A cuffless blood pressure monitoring system based on PWTT principle
Through the cuffless PWTT principle blood pressure monitoring system, the patch-type electrocardiogram monitoring device and the watch-type physiological parameter monitoring device, combined with the cloud server's PWTT blood pressure calculation method and biometric calibration algorithm, the cuff compression problem of the Holter blood pressure instrument during 24-hour monitoring is solved, and high-precision and low-error blood pressure monitoring is achieved.
Patent Information
- Application Number
- CN202010665110.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2020-07-10
- Publication Date
- 2025-04-01
- Estimated Expiration
- 2040-07-10
AI Technical Summary
The existing Holter blood pressure instrument needs to use a cuff to compress the artery during 24-hour uninterrupted monitoring, resulting in affected patients' rest, redness and swelling of the compressed area, and blood circulation. The cuff compression force decreases, resulting in increased errors in monitoring data and reduced accuracy.
The cuffless blood pressure monitoring system based on the PWTT principle is adopted, and the ECG, body temperature, and blood oxygen volume wave parameters are monitored through the patch ECG, body temperature, and blood oxygen volume wave parameters, and the PWTT blood pressure calculation method in the cloud server and the mobile kinetic energy mutation calibration algorithm based on biological characteristics are used to accurately obtain blood pressure data.
It realizes 24-hour uninterrupted blood pressure monitoring with high accuracy and low error, avoids discomfort and health risks caused by cuff compression, and is suitable for the uninterrupted monitoring needs of families or hospitals.
Smart Images
Figure CN113925474B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of blood pressure monitoring, and in particular to a cuffless blood pressure monitoring system based on the PWTT principle. Background Art
[0002] Blood pressure usually refers to arterial blood pressure. Medical science shows that changes in blood pressure are directly related to cardiovascular and cerebrovascular diseases. Currently, the most commonly used blood pressure monitoring equipment is the Holter blood pressure monitor. However, the Holter blood pressure monitor has the following disadvantages when monitoring patients 24 hours a day: 1. The Holter blood pressure monitor needs to use a cuff to compress the artery when monitoring blood pressure, which greatly affects the patient's rest when used; 2. Since the cuff compresses the artery, it will cause redness and swelling in the compressed area for too long and affect blood circulation; 3. Since the cuff's compression force will gradually decrease over time, the monitoring data will gradually increase in error and the accuracy will gradually decrease when used 24 hours a day.
[0003] Although the PWTT principle can accurately obtain blood pressure data, it is difficult to calibrate the start and end positions of the PWTT, especially when obtaining the end point. The PWTT principle requires calibrating the start point at the ECG R wave and the end point at 20% of the pulse wave rise. The calibrated start point and end point are the PWTT segment. In this process, the pulse wave is affected by external factors, which will cause the start or end point of the pulse wave to be not on the same straight line, so the end point of the PWTT calibration is difficult to obtain.
[0004] In order to solve the above problems, the present invention provides a cuffless blood pressure monitoring device based on the PWTT principle. Summary of the invention
[0005] The purpose of the present invention is to provide a cuffless blood pressure monitoring device based on the PWTT principle to solve the problems mentioned in the above technical background. At the same time, the present invention has high monitoring accuracy, small error, no need to use a cuff, and is easy to carry. It can monitor central arterial pressure, blood oxygen saturation, body temperature, electrocardiogram and heart rate data 24 hours a day, and is suitable for use in homes or hospitals for uninterrupted monitoring of physiological parameters.
[0006] To achieve the above purpose, the technical solution adopted by the present invention is as follows:
[0007] A cuffless blood pressure monitoring system based on the PWTT principle, characterized in that it is composed of a patch-type ECG monitoring device, a wristwatch-type physiological parameter monitoring device and a cloud server, the wristwatch-type physiological parameter monitoring device simultaneously monitors ECG, body temperature and blood oxygen volume wave parameters, the wristwatch-type physiological parameter monitoring device is composed of a synchronous monitoring host, a reflective photoelectric sensor, a body temperature probe a, an ECG electrode a, a display and a wristwatch, the reflective photoelectric sensor and the body temperature probe a are located below the synchronous monitoring host, the ECG electrode a and the display are located on the front of the wristwatch, the patch-type ECG monitoring device is composed of an ECG monitoring host, an ECG electrode b, a body temperature probe b and a shell, the patch-type ECG monitoring device is attached to the human chest, the cloud server is equipped with a PWTT blood pressure calculation method, the synchronous monitoring host uses a reflective light to calculate blood pressure. The electrical sensor, temperature probe a, and ECG electrode a obtain blood oxygen volume wave data, wrist temperature data, and ECG data a. The ECG monitoring host obtains ECG data b and chest temperature data through ECG electrode b and temperature probe b. After the data is obtained, the ECG monitoring host transmits the data to the synchronous monitoring host via Bluetooth. The synchronous monitoring host simultaneously uploads the blood oxygen volume wave data, wrist temperature data, ECG data a, ECG data b, and chest temperature data to the cloud server. The cloud server calibrates and analyzes the received data to obtain the central arterial pressure (BP) value, heart rate value, temperature value (T), and blood oxygen saturation value. The cloud server transmits the calibrated and analyzed values to the synchronous monitoring host, and the synchronous monitoring host displays the values through a display. The calibrated and analyzed values in the cloud server can also be viewed online.
[0008] Preferably, both the synchronous monitoring host and the electrocardiogram monitoring host contain synchronization modules, the two synchronization modules are synchronized via Bluetooth mode, and the synchronization error value is less than 50 microseconds / hour.
[0009] Preferably, each wristwatch-type physiological parameter monitoring device can be connected to 1 to 30 patch-type ECG monitoring devices or wristwatch-type physiological parameter monitoring devices via a wireless network (Bluetooth, 4G or WiFi).
[0010] Preferably, the wristwatch-type physiological parameter monitoring devices can transmit data to each other via a wireless network (Bluetooth, 4G or WiFi).
[0011] Preferably, the patch-type ECG monitoring device and the wristwatch-type physiological parameter monitoring device monitor continuously and upload data to the cloud server every 5s±0.01s.
[0012] Preferably, the PWTT algorithm in the cloud server calculates the blood pressure data as follows: a. The cloud server receives the ECG data and blood oxygen volume wave data monitored by the patch-type ECG monitoring device and the wristwatch-type physiological parameter monitoring device; b. The PWTT starting calibration position is obtained at the R wave in the ECG data a or the ECG data b; c. The PWTT ending calibration position is obtained in the blood oxygen volume wave data; d. The difference between the obtained PWTT starting calibration position and the obtained PWTT ending calibration position is calculated to obtain the pulse wave delay value (△T) of PWTT; e. The central arterial pressure is obtained according to the formula Bp=A+B*△T, where Bp is the central arterial pressure, A and B are constants, and △T is the pulse wave delay value.
[0013] Preferably, when obtaining the PWTT end calibration position in the blood oxygen volume wave data, a biometric-based mobile kinetic energy mutation calibration algorithm [a*T*Xn*(Xn-Xn-m)*(Xn+m-Xn)] is performed, wherein a is the sensitivity coefficient, T is the body temperature data (equal to (wrist temperature data+chest temperature data) / 2 at normal temperature), Xn is the measured pulse wave data sequence (the pulse wave is obtained at the same time when the blood oxygen volume wave is obtained), n is the serial number, and m is the coefficient of the organism's own elastic scale.
[0014] Compared with the prior art, the present invention has the following beneficial effects:
[0015] By optimizing the design and combination of cloud servers, patch-type ECG monitoring devices and wristwatch-type physiological parameter monitoring devices, a cuffless blood pressure monitoring device based on the PWTT principle is developed to replace the Holter blood pressure monitor without the need for a cuff, solving the following drawbacks of the Holter blood pressure monitor when monitoring patients for 24 hours: 1. The Holter blood pressure monitor needs to use a cuff to compress the artery when monitoring blood pressure, which greatly affects the patient's rest during use; 2. Since the use of the cuff to compress the artery will cause redness and swelling in the compressed area over time, and affect blood circulation; 3. Since the use of the cuff will gradually reduce the pressure of the cuff over time, the error of the monitoring data will gradually increase and the accuracy will gradually decrease over time when used for 24 hours. In addition, the mobile kinetic energy mutation calibration algorithm based on biometrics is used to accurately obtain the end point position of the PWTT calibration. The present invention has high monitoring accuracy, small error, does not require the use of a cuff, and is easy to carry. It can monitor central arterial pressure, blood oxygen saturation, body temperature, electrocardiogram and heart rate data 24 hours a day, and is suitable for use in homes or hospitals for uninterrupted monitoring of physiological parameters. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Figure 1 The present invention is a schematic diagram of the structure;
[0017] Figure 2The following is a schematic diagram of the structure of a wristwatch-type physiological parameter monitoring device;
[0018] Figure 3 Figure 1: PWTT calibration diagram.
[0019] Figure 4 Figure 1: Blood oxygen volume wave diagram (A is an idealized diagram, B is a measured diagram).
[0020] The corresponding parts are marked in the figure: patch-type ECG monitoring device (1), wristwatch-type physiological parameter monitoring device (2), cloud server (3), synchronous monitoring host (6), reflective photoelectric sensor (7), body temperature probe a (8), ECG electrode a (5), display (4), wristwatch (9). DETAILED DESCRIPTION
[0021] The technical solution of the present invention will be clearly described below in conjunction with the accompanying drawings of the specification. In conjunction with the accompanying drawings of the specification, the specific implementation of the present invention is as follows:
[0022] See attached Figures 1-2A cuffless blood pressure monitoring system based on the PWTT principle, characterized in that it is composed of a patch-type ECG monitoring device (1), a wristwatch-type physiological parameter monitoring device (2) and a cloud server (3), wherein the wristwatch-type physiological parameter monitoring device (2) simultaneously monitors ECG, body temperature and blood oxygen volume wave parameters, the wristwatch-type physiological parameter monitoring device (2) is composed of a synchronous monitoring host (6), a reflective photoelectric sensor (7), a body temperature probe a (8), an ECG electrode a (5), a display (4) and a wristwatch (9), wherein the reflective photoelectric sensor (7) and the body temperature probe a (8) are located below the synchronous monitoring host (6), and the ECG electrode a (5) and the display (4) are located on the front of the wristwatch (9), the patch-type ECG monitoring device (1) is composed of an ECG monitoring host, an ECG electrode b, a body temperature probe b and a shell, the patch-type ECG monitoring device (1) is attached to the human chest, the cloud server (3) is equipped with a PWTT blood pressure calculation method, and the synchronous monitoring host (6) is connected to the cloud server (3) by a PWTT blood pressure calculation method. The step monitoring host (6) obtains blood oxygen volume wave data, wrist body temperature data, and electrocardiogram data a through a reflective photoelectric sensor (7), a body temperature probe a (8), and an electrocardiogram electrode a (5). The electrocardiogram monitoring host obtains electrocardiogram data b and chest body temperature data through electrocardiogram electrode b and body temperature probe b. After the data is obtained, the electrocardiogram monitoring host transmits the data to the synchronous monitoring host (6) through Bluetooth. The synchronous monitoring host (6) simultaneously uploads the blood oxygen volume wave data, wrist body temperature data, electrocardiogram data a, electrocardiogram data b, and chest body temperature data to a cloud server (3). The cloud server (3) calibrates and analyzes the received data to obtain a central arterial pressure (BP) value, a heart rate value, a body temperature value (T), and a blood oxygen saturation value. The cloud server (3) transmits the calibrated and analyzed values to the synchronous monitoring host (6). The synchronous monitoring host (6) displays the values through a display (4). The calibrated and analyzed values in the cloud server (3) can also be viewed online.
[0023] Preferably, the synchronization monitoring host (6) and the electrocardiogram monitoring host both contain synchronization modules, the two synchronization modules are synchronized via Bluetooth mode, and the synchronization error value is less than 50 microseconds / hour.
[0024] Preferably, each wristwatch-type physiological parameter monitoring device (2) can be connected to 1 to 30 patch-type electrocardiogram monitoring devices (1) or wristwatch-type physiological parameter monitoring devices (2) via a wireless network (Bluetooth, 4G or WiFi).
[0025] Preferably, the wristwatch-type physiological parameter monitoring devices (2) can transmit data to each other via a wireless network (Bluetooth, 4G or WiFi).
[0026] Preferably, the patch-type electrocardiogram monitoring device (1) and the wristwatch-type physiological parameter monitoring device (2) monitor continuously and upload data to the cloud server (3) every 5s±0.01s.
[0027] See attached Figure 3 : Preferably, the PWTT algorithm in the cloud server (3) calculates the blood pressure data as follows: a. The cloud server (3) receives the ECG data and blood oxygen volume wave data monitored by the patch-type ECG monitoring device (1) and the wristwatch-type physiological parameter monitoring device (2); b. The PWTT start calibration position is obtained at the R wave in the ECG data a or the ECG data b; c. The PWTT end calibration position is obtained in the blood oxygen volume wave data; d. The difference between the obtained PWTT start calibration position and the obtained PWTT end calibration position is calculated to obtain the pulse wave delay value (△T) of PWTT; e. The central arterial pressure is obtained according to the formula Bp=A+B*△T, where Bp is the central arterial pressure, A and B are constants, and △T is the pulse wave delay value.
[0028] See attached Figure 4 , comparing Figure A and Figure B, it can be found that in the idealized Figure A, the PWTT calibration end positions are all on the same straight line, and there is no other clutter interference. At this time, the PWTT calibration end position is easy to obtain. In the measured Figure B, it can be found that the blood oxygen volume wave diagram will be interfered by external clutter. At this time, it is difficult to accurately find the PWTT calibration end position. If Figure B is optimized to Figure A through software, the data is distorted at this time, and there is a large difference with the actual data. In the present invention, it is preferred that the PWTT end calibration position is obtained in the blood oxygen volume wave data. When the biometric feature is set, the mobile kinetic energy mutation calibration algorithm [a*T*Xn*(Xn-Xn-m)*(Xn+m-Xn)] is performed, where a is the sensitivity coefficient, T is the body temperature data (equal to (wrist body temperature data + chest body temperature data) / 2 at normal temperature), Xn is the measured pulse wave data sequence (pulse wave is obtained at the same time when the blood oxygen volume wave is obtained), n is the sequence number, and m is the coefficient of the elastic scale of the organism itself. Through this algorithm, the PWTT calibration end position at 20% of each blood oxygen volume wave can be accurately found. This process can ensure the smooth progress of the process in claim 5.
[0029] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Those familiar with the technology may modify or improve the above embodiments without violating the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed by the present invention should still be covered by the claims of the present invention.
Claims
1. A cuffless blood pressure monitoring system based on the PWTT principle, characterized in that: include: The system consists of a patch-type ECG monitoring device, a wristwatch-type physiological parameter monitoring device and a cloud server; The patch-type ECG monitoring device is attached to the human chest and consists of an ECG monitoring host, ECG electrodes b, a body temperature probe b and a shell; The wristwatch-type physiological parameter monitoring device is used to simultaneously monitor ECG, body temperature and blood oxygen volume wave parameters, and is composed of a synchronous monitoring host, a reflective photoelectric sensor, a body temperature probe a, an ECG electrode a, a display and a wristwatch; wherein the reflective photoelectric sensor and the body temperature probe a are located below the synchronous monitoring host, and the ECG electrode a and the display are located on the front of the wristwatch; The cloud server is equipped with a PWTT blood pressure calculation method to calculate blood pressure data; The synchronous monitoring host obtains blood oxygen volume wave data, wrist body temperature data, and electrocardiogram data a through a reflective photoelectric sensor, a body temperature probe a, and an electrocardiogram electrode a; The ECG monitoring host obtains ECG data b and chest temperature data through ECG electrode b and temperature probe b. After the data is obtained, the ECG monitoring host transmits it to the synchronization monitoring host via Bluetooth. The synchronization monitoring host simultaneously uploads the blood oxygen volume wave data, wrist temperature data, ECG data a, ECG data b, and chest temperature data to the cloud server; The cloud server calibrates and analyzes the received data to obtain the central arterial pressure BP value, heart rate value, body temperature value T, and blood oxygen saturation value, and then transmits the calibrated and analyzed values to the synchronous monitoring host; The cloud server is equipped with a PWTT blood pressure calculation method to calculate blood pressure data, including: when obtaining the PWTT end calibration position in the blood oxygen volume wave data, performing a mobile kinetic energy mutation calibration algorithm [a*T*Xn*(Xn-Xn-m)*(Xn+m-Xn)] based on biometrics to find the PWTT calibration end position at 20% in each blood oxygen volume wave; wherein a is a sensitivity coefficient, T is body temperature data, and at normal temperature, T=(wrist body temperature data+chest body temperature data) / 2, Xn is a measured pulse wave data sequence, that is, the pulse wave is obtained while obtaining the blood oxygen volume wave, n is a sequence number, and m is a coefficient of the elastic scale of the organism itself; The synchronous monitoring host displays the values through the display, and the values calibrated and analyzed in the cloud server can also be viewed online.
2. The system according to claim 1, characterized in that Also includes: Both the synchronous monitoring host and the ECG monitoring host contain synchronization modules. The two synchronization modules are synchronized via Bluetooth mode, and the synchronization error value is less than 50 microseconds / hour.
3. The system according to claim 1, characterized in that Also includes: Each wristwatch-type physiological parameter monitoring device is connected to 1 to 30 patch-type electrocardiogram monitoring devices or wristwatch-type physiological parameter monitoring devices via a wireless network.
4. The system according to claim 3, characterized in that Also includes: The wristwatch-type physiological parameter monitoring devices can also transmit data to each other via wireless networks.
5. The system according to claim 1, characterized in that Also includes: The patch-type ECG monitoring device and the wristwatch-type physiological parameter monitoring device monitor continuously and upload data to the cloud server every 5s±0.01s.
6. The system according to any one of claims 1 to 5, characterized in that: The cloud server uses the PWTT blood pressure calculation method to calculate blood pressure data, specifically including: a. The cloud server receives the ECG data and blood oxygen volume wave data monitored by the patch-type ECG monitoring device and the wristwatch-type physiological parameter monitoring device; b. Obtain the PWTT start calibration position at the R wave in ECG data a or ECG data b; c. Obtain the PWTT end calibration position in the blood oxygen volume wave data; d. Calculate the difference between the obtained PWTT starting calibration position and the obtained PWTT ending calibration position to obtain the pulse wave delay value (△T) of PWTT. e. Calculate the central arterial pressure according to the formula Bp=A+B*△T, where Bp is the central arterial pressure, A and B are constants, and △T is the pulse wave delay value.
7. The system according to claim 3 or 4, characterized in that: The wireless network includes: Bluetooth, 4G or WiFi.
Citation Information
Patent Citations
Pulse wave velocity determination, for example for blood pressure monitoring
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