PPG sensor. Examples of suitable integrated chip sensors include, but are not limited to, analog front-end chip-type integrated sensors.

[0115] The preferred integrated chip for PPG data acquisition is the MAX86176 ECG & PPG analog front-end manufactured by Maxim Integrated in San Jose, California. It features: a) 2.728 mm 2.708mm wafer-level package; b) supports frame rates from 1fps to 2kfps; c) supports inputs to up to 6 LEDs and 4 photodiodes; d) high-resolution 20-bit charge-integrating ADC; and e) CMRR > 110dB at power line frequency. However, other small sensor acquisition systems or AFE-type chips can be used, which are operable for power supply, reception, filtering, and conversion of PPG signals into digital data for processing.

[0116] Figure 11 A microcontroller unit 70 is also shown, which can operate in conjunction with a custom algorithm hub 72 to evaluate PPG data collected from sensors and extract and compute features, and ultimately calculate desired BP and vital sign values.

[0117] The microcontroller 70 is also shown communicating with the memory 80 (e.g., flash memory) for reading, writing, and storing data and results.

[0118] Figure 11 A display 90 is also shown, on which various information (e.g., BP value) can be displayed.

[0119] Optionally, system 50 may include a wireless communication module to wirelessly transmit information to another device. System 50 may be equipped with, for example, Bluetooth technology to send information to portable computing devices such as smartphones, tablets, or computers.

[0120] Portable computing devices can be programmed using applications to operate with the PPG acquisition unit 50 to synchronize data and values, user information, and display user history and data.

[0121] Optionally, the system may include a remote or cloud server programmed and operable to communicate with a portable computing device via the internet, record all user data, and download new versions of the application algorithm and BP algorithm to the portable computing device. In an embodiment, the BP algorithm may be updated on the server (e.g., adjusting the aforementioned scaling factor or machine learning algorithm) based on the collection of more user BP data and user input (such as age, weight, height, calibrated cuff pressure, etc.). The updated BP algorithm can then be downloaded to the portable computing device, and finally to the wearable BP measurement device.

[0122] Figure 12This is a flowchart illustrating another method 1000 for calculating blood pressure based on PPG data.

[0123] Step 1010 describes the collection of pulsatility data. This step can be performed by activating a PPG sensor (e.g., PPG sensors 8 and 9 described above) placed in a watch or other type of wearable device to obtain simulated data on blood flow.

[0124] In some embodiments of the invention, PPG sensor data collection is customized to achieve a sampling rate (e.g., 2230 Hz) that is significantly larger than the default value (e.g., 500 Hz). The inventors have found that sampling rates greater than 1 kHz are important because Δt is small, where Δt refers to the time difference between the same pulses collected by the PPG sensor. Therefore, in order to obtain a sufficient number of data points, the sampling rate must be increased, as further described herein.

[0125] In embodiments, several steps are applied to customize or modify the sampling rate of the PPG sensor, including reprogramming the underlying code to the processor or microcontroller. In embodiments of the invention, the following steps are performed: 1) Reduce the sample counter time between reads (e.g., the MICROS_PER_READ variable) to increase the sampling rate. In an embodiment, we reduce the sample counter time to less than 1 millisecond, and in a preferred embodiment, we reduce the sample counter time to less than 0.5 milliseconds, and in one embodiment, we set the sample counter time to approximately 400 microseconds or 0.4 milliseconds, corresponding to a maximum value of 2500 Hz.

[0126] 2) Reprogram the processor (e.g., microcontroller) to accommodate the increased data due to the increased sampling rate. Interrupt Service Routine (ISR) is typically responsible for handling interrupt requests from hardware devices to the CPU. Given the increased sampling rate, we want to adjust the interrupt timer to avoid pausing sampling. For example, for a 500 Hz sampling rate, the default interrupt timer has a set count of 249; for a 2500 Hz sampling rate, we would change the set count from 249 to 49.

[0127] 3) Adjust the baud rate used for data transmission and display appropriately. If the baud rate is not adjusted or does not match the sampling rate, we will not be able to record every sampled data point, and data points may be lost.

[0128] Step 1020 is signal processing. In this embodiment, the PPG signal is filtered 1022 and amplified 1024 by the sensor board or AFE chip, and then converted into a digital signal to perform processing or preprocessing.

[0129] Step 1030 describes the extraction of pulse feature points. In an embodiment, this is achieved by targeting, for example, the above-mentioned combination Figure 7A , Figure 7B The described feature points are evaluated from the signal in step 1020 to perform this step. Examples of feature points include, but are not limited to, a1, a1′, b, b′, c, c′, a2, a2′, c, and d. This step can be performed algorithmically to identify the period and the valleys (minimum) and peaks (maximum) within each period. This step can be performed by a microprocessor in conjunction with the above. Figure 5 , Figure 11 The algorithm is executed by the central processing unit in the described PPG acquisition systems 50 and 100.

[0130] Step 1040 describes determining the scaling factor. In this embodiment, this step is performed by calibrating the blood pressure device to the measured blood pressure reading using a conventional BP measurement device, such as a conventional oscillometric compression cuff device. By inputting the individual's current blood pressure (MAP or DBP) reading from the compression blood pressure device, the scaling factor (P, respectively) can be derived using the equations listed above for MAP and DBP. f or P d This step can be omitted during continuous monitoring after the scaling factor has been initially determined.

[0131] Step 1050 describes the calculation of the blood pressure value. In an embodiment, this step is performed by calculating the MAP according to the equations described herein and based on the feature points and scaling factors determined in steps 1030 and 1040 above. This can be accomplished by a microprocessor in conjunction with the above-described steps. Figure 5 , Figure 11 The algorithm hub in the described PPG acquisition system 50, 100 performs this step. The algorithm hub or another storage device can store various algorithms used to determine different blood pressure values ​​and other vital signs.

[0132] Next, calculate other blood pressure values ​​(e.g., DBP and SBP) as described above.

[0133] Figure 13 This is a flowchart illustrating a method 1100 for calculating blood pressure based on PPG data.

[0134] Step 1110 describes performing data preprocessing on the PPG data. In this embodiment, the raw PPG data is analyzed in both the time and frequency domains, and a bandpass filter (e.g., a 4th-order Butterworth bandpass filter with a frequency range of (0.4Hz-8Hz)) is used to remove very low-frequency respiratory signals and baseline drift.

[0135] Step 1120 describes the extraction of reference points. As described above, this function marks characteristic points of the PPG signal, such as systolic peak, diastolic peak, and pulse onset. The derivative of the processed PPG signal is used in conjunction with defined conditions (e.g., examining data within a relatively small time window over a continuous long data collection period) to correctly extract characteristic points from each set of PPG data. In embodiments, PPG data is collected over a collection period lasting from 10 seconds to 2 minutes, more preferably from 30 seconds to 90 seconds, and in some embodiments, approximately 1 minute.

[0136] The time window for analyzing data within a relatively long data collection period can vary, and in some embodiments, it ranges from 1 second to 2 minutes or from 1 second to 10 seconds. Preferably, the time window varies with the collection period, such that the collection period can be divided into 5 to 20 (or more) time windows.

[0137] Step 1130 describes the evaluation of signal quality. In an embodiment, this step includes comparing the heartbeat morphology of each pulse with a reference pulse template calculated based on the input PPG data, and then calculating the cross-correlation result to determine the signal quality of each pulse.

[0138] As described above, the entire length of the collected data (e.g., a one-minute continuous PPG data collection) is initially divided into shorter windows (e.g., 10-second windows). We then identify the data within each window and which pulses are included in that window.

[0139] The second step is to obtain a reference pulse template from the window. First, we obtain all heartbeat-to-heartbeat intervals from the pulses in the window, where, in this embodiment, we define the heartbeat-to-heartbeat interval as the time difference between the points of maximum slope in adjacent pulses. Second, we obtain the heartbeat morphology for each individual pulse in the window, and we calculate a statistical value (e.g., average or median) for all heartbeat morphologies and set this statistical value as the reference pulse template. In a preferred embodiment, the median is used as the reference pulse template.

[0140] Next, we calculate the cross-correlation value between each pulse morphology and the reference pulse template. Any pulse with a cross-correlation value less than a threshold is considered a low-quality pulse and is discarded.

[0141] In addition, we assess the quality of feature point extraction by identifying which pulses have relatively poor feature point labeling. For example, in some pulses, the diastolic peak is missing or indistinguishable; we identify these imperfect pulses as low-quality pulses and discard them.

[0142] The output of signal quality assessment step 1130 is to identify low-quality pulses and discard them.

[0143] Step 1140 describes the performance of a preliminary or raw blood pressure calculation. This step uses the PPG model described above to convert the extracted PPG features into raw BP estimates.

[0144] In this embodiment, a scaling factor is applied to compensate for pressure loss in the arterial side of the circulation. In this embodiment, a scaling factor of 0.7 is applied to calculate the estimated BP, where BP... 估计 =BP 原始 / 0.7. However, in some embodiments, the pressure loss in the arterial side of the circulation is compensated based on the above-described BP estimation scaling factor.

[0145] The output of step 1140 is the BP matrix of the preliminary BP estimate.

[0146] In embodiments of the invention, the initial BP estimate is further processed. The inventors have found that further processing can be helpful due to the wide variability of the initial BP matrix. For example, the initial BP matrix may include approximately 70 heartbeat-to-heartbeat BP values ​​from 1-minute long PPG data. Heartbeat-to-heartbeat values ​​can fluctuate significantly. In some cases, BP values ​​can fluctuate between a minimum of approximately 10 mmHg and a maximum of approximately 2000 mmHg or greater. Therefore, in embodiments, instead of processing all raw heartbeat-to-heartbeat BP values ​​to generate the final BP estimate, a BP threshold range is specified to include only a portion of the raw BP values ​​for calculating the final BP estimate. Furthermore, some raw BP values ​​are excluded.

[0147] Step 1150 describes testing different calculation methods. In this embodiment, several different arithmetic methods were performed to improve the accuracy of the BP estimate. Examples of arithmetic methods include, but are not limited to: Total absolute error. The sum of each pair (BP estimate - BP reference), where the BP reference is measured simultaneously with the BP estimate using an arm cuff device (or another technique).

[0148] Confidence level. The number of BP estimates within a threshold error, wherein, in embodiments of the invention, the threshold error ranges from + / -5 mmHg to + / -10 mmHg, and in some embodiments, the threshold error ranges from + / -5 mmHg to + / -8 mmHg.

[0149] These methods are calculated using only the mean and median of the BP estimates.

[0150] Step 1160 describes the assessment of the accuracy of the calculation method. In this step, the accuracy of the calculation method being tested is inquired. In embodiments, accuracy is based on which BP range produces the minimum total absolute error and the highest confidence level. In embodiments, the defined threshold range (average) is less than 300 mmHg, and in some embodiments, the defined threshold range (average) is less than 200 mmHg, and in some embodiments, the defined threshold range (average) is between 30 mmHg and 190 mmHg.

[0151] Step 1170 states that the final blood pressure is calculated (i.e., recalculated) based on the selected calculation method (mean or median), the defined threshold range, and the omission of any low-quality pulses.

[0152] Optionally, determining pulse transit time-based blood pressure (PTT-BP) includes providing a PTT-BP linear regression equation; calculating the average PTT from the input data; and obtaining a blood pressure estimate based on the PTT and PTT-BP equations. In embodiments where a cuff is not used to determine the reference value described above in conjunction with step 1150, the PTT-BP value can be used as a reference BP to test different calculation methods.

[0153] Another function of calculating PTT-BP is to evaluate the accuracy of the aforementioned feature point extraction method. Because PTT-BP calculation uses feature points extracted using this method, accurate PTT-BP estimation results demonstrate the high accuracy of the feature point extraction method.

[0154] Furthermore, in the embodiments, once a reference value is obtained and used to determine the limiting threshold blood pressure range, the cuff can be removed from the person's arm, and BP monitoring can continue using the established limiting threshold range. Therefore, embodiments of the invention have the advantage that the cuff is removed from the person's arm once the BP limiting threshold range (and any other factors as described herein) is established during the initial setup or calibration phase. After the calibration phase, the cuff is removed, and the blood pressure device is operable to continuously calculate MAP, SBP, and DBP as described above.

[0155] Example According to an embodiment of the present invention, a test is performed to estimate a person's MAP.

[0156] Test setup description: As described above, two identical PPG sensors were placed on a person's left arm. The first sensor was placed at the wrist, and the second sensor was placed on the forearm, approximately 15cm away from the first sensor. As mentioned above, both sensors were connected to an Arduino board for signal acquisition. Additionally, an Omron BP monitor (reference device) was worn on the right arm to obtain a reference BP value for comparison.

[0157] Eight sets of one-minute data were collected using the test and reference equipment. An initial MAP matrix was calculated. Then, different methods described above were tested to determine a defined threshold range (average value) (30 mmHg to 190 mmHg in this embodiment) to filter out raw MAP estimates that were outside this range. The average MAP was then recalculated based on the filtered MAP matrix to produce the final MAP estimate.

[0158] The result is Figures 14-15 As shown in the image.

[0159] refer to Figure 14 The calculated MAP (Cal_MAP) is compared with the reference MAP value (Ref_MAP) from the Omron BP monitor, with the difference (Abs_Error).

[0160] refer to Figure 15 The Bland-Altman plot illustrates the accuracy of PPG-BP, where "O" scatter points represent the difference between the MAP calculated according to the PTT model and the reference BP reading from the Omron BP monitor. "X" scatter points are comparisons between the MAP calculated according to our PPG-BP model and the reference BP reading from the Omron BP monitor.

[0161] Based on this dataset, the current accuracy for the PPG-BP testing equipment is calculated to be approximately 6 ± 10 mmHg. The above results demonstrate the effectiveness of the PPG-BP testing equipment for MAP estimation according to embodiments of the present invention. Although combined with... Figures 14-15 The results illustrate specific implementations, and the invention is not intended to be limited thereto. In fact, other implementations and steps may be included in the invention in any logical combination or order, unless excluded by any appended claims.

[0162] Optional embodiments Although the device is described as being placed on the wrist, it can be configured in other ways. The device can be configured to read blood velocity data from another part of the body where arteries are located near the skin surface. In one embodiment, the device is placed on capillaries near the patient's skin surface and PPG signals and calculations are performed as described herein, without needing to query the arteries. Examples of other configurations include, but are not limited to, handheld probes (with or without umbilical cords for electronic wiring), patches (optionally with adhesive), clips (e.g., for the ear), loops, and bands whether around the chest, waist, thigh, or another area.

[0163] In addition, it should be understood that data, program updates and other communications can be transmitted between BP monitoring devices, portable computing devices and local area networks or remote servers or the cloud.

[0164] It should also be understood that, in embodiments, the BP monitoring device may be operable to be controlled by a remote device, such as a tablet, smartphone, or laptop computer.

[0165] In other embodiments, additional types of sensors are combined with or replace one or more sensors. For example, see reference... Figure 2 One or more of the paired 110, 112 PPG probes can be replaced by Doppler or light emitters and detectors. Preferably, the sensors are self-contained / independent and include their own processing electronics to provide signals to the CPU. However, in embodiments, less complex emitters and detectors, along with a camera, can be integrated into the device, and raw data is sent to the processor for preprocessing and evaluation. In an embodiment, a Doppler probe is combined with a PPG sensor. Ultrasonic energy from the Doppler probe is used to generate temporary distortion in a PPG waveform. By then examining when each PPG sensor detects the ultrasonic distortion waveform, multiple PPG sensors can better determine the number of pulse waves transmitted between a first PPG sensor and a second PPG sensor spaced apart by a distance (X).

[0166] In the embodiments, the device includes multiple operating modes, including but not limited to positioning mode, calibration mode and / or monitoring mode.

[0167] In an embodiment, a vascular positioning mode or module is operable to alert the user to maintain the device in an optimal position on the skin. This positioning mode (as opposed to the blood pressure monitoring mode described above) can be activated by the user to begin transferring energy into the skin. In positioning mode, the energy transmitter transfers energy into the skin, and the electronics send processed data to the main processor for evaluation. In an embodiment, the processor can operate during positioning mode to alert the user to the optimal position (e.g., via sound, vibration, or a visual indicator) as the user moves the device (whether wearable or handheld) along the skin. The user can scroll back and forth along the skin area to search for the optimal position. The audio indicator can be operable to increase volume or tone as the measured blood velocity increases. Similarly, the device can be operable to provide visual feedback (e.g., color or brightness of light) or tactile feedback (e.g., vibration generated by a small electromechanical actuator or motor) corresponding to changes in velocity along the skin position. Once the user is satisfied with the position, the user fastens or holds the device in place and activates the blood pressure monitoring mode.

[0168] In this embodiment, the calibration mode prompts the user for a blood pressure reading (or another blood pressure-related parameter, such as stroke volume) obtained through alternative means (e.g., oscillometric compression cuff device, catheter, etc.). The reading is then obtained by the device itself (e.g., device 100, and assuming P...). f The placeholder / estimated value is equivalent to the actual reading measured by an alternative device (e.g., an oscilloscope compression cuff) and the scaling factor P described herein is solved. f The equation is used to automatically calculate the user's scaling factor. In a preferred embodiment, the calibration mode prompts the user to repeat the calibration several times until the scaling factor becomes constant.

[0169] In one embodiment, a monitoring mode can be performed after the positioning and calibration modes.

[0170] While several embodiments have been disclosed above, it should be understood that other modifications and changes can be made to the disclosed embodiments without departing from the present invention. In fact, any components described herein can be combined with each other unless these components are exclusive. Any steps described herein can be combined in any combination and order unless these steps are exclusive.

Claims

1. A blood pressure monitoring device for calculating mean arterial pressure of a user, comprising: a housing and a band adapted to hold the housing against a patient's wrist; a first PPG sensor within the housing and aligned with an artery in the wrist when the housing is tied to the wrist; a second PPG sensor within the housing spaced apart from the first PPG sensor and aligned with an artery in the wrist when the housing is tied to the wrist; and a processor arranged within the housing and operable to: calculate a plurality of features from PPG waveform data generated by the PPG sensors; and calculate the mean arterial pressure, MAP, based on the plurality of features. The calculation of the MAP is further based on a predetermined scaling factor associated with the user, wherein the scaling factor is calculated initially and based on measuring blood pressure using a second type of blood pressure measuring device.

2. The blood pressure monitoring device of claim 1, wherein, One of the plurality of features comprises diastolic velocity, systolic velocity, systolic volume, diastolic volume, diastolic distance, systolic distance, heart rate, diastolic time, and / or systolic time.

3. The blood pressure monitoring device of claim 1, wherein, The processor is further operable to calculate diastolic blood pressure, DBP.

4. The blood pressure monitoring device of claim 1, wherein, The processor is further operable to calculate systolic blood pressure based on the calculated MAP and DBP.

5. The blood pressure monitoring device of claim 1, wherein, The housing further houses a battery, a memory, and PPG electronics.

6. The blood pressure monitoring device of claim 1, wherein, 7. The blood pressure monitoring device of claim 1, further comprising a trained machine learning model for determining the MAP based on the plurality of features extracted from the PPG waveform data.

8. The blood pressure monitoring device of claim 1, further comprising a display, and wherein the band, the housing, and the display collectively form a watch-like shape.

9. The blood pressure monitoring device of claim 1, further comprising a positioning module for alerting a user of an optimal position on the wrist to tie the housing to the optimal position when the user adjusts the position of the housing along the user's wrist. The calculation of the MAP is performed without using ECG data.

10. The blood pressure monitoring device of claim 1, wherein, 11. A blood pressure monitoring system for calculating mean arterial pressure of a user, comprising: a housing and a window adapted to hold against a patient's skin; at least one sensor modality arranged within the housing; and a processor arranged operable to: calculate a plurality of features from data generated by the sensor modality; and calculate the mean arterial pressure, MAP, based on the plurality of features. The at least one sensor modality comprises a PPG sensor modality comprising a first PPG sensor and a second PPG sensor aligned through the window towards an artery.

12. The blood pressure monitoring system of claim 11, wherein, The calculation of the MAP is performed without using ECG data.

13. The blood pressure monitoring system of claim 12, wherein, One of the plurality of features comprises diastolic velocity, systolic velocity, systolic volume, diastolic volume, diastolic distance, systolic distance, heart rate, diastolic time, and / or systolic time.

14. The blood pressure monitoring system of claim 11, wherein, ​ 15. The blood pressure monitoring system of claim 11, wherein, The calculation of the MAP is also based on a predetermined scaling factor associated with the user, wherein the scaling factor is calculated initially and based on a measurement of blood pressure using a second type of blood pressure measurement device.

16. A device, method or system for calculating MAP, and wherein the calculation of the MAP is based on peripheral PPG data obtained from a plurality of PPG sensors arranged to read only one peripheral region of the skin, and optionally wherein the region of the skin is a segment of skin along the outside of the wrist.

17. A method for monitoring mean arterial pressure (MAP) of a person based on PPG data, comprising: arranging a first PPG sensor and a second PPG sensor on the skin of the person, optionally on the wrist, wherein the second PPG sensor is spaced apart from the first PPG sensor by a fixed distance; obtaining PPG data corresponding to blood flow in blood vessels of the person from the first PPG sensor and the second PPG sensor; extracting a plurality of features from the PPG data; and calculating the mean arterial pressure (MAP) based on the plurality of features.

18. The method of claim 17, further comprising transmitting the MAP to a portable computing device.

19. The method of claim 17, wherein, The calculation of the MAP is performed without using ECG data.

20. The method of claim 17, further comprising calculating diastolic blood pressure (DBP).

21. The method of claim 17, further comprising calculating systolic blood pressure.

22. The method of claim 17, further comprising alerting a user regarding an optimal position of the positioning of the first PPG sensor and the second PPG sensor as the user adjusts and moves the first PPG sensor and the second PPG sensor along the skin of the person.

23. The method of claim 17, wherein, One of the plurality of features comprises diastolic velocity, systolic velocity, systolic volume, diastolic volume, diastolic distance, systolic distance, heart rate, diastolic time and / or systolic time.

24. The method of claim 17, wherein, The step of calculating MAP is performed using a BP algorithm, and wherein the BP algorithm is calibrated using an auxiliary BP measurement device, optionally a pressure cuff type device.

25. The method of claim 24, wherein, The calculation of MAP is performed without compression after calibration.

26. The method of claim 24, wherein, The calculation of MAP is performed continuously after calibration.

27. The blood pressure monitoring system of claim 11, further comprising an adhesive to adhere the housing to the skin.

28. The blood pressure monitoring system of claim 11, further comprising a umbilical cord line extending from the housing, the umbilical cord line for transmitting information to and from the housing to another device, and optionally for transmitting power from another device to the housing.

29. The blood pressure monitoring system of claim 12, wherein, The data generated by the sensor modalities is velocity and / or volume flow data of the patient.

30. The blood pressure monitoring system of claim 29, wherein, The features are calculated from the flow data.

31. The blood pressure monitoring system of claim 30, wherein, The MAP is calculated without using a database, atlas or general population type information.

32. The blood pressure monitoring system of claim 12, wherein, All sensors are aligned to the same anatomical body part and are spaced apart from each other by less than 100 mm, and optionally less than about 50 mm.

33. The method of claim 17, further comprising evaluating signal quality after the extracting step, wherein evaluating signal quality comprises: computing a reference template, comparing the beat morphology of each pulse to the reference template, identifying low quality features based on the comparing step, and excluding the low quality features from the plurality of features used in the MAP computing step.

34. The method of claim 33, further comprising computing a defined threshold BP range after the MAP computing step, and recomputing the MAP based on the defined threshold BP range.

35. The method of claim 34, wherein, determining the defined threshold BP range based on computing a BP error and a confidence level.

36. The blood pressure monitoring device of claim 1, wherein, The processor is further programmed to evaluate signal quality after computing the plurality of features, wherein evaluating signal quality comprises computing a reference template, comparing the beat morphology of each pulse to the reference template, identifying low quality features based on the comparing step, and excluding the low quality features from the plurality of features used in the MAP computing step.

37. The blood pressure monitoring device of claim 36, wherein, The processor is further programmed to compute a defined threshold BP range after the MAP computing step, and recomputing the MAP based on the defined threshold BP range.

38. The blood pressure monitoring device of claim 37, wherein, The processor is further programmed to determine the defined threshold BP range based on computing a BP error and a confidence level.

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