Photoplethysmography-based blood pressure monitoring device

Abstract

The blood pressure monitoring device comprises a wrist strap, a case, and a display. The strap portion is configured to secure the device to the wrist without obstructing blood flow. One or more sensor modalities are positioned within the case to acquire sensor data from the arteries of the wrist. In a preferred embodiment, a photoplethysmography sensor is incorporated into the case to generate PPG waveform data. A processor within the case is operable to calculate mean arterial pressure, and optionally diastolic and systolic blood pressure, based on the extraction and calculation of sensor data features. Related methods and systems are also described.

Description

[Technical Field]

[0001] This invention relates to blood pressure measurement, and more particularly to the non-invasive, non-pressurized measurement of mean arterial pressure. [Background technology]

[0002] Mean arterial pressure (MAP) is the average blood pressure of an individual during one cardiac cycle. MAP is thought to be the perfusion pressure experienced by the body's organs. If MAP remains low for a significant period of time, vital organs may not receive sufficient oxygen.

[0003] MAP can be measured directly by invasive monitoring, for example, using an intravascular pressure transducer. However, intravascular devices can cause problems such as embolism, nerve damage, infection, bleeding, and / or damage to the vessel wall. In addition, implantation of intravascular leads requires highly skilled physicians such as surgeons, electrophysiologists, or interventional cardiologists.

[0004] In addition, at a normal resting heart rate, MAP can be approximated by measuring systolic blood pressure (SBP) and diastolic blood pressure (DBP), doubling the lower (diastolic) blood pressure and adding it to the higher (systolic) blood pressure, and then dividing the combined sum by 3. That is,

[0005]

number

[0006] SBP and DBP can be measured using conventional blood pressure cuff devices. However, such devices are undesirable because they occlude the blood vessels. In addition, due to the occluding nature of these types of devices, they cannot be worn for any length of time. Therefore, cuff-based devices are not adequate for continuous blood pressure monitoring.

[0007] Attempts have been made to measure blood pressure without a cuff by using pulse arrival time (PAT) and pulse wave propagation time (PTT). Both PAT and PTT measure the time delay of the pulse emitted from the heart to the finger and have been shown to correlate with both systolic and diastolic blood pressure. See, for example, Non-Patent Document 1. Also see Patent Document 1 for Banet.

[0008] Pulse wave arrival time (PAT) and pulse wave propagation time (PTT) are typically measured with conventional vital signs monitors that include separate modules for determining both electrocardiogram (ECG) and pulse oximetry, i.e., oxygen saturation (SpO2) values. To obtain ECG values, multiple electrodes are typically attached to the patient's chest to determine the time-dependent component of the ECG waveform, characterized by a sharp spike called the "QRS complex." The QRS complex indicates the initial ventricular depolarization within the heart and informally represents the onset of the heartbeat and the subsequent pressure pulse.

[0009] To obtain SpO2, a bandage or clothespin-shaped sensor is attached to the patient's finger, and the sensor includes an optical system that operates in a spectral region specific to detecting and quantifying the amount of hemoglobin in the underlying artery. The optical module typically includes first and second light sources (e.g., light-emitting diodes or LEDs) that transmit optical radiation at red wavelengths (λ-600-700 nm) and infrared wavelengths (λ-800-1200 nm), respectively.

[0010] The photodetector measures the radiation emitted from the optical system as it passes through the patient's finger. Other body parts (e.g., 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 called a PPG (photoplethysmogram waveform)). For each heartbeat, the PPG shows the change in arterial blood volume based on the amount of radiation absorbed along the optical path between the LED and the photodetector. It is possible to calculate the SpO2 value from the PPG waveform. Time-dependent features of the PPG waveform show both the pulse rate and the volumetric absorbance change in the underlying artery (e.g., in the finger) (caused by the propagating pressure pulse).

[0011] A typical PAT measurement determines the time it takes to separate the maximum point in the QRS complex (indicating the peak of ventricular depolarization) from the portion of the PPG waveform (indicating the arrival of the pressure pulse). PAT depends primarily on arterial compliance, the distance the pressure pulse propagates (which is roughly approximated by the patient's arm length), and blood pressure. To account for patient-specific characteristics such as arterial compliance, PAT-based blood pressure measurements are typically "calibrated" using a conventional blood pressure cuff. Typically, during the calibration process, the blood pressure cuff is applied to the patient, used to take one or more blood pressure measurements, and then removed. Thereafter, the calibrated measurements, along with changes in PAT, are used to determine the patient's blood pressure and blood pressure variability. PAT is typically inversely proportional to blood pressure; i.e., a decrease in PAT indicates an increase in blood pressure.

[0012] The aforementioned system has several drawbacks, including, for example, the need for electrodes and sensors placed across multiple different locations on the patient; the need to use two different types of devices (i.e., an ECG electrode set and reader, and a pulse oximetry device); the increased risk of waveform detection errors resulting from the need for ECG; and the requirement for a finger clip, which is inconvenient to wear for extended periods.

[0013] In view of the above, a blood pressure monitoring device with higher reliability, robustness, and convenience is desired.

Prior Art Documents

Patent Documents

[0014]

Patent Document 1

Non-Patent Documents

[0015]

Non-Patent Document 1

Brief Description of Drawings

[0016] [Figure 1] Front-side perspective view of a blood pressure monitoring device according to an embodiment of the present invention. [Figure 2] Enlarged rear view of a part of the blood pressure monitoring device shown in FIG. 1. [Figure 3] Enlarged front view of a part of the blood pressure monitoring device shown in FIG. 1. [Figure 4] Front view of a blood pressure monitoring device arranged on the left wrist according to an embodiment of the present invention. [Figure 5] Block diagram of a blood pressure monitoring device according to an embodiment of the present invention. [Figure 6] Schematic diagram of another blood pressure monitoring device according to an embodiment of the present invention. [Figure 7A]A diagram showing an exemplary PPG waveform recorded by the blood pressure monitoring device shown in FIG. 12. [Figure 7B] A diagram showing an exemplary PPG waveform recorded by the blood pressure monitoring device shown in FIG. 12. [Figure 8] A flowchart showing an overview of a method for calculating blood pressure based on PPG information according to an embodiment of the present invention. [Figure 9] A flowchart showing another method for calculating blood pressure based on PPG information according to an embodiment of the present invention. [Figure 10] A block diagram of a PPG system including a microcontroller and a sensor according to an embodiment of the present invention. [Figure 11] A block diagram of another PPG data acquisition system including a microcomputer and an electronic device according to an embodiment of the present invention. [Figure 12] A flowchart showing another method for calculating blood pressure based on PPG information according to an embodiment of the present invention. [Figure 13] A flowchart showing another method for calculating blood pressure based on PPG information according to an embodiment of the present invention. [Figure 14] A diagram showing the calculated blood pressure data set in tabular form. [Figure 15] A diagram showing the calculated blood pressure data set in graphical form.

Mode for Carrying Out the Invention

[0017] Summary of the Invention A blood pressure monitoring device for calculating a user's mean arterial pressure comprises a case and a strap configured to hold the case against the patient's wrist. Sensors are located within the case and directed towards the capillaries of the wrist when the case is secured to the wrist with the strap. A processor is located within the case and is operable to calculate a set of features from the data generated by the sensors and to calculate the mean arterial pressure (MAP) based on the set of features.

[0018] In one embodiment, a method for monitoring a human mean arterial pressure (MAP) comprises the steps of: activating two or more PPG probes directed at the capillaries of the human wrist to generate velocity data; and automatically calculating a plurality of features and the user's MAP based on the plurality of features from the PPG velocity data on a processor.

[0019] Optionally, SBP and DBP are calculated based on multiple features. In an embodiment, the method further comprises an evaluation step for evaluating the signal quality, the evaluation step including a step of calculating a reference template, a comparison step of comparing the beat morphology of each pulse with the reference template, a step of identifying low-quality features based on the comparison step, and a step of excluding the low-quality features from the plurality of features used in the step of calculating the MAP.

[0020] In one embodiment, the method further comprises the steps of calculating a limit threshold MAP range after the step of calculating the MAP, and recalculating the MAP based on the limit threshold MAP range.

[0021] In the embodiment, the limit threshold MAP range is determined based on a step of calculating the BP error and confidence level using the mean and median values ​​as optional. In this embodiment, the display on the case presents blood pressure information to the user.

[0022] In one embodiment, a blood pressure monitoring system for calculating a user's mean arterial pressure comprises a case and window configured to be held against the patient's skin, two or more PPG sensors disposed within the case, and a processor. The processor is configured and operable to calculate a plurality of features from velocity data generated from two or more of the PPG probes, and to calculate the mean arterial pressure (MAP) based on the plurality of features.

[0023] In one embodiment, the optical emitter directs light toward the artery through a window. In another embodiment, the optical emitter is integrated into the PPG probe. In yet another embodiment, the optical emitter (separate from the PPG probe) is located in a case and directs light toward the artery through a window. The PPG velocity data is at least partially based on the absorption of light by the artery and the blood flow through it.

[0024] In the embodiment, the multiple feature quantities include viscosity, heart rate, and blood oxygenation. In the embodiment, the plurality of feature quantities include diastolic velocity, systolic velocity, systolic volume, diastolic volume, diastolic distance, systolic distance, heart rate, diastolic time, and / or systolic time.

[0025] In the embodiment, the processor is further 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.

[0026] In one embodiment, the blood pressure monitoring system further comprises a trained model for determining the MAP based on a set of features extracted from velocity data. In the embodiment, the blood pressure monitoring system further comprises a console, the processor being enclosed within the console. The case, window, and two or more PPG probes can be integrated as a single handheld tool connected to the console by 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 for adhering the patch to the skin. In some embodiments, the sensors are directed to different locations within the same anatomical part of the body. In some embodiments, the target locations are within 110 mm of each other, or between 40 mm and 60 mm of each other. In some embodiments, the target locations are within 50 mm of each other, more preferably within 35 mm of each other. Examples of distinct anatomical parts of the body to which all sensors are directed include fingers, wrists, upper arms, thighs, chest, neck, and ears.

[0028] In the embodiment, one sensor is directed towards a blood vessel near the wrist, and another sensor is directed towards a blood vessel along the forearm. The sensors may be spaced 10-20 cm apart from each other, or in some embodiments, about 10-15 cm apart.

[0029] In the embodiment, the sensor does not simultaneously measure data from different anatomical parts of the body. For example, in the embodiment, the system's sensor is not directed at both the chest and the wrist at the same time. In the embodiment, the BP monitoring system's sensor is directed simply at one anatomical body part or another anatomical body part and detects volumetric flow data from only one body part.

[0030] In the embodiment, the processor prompts the user for actual blood pressure-related readings (e.g., oscillometric pressurized cuff) and calculates a patient-specific proportional factor (e.g., P) based on the actual blood pressure-related readings. f It is possible to operate to calculate ) and in that case, MAP is based on a patient-specific proportional factor.

[0031] In some embodiments, the data generated from the sensor modality is velocity data and volumetric flow 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 data (or features extracted or calculated therefrom). In some embodiments, the BP value is calculated based on the patient's volumetric flow data and without using a BP database to correlate pressure with sensor data.

[0032] While not intended to be theoretically binding, calculating BP values ​​based on the patient's volumetric flow rate data itself is more accurate than using a database to match BP values ​​to PPG signals, due to potential errors that may occur when generating the database. These potential errors can arise from differences in human, hardware, and software between the user and the hospital. Considering the time scale, even small differences in time can significantly impact BP value calculations. Therefore, in some embodiments of the present invention, a database of BP values ​​(associated with sensor signals) is avoided.

[0033] Modes for carrying out the invention Embodiments of the present invention allow for the determination of pressure values ​​without measuring height or height changes.

[0034] Embodiments of the present invention allow for the determination of pressure values ​​without measuring expansion or expansion changes. Embodiments of the present invention allow for the determination of pressure values ​​without measuring atmospheric pressure or changes in atmospheric pressure.

[0035] Embodiments of the present invention enable the determination of pressure values ​​in multiple anatomical regions without measuring information. Embodiments of the present invention allow for the determination of pressure values ​​without using ECG data.

[0036] Embodiments of the present invention allow for continuous monitoring of BP pressure values ​​without the need for pressurization. Embodiments of the present invention enable the determination of pressure values ​​based on photoplethysmography velocity data generated from a non-invasive wearable bracelet-like device.

[0037] Further descriptions, objectives, and advantages of the present invention may become apparent from the following detailed description, along with the accompanying drawings. Before describing the present invention in detail, it should be understood that the present invention is not limited to the specific variations described herein, and that various changes or modifications can be made to the described invention and that they can be replaced with equivalents without departing from the spirit and scope of the invention. As will be apparent to those skilled in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has distinct components and features that can be readily separated from or readily combined with features of any of several other embodiments without departing from the scope or spirit of the invention. In addition, many modifications can be made to adapt a particular situation, material, composition, process, process action, or step to the object, spirit, or scope of the invention. All such modifications are intended to be within the scope of the claims described herein.

[0038] The methods described herein may be carried out in any logically possible order of the described events, as well as in the order in which the events are described. Furthermore, if a range of values ​​is provided, it is understood that all intervening values ​​between the upper and lower limits of that range, and any other defined or intervening values ​​within that defined range, are encompassed within the present invention. It is also assumed that any optional feature of the described modifications of the present invention may be described and claimed independently or in combination with any one or more of the features described herein.

[0039] All existing subject matter referenced herein (e.g., publications, patents, patent applications, and hardware) is incorporated herein by reference in its entirety, except where such subject matter may conflict with the subject matter of the present invention (in which case the material present herein shall prevail).

[0040] U.S. Patent Application Publication No. 20220225885, filed on 28 December 2021, entitled “Non-Invasive Non-Compressive Blood Pressure Monitoring Device,” is incorporated herein by reference in its entirety for all purposes.

[0041] A singular reference to an item includes the possibility of multiple identical items existing. More specifically, as used herein and in the appended claims, the singular forms “a, an,” “said,” and “the” include multiple references unless the context explicitly indicates otherwise. Furthermore, the claims may be drafted to exclude optional elements. This statement is therefore intended to serve as prior art for the use of exclusive terms such as “solely,” “only,” or for the use of “negative” limitations in relation to the description of elements of the claims.

[0042] Overview Figure 1 shows a blood pressure monitoring device 100 according to one embodiment of the present invention. The blood pressure monitoring device 100 has a strap 120, a buckle 124, and a display 140 located on a case 142. The strap and case are sized and operable to fit snugly to a human wrist (not shown) without interfering with the user's vascular structure by compressing, occluding, or expanding it.

[0043] Referring to Figure 2, the rear of case 142 shows a plurality of sensors, or probe pairs 110, 112, 114, for acquiring blood flow information from which blood pressure values ​​are automatically calculated, as further described herein. In embodiments, a thin window or protective layer is disposed over the sensors on the rear surface of the case. The window may be made of a material through which sound waves and / or electromagnetic waves can pass.

[0044] Referring to Figure 3, the display 140 is operable to show 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 case to control the device for performing various functions, as further described herein, such as calibration mode, localization (or sensor position adjustment) mode, and / or monitoring mode.

[0045] Figure 4 shows a blood pressure monitoring device 100, according to one embodiment of the present invention, fixed to the user's left wrist (LW). When fixed, the rear side of the case is positioned flat against the outside of the left wrist.

[0046] In embodiments, the device includes a positioning mode or module that can operate to select which sensor or sensor pair should be used for a blood pressure monitoring mode. In embodiments, the device is programmed to automatically evaluate which sensor or sensor combination is best based on which sensor or sensor combination shows the greatest signal pickup. Optionally (e.g., if signal perception is insufficient), as further described herein, the device may prompt the user to move / adjust the device's position along the user's skin until an optimal signal is detected. Thus, the positioning mode can provide an optimal sensor combination for each location, as well as an optimal location considering each of the sensor combinations available to the device.

[0047] In addition, in the embodiment, during blood pressure monitoring mode, the device is operable to automatically and periodically check each sensor for signal strength and select the combination of sensors with the maximum signal. This step serves to continuously ensure that the optimal sensors are used for blood pressure monitoring.

[0048] System Architecture Figure 5 is a block diagram of a blood pressure monitoring device 100 according to one embodiment of the present invention. The device comprises a plurality of PPG sensors 110, 112, a PPG electronics 192, and a main printed circuit board 150 supporting a CPU and memory. As further described herein, the CPU, memory, and electronics are operable to control the probes and to evaluate the PPG signals generated from the probes. Preferably, 2 to 10 PPG probes are arranged in a case so that some of the probes are close to the artery being examined when the device is fixed to the user's wrist. In an embodiment, the case holds 2 to 6 PPG sensors arranged as shown in Figure 2.

[0049] Figure 5 also shows a power supply (preferably a rechargeable battery) 130, an output unit (e.g., a display 140 or a speaker 162), a communication interface 160 (preferably a wireless short-range communication module such as Bluetooth®), a port 132 for charging the battery via a charging cable (such as a USB charging cable), transmitting data to and from the device, or both, and an input unit 170 (e.g., a button or touchscreen), all of which communicate with each other.

[0050] Preferably, port 132 has a low-profile design configured to connect to a standard charging cable interface (e.g., a 2-pin magnetic, clip-type, or USB-C type connector).

[0051] The memory stores data, information, and computer programs that include instructions for the CPU or other components of the blood pressure monitoring device 100. The types of information stored may vary and may include, but are not limited to, raw sensor signal data, 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 memory (e.g., RAM) and non-volatile memory types. In embodiments, the system includes a flash memory device for storing and recording new data. In practice, the present invention is intended to include a wide range of memory, processor, and circuit framework types, unless specifically excluded by any appended claims.

[0052] Device 100 is operable to alert the user based on an evaluation of information. If the information is outside a predetermined range, the device alerts the user. Examples of alerts include an audible alarm via the audio component 162, a visual graphic displayed on the display 140, and a text or email sent to the user or hospital care. Examples of types of information that may generate an alert when outside a predetermined range include, but are not limited to, battery or power levels, vital values, MAP values, SBP values, or DBP values.

[0053] Optionally, in the embodiments, information is transmitted to portable computing devices such as smartphones, tablets, and laptop computers. In addition, in the embodiments, a server (local or remote) is programmed and operational to communicate with the portable computing devices. The data is recorded, stored, evaluated, and collected by the server for backup and storage, and may also be used to update or train BP models. Updated firmware, software, algorithms, models, and apps may also be downloaded from the server to remote devices and then to the PPG BP monitoring system described herein.

[0054] Use of photoplethysmography (PPG) to determine the map. In embodiments of the present invention, PPG information obtained from a PPG sensor is used to determine the MAP.

[0055] Referring to Figures 6-7B, a PPG system 700 for deriving a MAP and exemplary recorded PPG waveforms 800, 800' are shown. A PPG system 700 is shown having a first PPG sensor 710 and a second PPG sensor 712 positioned to cover one or more capillaries in the arm of a patient or user. The first PPG sensor 710 is positioned at a fixed distance (X) from the second PPG sensor 712. Each PPG sensor is operable to send and receive light several millimeters into the patient's arm. The PPG sensors can be embodied in case 730 as described above in relation to Figure 4. The PPG sensors can be positioned alongside other sensors (e.g., Doppler, photoacoustic, etc.). In embodiments, the system comprises combinations of different types of sensors. An exemplary PPG sensor is the Valencell BW 4.0 manufactured by Valencell Corporation (Raleigh, North Carolina, NC).

[0056] In the embodiment shown in Figure 6, the system 700 comprises a PPG electronic device 740. The PPG electronic device 740 (optionally in the form of one or more PCBs) may include one or more processors, memory and storage devices, an AD converter, a communication module (e.g., for hardwired or wireless), and a power supply or interface connection.

[0057] During operation, the PPG sensor can operate with the PPG electronic equipment to generate and record PPG waves as blood passes through a blood vessel (e.g., RA or other arterioles), as shown in Figures 7A and 7B. As further described herein, the device can operate to extract and calculate several feature quantities from the blood pulse wave as the blood pulse wave passes through each of the PPG sensors 710 and 712.

[0058] Overview of PPG sensor methods Referring to Figure 8, an overview of method 850 for calculating blood pressure according to one embodiment of the present invention is shown.

[0059] Step 860 describes the process of activating the PPG sensor. This step can be performed by a user to activate (i.e., turn on) the PPG sensors 710, 712 shown in Figure 6. When the sensor is materialized in case 100 as shown in Figure 1, the user may activate the sensor by toggling button 170.

[0060] In the embodiments, the system is programmed to activate the sensor continuously or periodically. In the embodiments, a BP app stored on the user's portable computing device is operable to create a BP monitoring schedule that controls when the sensor is activated and / or when it is activated. Unlike pressurized BP monitors, embodiments of the present invention are capable of activating and monitoring the BP continuously in real time (e.g., every 60 seconds or less, or more preferably every 30 seconds or less).

[0061] Step 870 describes the process of calculating PPG wave features based on data generated by the PPG sensor. This process can be performed by sending the data generated by the PPG sensor and PPG electronics to a processor that can be operated to extract and calculate various features from the PPG waveform (e.g., waveforms 800 and 800' shown in Figures 7A and 7B, respectively). Waveform 800 is an example of data generated from a first PPG sensor 710 directed towards a capillary artery 702. A second PPG sensor, fixedly positioned at a distance from the first sensor within case 100, can be operated to generate a second waveform (800'). The processor can be operated to extract and calculate various features from the first and second waveforms 800, 800', and to calculate features that characterize the changes between the two waveforms. Examples of extracted features include, but are not limited to, the onset of the pulse wave, the systolic peak, the diastolic peak, the end of the pulse wave, and the time at which each feature occurs. In one embodiment, given a PPG waveform input, the processor can operate to automatically detect these features based on the assumption, for example, that the highest and second highest amplitudes are the systolic and diastolic peaks, respectively, and that the end / beginning of the pulse is where the amplitude is minimum. In yet another embodiment, a trained model is applied by the processor to extract these features based on the input waveform.

[0062] As described above, several features are automatically calculated based on the extracted or detected features. Examples of calculated features include, but are not limited to, heart rate, systolic time and diastolic time, systolic velocity and diastolic velocity, systolic volume and diastolic volume, and systolic distance and diastolic distance, as will be further explained below in relation to Figure 9.

[0063] Step 880 describes the process of calculating mean arterial pressure (MAP) based on the calculated wave characteristics. This process can be executed by a processor according to computer-readable instructions stored in memory, as will be described in detail below with reference to Figure 9.

[0064] Step 890 represents the process of calculating diastolic blood pressure (DBP). This process can also be executed by a processor according to computer-readable instructions stored in memory, as will be described in detail below with reference to Figure 9.

[0065] Step 892 describes the process of calculating systolic blood pressure (SBP). This process can also be executed by a processor according to computer-readable instructions stored in memory, as will be described in detail below with reference to Figure 9.

[0066] Referring to Figure 9, a detailed method 900 for calculating blood pressure based on PPG information generated from a PPG sensor (e.g., PPG sensors 710, 712 shown in Figure 6) is shown according to one embodiment of the present invention.

[0067] Step 910 describes the process of detecting features from PPG sensor data. In this embodiment, the first PPG sensor 710 records a PPG waveform 800. The PPG waveform 800 exhibits various wave features, including: pulse wave start (a1), pulse wave systolic peak (b), pulse wave diastolic peak (c), and pulse wave end / start (a2). The processor can be operated to automatically detect the various features as described above and to record the value and time of each feature.

[0068] Similarly, as shown in Figure 6, a second PPG sensor 712, fixedly positioned at a distance (X) from the first PPG sensor 710, records the corresponding PPG waveform (800'). From the second waveform, the device identifies corresponding features, including: the pulse wave start (a1'), the pulse wave systolic peak (b'), the pulse wave diastolic peak (c'), and the pulse wave end / start (a2'). The processor can be operated to automatically detect various features and record the value and time of each feature, as described above. For example, the pulse wave start point may be defined by the point of maximum gradient between adjacent pulses.

[0069] Step 930 describes the process of calculating the systolic and diastolic velocities. This process is performed by a processor, where the velocities are equal to distance divided by time. The distance between the sensors is fixed and equal to X (see Figure 6). In the embodiment, X is in the range of 1 mm–20 mm, more preferably 15 mm–35 mm, and optionally 25 mm–50 mm.

[0070] In addition, the time it takes for the pulse wave systolic peak to move between sensors can be calculated from the recorded PPG waveform and is equal to (b'-b). In embodiments, the time (b'-b) is in the range of approximately 5ms-40ms, more preferably 10ms-20ms. Therefore, Systolic velocity = x / (b'-b) Similarly, we calculate the diastolic velocity when the time it takes for the diastolic peak of the pulse wave to move between sensors is (c'-c). Therefore, diastolic velocity = x / (c'-c) Step 950 shows the process of calculating the systolic and diastolic volumes based on the corresponding rates.

[0071] The systolic volume is equal to the systolic distance traveled multiplied by the arterial area (A), where the systolic distance traveled is equal to the systolic velocity multiplied by time, and the area (A) can be determined by ultrasound or other means. As mentioned above, velocity and time are known. Therefore, Distance traveled by blood during systole = (x / (b'-b)) × (b-a1) Furthermore, systolic volume is equal to the distance traveled during systole multiplied by the area of ​​the artery (A). That is, Systolic volume = A × (x / (b'-b)) × (b-a1) Similarly, diastolic volume is equal to the diastolic distance traveled multiplied by the arterial area (A), where the diastolic distance traveled by blood is equal to the velocity multiplied by time. As mentioned above, velocity and time are known.

[0072] Therefore, the distance traveled during the expansion phase is, Distance during the expansion phase = (x / (c'-c)) × (a²-b) Diastolic volume is equal to the distance traveled during diastole multiplied by the area of ​​the artery (A). That is, Diastolic volume=A×(x / (c'-c))×(a2-b) Step 960 shows the process of calculating mean arterial pressure (MAP). This process is performed automatically on a programmed processor, where MAP is equal to cardiac output (CO) multiplied by systemic vascular resistance (SVR). Here, CO is equal to the heart rate (HR) multiplied by the stroke volume (SV), and the stroke volume (SV) is proportional to the systolic volume (calculated according to step 950 in this embodiment) by a proportional factor P. f It is equal to the result of multiplying by .

[0073]

number

[0074] Here, the proportionality constant P fIt can be calculated as described herein. Therefore, cardiac output (CO) can be determined as follows:

[0075]

number

[0076] Here, HR can be estimated based on the time per pulse wave, i.e., (a2-a1) or the number of beats per minute (60 / (a2-a1)). We also found that systemic vascular resistance (SVR) is related to the pressure change (Δp) and volumetric flow rate (vol). f I also know that it is equal to the result of dividing by ).

[0077] The pressure change (Δp) can be calculated from the systolic and diastolic rates described above. In some embodiments, the pressure change (Δp) is approximately equal to the rate change (i.e., Δv) between the systolic and diastolic rates. In some embodiments, the pressure change (Δp) is given by Poiseuille's equation (e.g., ΔP = 4Δv) or Bernoulli's equation (e.g., ΔP = Δv) 2 It is estimated based on ). In the latter case, after substituting the velocity into the equation, the pressure change (Δp) is approximately equal to the following: That is,

[0078]

number

[0079] Volumetric flow rate (vol f The mean systolic velocity can be approximated as the area of ​​the artery (A) multiplied by the mean systolic velocity, where the mean systolic velocity is equal to (systolic velocity + diastolic velocity) / 2, that is, as shown in the following equation.

[0080]

number

[0081] and,

[0082]

number

[0083] Systemic vascular resistance (SVR) can be simplified as follows:

[0084]

number

[0085] Substituting CO and SVR into the equation for MAP, we obtain the following equation:

[0086]

number

[0087] Here, HR, a1, b, b', c, c' are automatically detected from the PPG waveform, and x is equal to the fixed distance between sensors, as described above. P f It is first calculated by calibrating the blood pressure device against blood pressure readings measured using a clinically acceptable BP measurement device (e.g., a conventional oscillometric pressurized cuff device as described above). By inputting the individual's current MAP blood pressure reading from the pressurized blood pressure device, the MAP algorithm listed above is used to calculate P f It is possible to derive this.

[0088] Step 970 describes the process of calculating diastolic blood pressure (DBP). This process is performed automatically on a programmed processor. Diastolic blood pressure (DBP) = diastolic output (DO) × SVR, where DO is equal to HR × diastolic stroke volume.

[0089] As described above, HR can be directly measured by a PPG sensor, and SVR can be calculated as described above. The diastolic volume can be calculated from the following formula. That is, Diastolic stroke volume (DSV) = Diastolic volume * Proportionality factor (Pd), or

[0090] [Number]

[0091] Substituting HR, SVR, and diastolic stroke volume into the formula for diastolic blood pressure (DBP) gives the following formula. That is,

[0092] [Number]

[0093] Pd is the proportionality factor in diastole and can be initially calculated by calibrating the blood pressure device against blood pressure readings measured using a clinically acceptable BP measurement device (e.g., a conventional oscillometric pressure cuff device as described above). By inputting the current diastolic blood pressure reading of an individual from a pressure-type blood pressure device, it is possible to derive Pd using the DBP algorithm listed above.

[0094] Step 980 represents the step of calculating systolic blood pressure (SBP). This step is automatically executed on a programmed processor. SBP may be approximated according to the following formula. That is, MAP = DBP + (SBP - DBP) / 3 This means that SBP = (3 × MAP) - (2 × DBP), where MAP and DBP can be calculated as described above.

[0095] Calculation Model While exemplary models for automatically calculating blood pressure values ​​(e.g., including MAP, SBP, DBP) based on sensor data have been described above, a wide variety of models may be used to calculate blood pressure from features extracted from recorded waveforms. In embodiments, a machine learning or AI model is trained and employed 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, the CNN is trained with user data to correlate various extracted features (such as the extracted features described above) with blood pressure.

[0096] Function approximation using machine learning (e.g., deep neural networks) has been described in various publications, such as Jonas Adler et al., "Solving ill-posed inverse problems using iterative deep neural networks," Inverse Problems, 2017, Volume 33, Issue 12. Function approximation models can be trained with data collected by simultaneously recording values ​​for diverse subject groups using the novel PPG blood pressure monitoring device and sphygmomanometer described herein. The extracted features are associated with the actually measured BP values. Finally, the trained model is calibrated for each user (e.g., P f or P d It is expected that they will not require (to determine) that.

[0097] Figure 10 Prototype Implementation Configuration Figure 10 shows one implementation of a PPG signal acquisition system 10 according to one embodiment of the present invention. The system 10 includes a microprocessor board 1 (for example, an UNO R3 board manufactured by Arduino Srl) which functions as a microprocessor for acquiring and transmitting PPG signals from a PPG sensor assembly 7.

[0098] A PPG sensor assembly 7 is shown, comprising a board and PPG sensors 8 and 9. In this embodiment, each PPG sensor 8, 9 in the assembly has the following characteristics: a) Diameter = approximately 16 mm (0.625 inches); b) Total thickness = approx. 3mm (0.125 inch); c) Cable length = approximately 609 mm (24 inches) (or less, or may be cut to the desired length); d) Voltage = 3V~5V; e) Current consumption = approximately 4mA at 5V; f) Ambient light sensor (e.g., Avago APDS-9008); g) A green light source (for example, Kingbright AM2520ZGC09).

[0099] However, it should be understood that a wide range of PPG sensor assemblies may be used to carry out the present invention, except as limited by the attached claims. Power can be supplied to the microprocessor board 1 via jack 2 (e.g., a USB port). A sensor board 7 that receives this power through connections 3 and 4 (e.g., a 5V pin and a ground pin) is shown. Optionally, a rechargeable battery (not shown) is connected to the microprocessor board 1, and jack 2 can be used to charge the battery.

[0100] PPG sensors 8 and 9, connected to the microprocessor board at pins 5 and 6 respectively, are also shown. PPG signals are sent to the board through these pins and converted by an analog-to-digital converter (ADC) on the microprocessor. In this embodiment, the ADC can represent the analog voltage by 1,024 digital levels. The ADC converts the voltage readings into bits of information that the microprocessor can understand. The digitized information is sent to the onboard processor and memory and optionally sent to a portable computing device or personal computer (i.e., a PC) via jack or port 2.

[0101] The implementation shown in Figure 10 provides a convenient approach for acquiring PPG data from two locations on the wrist. The PPG signals from sensors 8 and 9 are sent to a processor and stored (e.g., as a CSV file). This data is then input to an algorithm module or hub described herein for calculating MAP and other vitals.

[0102] Figure 11 Miniaturized implementation form of PPG acquisition system Figure 11 shows one 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.

[0103] The integrated chip sensor can operate 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 integrated sensors.

[0104] A preferred integrated chip for PPG data acquisition is the MAX86176 ECG&PPG Analog Front End from Maxim Integrated (San Jose, California). It has the following characteristics: a) a 2.728 mm × 2.708 mm wafer-level packaging package; b) support for frame rates of 1 fps to 2 kfps; c) support for up to 6 LED and 4 photodiode inputs; d) a high-resolution 20-bit charge-integrating ADC; and e) a CMRR greater than 110 dB at power line frequencies. However, other small sensor acquisition systems or AFE type chips capable of power supply, reception, filtering, and converting PPG signals to digital data (for processing) may be used.

[0105] Figure 11 also shows a microcontroller unit 70 that can operate with a customized algorithm hub 72 to evaluate PPG data collected from sensors, extract and compute features, and finally calculate desired BP and vital values.

[0106] A microcontroller 70 that communicates with memory 80 (e.g., flash memory) for reading, writing, and storing data and results is also shown. Figure 11 also shows a display 90 capable of displaying various information (e.g., BP values).

[0107] Optionally, the system 50 may be equipped with a wireless communication module for wirelessly transmitting information to another device. The system 50 may also be equipped with Bluetooth® technology for transmitting information to a portable computing device such as a smartphone, tablet, or computer.

[0108] The portable computing device may be programmed using an app to synchronize data and values, user information with the PPG acquisition unit 50, and to operate in order to display user history and data.

[0109] Optionally, the system may include an operational remote or cloud server that communicates with a portable computing device via the internet, records all user data, and downloads new versions of the app and BP algorithms onto the portable computing device. In embodiments, the BP algorithm can be updated on the server based on the collection of more user BP data and user input (age, weight, height, calibration cuff pressure, etc.) (e.g., adjusting the proportional factor or machine learning algorithms mentioned above). The BP algorithm can then be downloaded to the updated portable computing device and subsequently to a wearable BP measurement device.

[0110] Figure 12 is a flowchart illustrating another method for calculating blood pressure based on PPG data. Step 1010 involves the collection of pulsatility data. This step may be performed by activating a PPG sensor (e.g., PPG sensors 8,9 described above) located in a wristwatch or another type of wearable device to acquire analog data of blood flow.

[0111] In some embodiments of the present invention, PPG sensor data acquisition is customized to achieve a much higher sampling rate (e.g., 2230 Hz) 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, to obtain a sufficient number of data points, it is necessary to increase the sampling rate, as further described herein.

[0112] In embodiments, several steps are applied, including the step of reprogramming the underlying code in the processor or microcontroller to customize or modify the sampling rate of the PPG sensor. In embodiments of the present invention, the following steps are performed: 1) A step of reducing the sample counter time between reads (e.g., the MICROS_PER_READ variable) in order to increase the sampling rate. In one embodiment, the sample counter time is reduced to less than 1 millisecond, in a preferred embodiment, the sample counter time is reduced to less than 0.5 milliseconds, and in one embodiment, the sample counter time is set to approximately 400 microseconds, or 0.4 milliseconds, corresponding to a maximum of 2500 Hz.

[0113] 2) The process of reprogramming the processor (e.g., microcontroller) to accommodate the increased data resulting from the increased sampling rate. Interrupt service routines (ISRs) are typically responsible for handling interrupt requests from hardware devices to the CPU. It is desirable to adjust the interrupt timer to avoid interrupting sampling, taking into account the increased sampling rate. For example, if the default interrupt timer has a set count of 249 for a sampling rate of 500Hz, the set count should be changed from 249 to 49 to increase the sampling rate to 2500Hz.

[0114] 3) The process of adjusting the baud rate in order to properly transmit and display the data. If the baud rate is not adjusted or does not correspond to the sampling rate, it may not be possible to record all sampled data, and data points may be lost.

[0115] Step 1020 is signal processing. In the embodiment, signal processing or preprocessing is performed by the sensor board or AFE chip to filter (1022), amplify (1024) the PPG signal and convert it to digital.

[0116] Step 1030 represents the process of extracting feature points of the pulse. In embodiments, this step is performed by evaluating the signal from step 1020 for the feature points described above in relation to Figures 7A and 7B. Examples of feature points include, but are not limited to, a1, a1', b, b', c, c', a2, a2', c, and d. This step may be performed by an algorithm for identifying cycles, as well as troughs (minims) and peaks (maximums) in each cycle. This step may be performed by a microprocessor in combination with the algorithm hub in the PPG acquisition systems 50, 100 described above in relation to Figures 5 and 11.

[0117] Step 1040 describes the step of determining the proportional factor. In the embodiment, this step is performed by calibrating the blood pressure device to blood pressure readings measured using a conventional BP measuring device (such as a conventional oscillometric pressurized cuff device). By inputting the individual's current blood pressure (MAP or DBP) readings from the pressurized blood pressure device, the proportional factor (P f The proportional factors (or Pd, respectively) can be derived using the equations for MAP and DBP listed above. Once the proportional factors have been determined, this step may be omitted during continuous monitoring.

[0118] Step 1050 represents the step of calculating blood pressure values. In embodiments, this step is performed by calculating MAP from the formula described herein, based on the feature points and proportional factors determined in steps 1030 and 1040 above. This step may be performed by a microprocessor in combination with the algorithm hub in the PPG acquisition systems 50 and 100 described above in relation to Figures 5 and 11. The algorithm hub or another storage device can hold various algorithms for determining different blood pressure values ​​and other vital signs.

[0119] Next, other blood pressure values ​​(e.g., DBP and SBP) are calculated as described above. Figure 13 is a flowchart of method 1100 for calculating blood pressure based on PPG data.

[0120] Step 1110 describes performing data preprocessing on the PPG data. In this embodiment, the raw PPG data is analyzed in both the time domain and the frequency domain, and a bandpass filter (e.g., a fourth-order Butterworth bandpass filter with a frequency range of (0.4 Hz–8 Hz)) is employed to remove very low-frequency breathing signals and baseline drift.

[0121] Step 1120 represents the process of extracting fiducial points (reference points). As described above, this function labels feature points of the PPG signal (e.g., systolic peaks, diastolic peaks, and pulse initiations). The derivative of the processed PPG signal is used, along with restricted conditions (e.g., a relatively small time window for examining data within a long, continuous data acquisition period), to accurately extract feature points from each set of PPG data. In embodiments, PPG data is collected over an acquisition period of 10 seconds to 2 minutes, more preferably 30 seconds to 90 seconds, and in some embodiments, over approximately 1 minute.

[0122] The time window for analyzing data over a relatively long data collection period can vary, and in some embodiments, it is in the range of 1 second to 2 minutes, or 1 second to 10 seconds. Preferably, the time window changes with the collection period, thereby allowing the collection period to be divided into 5 to 20 time windows (or more).

[0123] Step 1130 describes a step of evaluating signal quality. In an embodiment, this step includes comparing the pulsation pattern of all pulses with a reference pulse template calculated from input PPG data, and then calculating the cross-correlation results to determine the signal quality of all pulses.

[0124] As described above, the entire length of the collected data (e.g., continuous PPG data collection over a 1-minute period) is first separated into shorter windows (durations) (e.g., a 10-second window). Next, the data within each window is identified, and it is determined which pulses are contained within that window.

[0125] The second step is to obtain a reference pulse template from the window. First, the beat-to-beat interval is obtained from the pulses in the window. Here, in an embodiment, the beat-to-beat interval is defined as the time difference between the maximum gradient points in adjacent pulses. Second, the pulsation pattern is obtained for each pulse in the window, and a statistical value (e.g., mean or median) of all pulsation patterns is calculated and set as the reference pulse template. In a preferred embodiment, the median is used for the reference pulse template.

[0126] Next, we calculate the cross-correlation value for all pulse forms and referenced pulse templates. Pulses with a cross-correlation value smaller than the threshold are considered low-quality pulses, and we discard them.

[0127] In addition, we evaluate the quality of feature point extraction by identifying which pulses have relatively poor feature point labeling. For example, some pulses may lack or be unable to distinguish diastolic peaks, and we identify these incomplete pulses as low-quality pulses and discard them.

[0128] The output of the signal quality evaluation process 1130 is to identify and discard low-quality pulses. Step 1140 describes the process of performing a preliminary, i.e., raw (unprocessed) blood pressure calculation. This step uses the PPG model described above to convert the extracted PPG features into raw BP estimates.

[0129] In the embodiment, a scaling factor is applied to compensate for pressure loss on the arterial side of the circulation. In the embodiment, a scaling factor of 0.7 is applied to calculate the estimated BP, where BP estimated =BP raw This is / 0.7. However, in some embodiments, pressure loss on the arterial side of the circulation is compensated based on the BP estimation proportional factor described above.

[0130] The output of process 1140 is the BP matrix of the preliminary BP estimates. In embodiments of the present invention, preliminary BP estimates are further processed. The inventors have found that further processing may be useful due to the widely varying characteristics of the initial BP matrix. For example, the initial BP matrix may include about 70 inter-beat BP values ​​from PPG data of one minute in length. Inter-beat values ​​can vary significantly. In some cases, BP values ​​can vary from a minimum of about 10 mmHg to a maximum of about 2000 mmHg or more. Therefore, in embodiments, instead of processing all inter-beat raw BP values ​​to generate a final BP estimate, the BP threshold range is specified to include only a portion of the raw BP values ​​in order to calculate the final BP estimate, and some raw BP values ​​are excluded.

[0131] Step 1150 describes a step in which different computational approaches are tested. In the embodiment, several different arithmetic approaches are performed to improve the accuracy of the BP estimate. Examples of arithmetic approaches include, but are not limited to, the following: Total absolute error. The sum of all pairs of (BP estimate - BP reference value), where the BP reference value is measured simultaneously with the BP estimate by an arm cuff device (or another technique).

[0132] Confidence level. The number of BP estimates within the threshold error. Here, in embodiments of the present invention, the threshold error is in the range of + / -5 mmHg to + / -10 mmHg, and in some embodiments, it is + / -8 mmHg.

[0133] These approaches are calculated separately using both the mean and median of the BP estimates. Step 1160 describes a step to evaluate the accuracy of the calculation approach. In this step, the accuracy of the tested calculation approach is examined. In embodiments, accuracy is based on which BP range yields the smallest total absolute error and the highest confidence level. In embodiments, the limited threshold range (mean) is less than 300 mmHg, in some embodiments the limited threshold range (mean) is less than 200 mmHg, and in some embodiments the limited threshold range (mean) is between 30 mmHg and 190 mmHg.

[0134] Step 1170 represents the process of calculating (i.e., recalculating) the final blood pressure based on a selected calculation approach (mean or median), a limited threshold range, and the omission of any of the low-quality pulses.

[0135] The process optionally includes determining pulse-to-tension time-based blood pressure (PTT-BP) and providing a PTT-BP linear regression equation, calculating the mean PTT from input data, and obtaining a blood pressure estimate based on the PTT and PTT-BP equation. In embodiments, if a cuff is not used to determine the reference value described above in relation to step 1150, the PTT-BP value can be used as the reference BP to test different calculation approaches.

[0136] In addition, another function of calculating PTT-BP is to evaluate the accuracy of the feature point extraction approach described above. Since the PTT-BP calculation uses the feature points extracted by the extraction method described above, an accurate PTT-BP estimation result will demonstrate that the feature point extraction approach has high accuracy.

[0137] In addition, in the embodiments, once a baseline value is obtained and used to determine the limit threshold blood pressure range, the cuff may be removed from the human arm, and BP monitoring can continue using the established limit threshold range. Thus, embodiments of the present invention have the advantage that once the BP limit threshold range (and any other factors described herein) is established during the initial setup or calibration phase, the cuff can be removed from the human arm. 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.

[0138] Examples Tests were conducted according to embodiments of the present invention to estimate human MAP. Description of the test setup. Two identical PPG sensors, as described above, were placed on a human left arm. The first sensor was placed on the wrist, and the second sensor was placed on the forearm, approximately 15 cm away from the first sensor. Both sensors were connected to an Arduino board as described above for signal acquisition. In addition, an Omron BP monitor (reference device) was attached to the right arm to obtain a reference BP value for comparison.

[0139] Eight sets of 1-minute data were collected using the test device and the reference device. The initial MAP matrix was calculated. Then, different approaches were tested as described above to determine the limiting threshold range (mean) (30 mmHg to 190 mmHg in this implementation) and to filter out raw MAP estimates that were outside this range. The mean MAP was then recalculated based on the filtered MAP matrix to obtain the final MAP estimate.

[0140] The results are shown in Figures 14 and 15. Referring to Figure 14, the calculated MAP (Cal_MAP) was compared with the reference MAP value (Ref_MAP) from Omron's BP monitor. The difference value (Abs_Error) is also shown.

[0141] Referring to Figure 15, the Bland-Altman plot shows the accuracy of PPG-BP, where the "○" scatter points represent the difference in MAP values ​​between the MAP calculated from the PTT model and the reference BP reading from Omron's BP monitor. The "×" scatter points represent the comparison results between the MAP calculated from our PPG-BP model and the reference BP reading from Omron's BP monitor.

[0142] Based on this dataset, the accuracy of the PPG-BP test device at that time was calculated to be approximately 6 ± 10 mmHg. The results described above demonstrate the effectiveness of the PPG-BP test device for estimating MAP according to embodiments of the present invention. While specific implementations are shown in relation to the results in Figures 14-15, the present invention is not intended to be limited in this way. In fact, other implementations and steps may be included in the present invention in any logical combination or order, unless excluded by the appended claims.

[0143] Alternative Embodiments Although the device is described as being positioned on the wrist, it may be configured in a different manner. The device may be configured to read blood flow velocity data from another part of the body where arteries close to the surface of the skin are present. In embodiments, the device is positioned to cover capillaries near the surface of the patient's skin, and the PPG signal and calculations are performed as described herein, without the need to examine the arteries. Examples of other configurations include, but are not limited to, handheld probes (with or without an umbilical cord for electronic cables), patches (optionally with adhesive), clips (e.g., for the ear), rings, and belts surrounding the chest, waist, thigh, or another body part.

[0144] In addition, please understand 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.

[0145] In some embodiments, the BP monitoring device may be capable of being controlled by a remote device such as a tablet, smartphone, or laptop.

[0146] In addition, in other embodiments, additional types of sensors are combined with or replace one or more of the multiple sensors. For example, referring to Figure 2, one or more of the individual PPG probes in pair 110,112 may be replaced with Doppler or optical emitters and detectors. Preferably, the sensors are self-contained / standalone and include their own processing electronics for providing signals to a CPU. However, in embodiments, less complex emitters and detectors, or cameras, may be incorporated into the device, and raw data may be sent to a processor for preprocessing and evaluation. In embodiments, a Doppler probe is combined with a PPG sensor. The ultrasonic energy from the Doppler probe is used to generate transient distortion in one PPG waveform. Multiple PPG sensors can then better determine the number of pulse waves propagating between the first PPG sensor and the second PPG sensor for a distance (X) they are separated by observing when each PPG sensor detects the ultrasonic distortion waveform.

[0147] In this embodiment, the device includes, but is not limited to, multiple operating modes, including a location mode, a calibration mode, and / or a monitoring mode. In embodiments, the vascular localization mode or module can be operated to alert the user to the optimal position on the skin for holding the device. This localization mode (as opposed to the blood pressure monitoring mode described above) can be activated by the user to initiate energy delivery to the skin. In localization mode, the energy emitter delivers energy into the skin, and the electronics transmit the processed data to the main processor for evaluation. In embodiments, the processor can be operated to alert the user to the optimal position (e.g., via sound, vibration, or visual indicator) as the user moves the device (whether a wearable or handheld device) along the skin during localization mode. The user can scroll back and forth along the skin area to find the optimal position. The audio indicator may be operated to increase the volume or pitch as the measured blood flow velocity increases. Similarly, the device may be operated 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) in response to changes in velocity associated with the position along the skin. Once the user is satisfied with the position, they secure or hold the device in place with the strap to activate blood pressure monitoring mode.

[0148] In the embodiment, the calibration mode asks the user for a blood pressure reading (or another blood pressure-related parameter such as stroke volume) obtained by an alternative means (e.g., an oscillometric pressurized cuff device, catheter, etc.). The user's proportional factor is obtained from a reading measured by the device itself (e.g., device 100, and P f Assume that the placeholder / estimated value of is equal to the actual reading measured by an alternative device (e.g., an oscillometric pressurized cuff), and the proportional factor P fThe device automatically calculates this by solving the equations described herein. In a preferred embodiment, the calibration mode prompts the user to repeat the calibration several times until the proportional factor becomes constant.

[0149] In this embodiment, the monitoring mode can be performed following the location mode and the calibration mode. While several embodiments have been disclosed above, it should be understood that other modifications and variations can be made to the disclosed embodiments without departing from the present invention. In fact, any of the components described herein may be combined with each other unless such components are mutually exclusive. Any of the steps described herein may be combined in any combination and order unless such steps are mutually exclusive.

Claims

1. A blood pressure monitoring device for calculating the user's mean arterial pressure, A case, and a strap configured to hold the case around the user's wrist, A first PPG sensor located within the case and directed toward the artery of the wrist when the case is strapped to the wrist, A second PPG sensor is located within the case, spaced apart from the first PPG sensor, and directed toward the artery of the wrist when the case is strapped to the wrist. It is a processor, Obtaining multiple features from the PPG waveform data generated by those PPG sensors, A blood pressure monitoring device comprising a processor capable of performing the calculation of mean arterial pressure (MAP) based on the aforementioned plurality of features.

2. The blood pressure monitoring device according to claim 1, wherein the calculation of the MAP is further based on a predetermined proportionality coefficient associated with the user, the proportionality coefficient is first calculated, and the blood pressure is measured using a second type of blood pressure measuring device.

3. The blood pressure monitoring device according to claim 1, wherein one of the plurality of features includes one or more of diastolic velocity, systolic velocity, systolic volume, diastolic volume, diastolic distance, systolic distance, heart rate, diastolic time, and systolic time.

4. The blood pressure monitoring device according to claim 1, wherein the processor is further operable to calculate diastolic blood pressure (DBP) or systolic blood pressure (SBP).

5. The blood pressure monitoring device according to claim 1, wherein the case further houses the processor, battery, memory, and PPG electronic equipment.

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

7. The blood pressure monitoring device according to claim 1, further comprising a display, wherein the strap, the case, and the display collectively form a shape resembling a wristwatch.

8. The blood pressure monitoring device according to claim 1, further comprising a positioning module for alerting the user to the optimal position on the wrist in order to strap-secure the case to the optimal position on the wrist when the user adjusts the position of the case along the user's wrist.

9. The blood pressure monitoring device according to claim 1, wherein the calculation of the MAP is performed without using ECG data.

10. A blood pressure monitoring system for calculating a user's mean arterial pressure, comprising the device according to any one of claims 1 to 4 and 6 to 9, wherein the processor is located outside the case.

11. A method for monitoring human mean arterial pressure (MAP) based on PPG data, A step of placing a first PPG sensor and a second PPG sensor on the human skin, optionally on the wrist, wherein the second PPG sensor is spaced a fixed distance away from the first PPG sensor. A step of acquiring PPG data corresponding to the blood flow in the human blood vessels from the first PPG sensor and the second PPG sensor, The acquisition process involves obtaining multiple features from the aforementioned PPG data, A method comprising a MAP calculation step of calculating the mean arterial pressure (MAP) based on multiple feature quantities.

12. The blood pressure monitoring system according to claim 10, further comprising an adhesive for adhering the case to the user's skin.

13. The blood pressure monitoring system according to claim 10, further comprising an umbilical cord extending from the case for transmitting information between the case and another device, and for optionally supplying power from the other device to the case.

14. The blood pressure monitoring system according to claim 10, wherein the data generated from the first PPG sensor and the second PPG sensor are the user's velocity data, volumetric flow rate data, or both.

15. The blood pressure monitoring system according to claim 14, wherein the aforementioned feature quantities are calculated from the volumetric flow rate data.

16. The blood pressure monitoring system according to claim 15, wherein the MAP is calculated without using a database, atlas, or general population type information.

17. The blood pressure monitoring system according to claim 10, wherein all of the sensors are directed towards the same anatomical body part and are spaced less than 100 mm apart from each other, and less than approximately 50 mm apart at will.

18. The method according to claim 11, further comprising an evaluation step for evaluating signal quality, the evaluation step comprising: calculating a reference template; comparing the pulsation pattern of each pulse with the reference template; identifying low-quality pulses based on the comparison step; and obtaining the plurality of feature quantities by excluding the low-quality pulses from the PPG data used.

19. The blood pressure monitoring device according to claim 1, wherein the processor is further programmed to evaluate the signal quality of the PPG waveform data, and the evaluation of the signal quality includes: calculating a reference template; comparing the pulsation pattern of each pulse with the reference template; identifying low-quality pulses based on the comparison step; and excluding the low-quality pulses from the PPG waveform data to be used to obtain the plurality of features.

20. The blood pressure monitoring device according to claim 19, wherein the processor is further programmed to calculate a limit threshold BP range after the step of calculating the MAP, and to recalculate the MAP based on the limit threshold BP range.

21. The blood pressure monitoring device according to claim 20, wherein the processor is further programmed to determine the limit threshold BP range based on calculating the BP error and confidence level.

22. The blood pressure monitoring system according to claim 10, further comprising a wireless communication module located within the case for wirelessly transmitting data.

23. The blood pressure monitoring system according to claim 10, wherein the case further comprises a port for transmitting data from the device via a code.

24. The blood pressure monitoring system according to claim 10, wherein the processor is enclosed in a console and information is transmitted between the case and the console via code.

25. The blood pressure monitoring device according to claim 19, wherein the calculation of the reference template is performed by calculating statistical values ​​of the pulsation patterns of all the pulses within a time window.

26. The blood pressure monitoring device according to claim 25, wherein comparing the pulsation pattern of each pulse with the reference template includes calculating a cross-correlation value between the pulsation pattern of each pulse and the reference template.

27. ​​The blood pressure monitoring device according to claim 26, wherein the identification of the low-quality pulse is based on whether the cross-correlation value is less than a threshold.

28. The blood pressure monitoring device according to claim 27, wherein the PPG waveform data is collected over a period of one minute and the time window is 10 seconds.

29. The blood pressure monitoring device according to claim 1, further comprising a trained machine learning model for determining the MAP based on the PPG waveform data, optionally further comprising a deep neural network-based model.

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