Blood pressure estimation method and device and storage medium

Abstract

The invention relates to a blood pressure estimation method and device and a storage medium, belongs to the technical field of medical equipment, and solves the problems that in the prior art, the accuracy of air bag type blood pressure measurement is insufficient, and the convenience of electrocardiogram and photoelectric volume type blood pressure measurement is insufficient. Performing data preprocessing on the original air pressure data to obtain original average pressure; s2, based on the original average pressure, processing through a blood pressure artificial neural network module to obtain a pressure difference; and S3, based on the original average pressure and the pressure difference, calculating to obtain diastolic pressure and systolic pressure as processing results, and storing and displaying the processing results.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment, and in particular to a blood pressure estimation method, device and storage medium. Background Art

[0002] The importance of blood pressure measurement lies in that it is one of the important indicators of the normal physiological function of the human body and plays a vital role in maintaining the health of the cardiovascular system. By measuring and analyzing blood pressure values, we can understand a person's heart and blood vessel health.

[0003] Existing blood pressure estimation methods are based on the measurement of physiological parameters, such as electrocardiogram (ECG), photoplethysmography (PPG), etc. The blood pressure measurement equipment used in these methods is highly dependent on signals and is greatly affected by factors such as skin contact, movement, and ambient light. It may cause signal distortion or loss, thereby affecting the accuracy of blood pressure measurement. In addition, existing blood pressure estimation methods based on machine learning improve the accuracy of blood pressure estimation by training a large amount of data. However, these methods have high computational complexity and large amount of calculation due to complex signal processing, making them difficult to promote in practical applications.

[0004] The Chinese invention patent application, publication number CN111358452A, invention name "A method and device for blood pressure prediction based on synchronous signals", discloses a blood pressure prediction method, including synchronous acquisition of electrocardiogram and photoplethysmogram, then data fusion processing of the acquired ECG signal and PPG signal, and regression calculation and inference of blood pressure data through a blood pressure model composed of a blood pressure convolutional neural network and a blood pressure artificial neural network. This method requires a complex electrocardiogram measurement machine, and the blood pressure model used has a large amount of calculation, which makes the overall process of the method complex and cannot be integrated into wearable devices with low computing power.

[0005] The Chinese invention patent application, publication number CN113520357A, titled "A blood pressure measurement device and method", discloses a blood pressure measurement method, including collecting signals from a pressure sensor, and fusing and calculating the collected signals to obtain a blood pressure value by a processor. This method lacks a correction process in the processing of the collected information, resulting in a large error in the obtained result, making it difficult to obtain an accurate blood pressure value.

[0006] There is a need in the art for a blood pressure estimation method, device and storage medium with high accuracy, good convenience and small amount of calculation data. Summary of the invention

[0007] In order to solve the above problems, a blood pressure estimation method, device and storage medium are provided according to an embodiment of the present invention.

[0008] According to one embodiment of the present invention, a blood pressure estimation method is provided, comprising the following steps: Step S1, collecting the original air pressure data of the measured object, and performing data preprocessing on the original air pressure data to obtain the original average pressure; Step S2, based on the original average pressure, the pressure difference is obtained by processing through the blood pressure artificial neural network module; Step S3, based on the original mean pressure and the pressure difference, the diastolic pressure and the systolic pressure are calculated as the processing result, and the processing result is stored and displayed; Wherein, the step S1 specifically includes the following steps: Step S100, collecting original air pressure data, the collected original air pressure data includes air pressure data and contact data; Step S200, preprocessing the collected original air pressure data to obtain the original average pressure as preprocessed data; Step S300, judging the visitor mode, judging whether the target user is in the visitor mode according to the target user's selection, if yes, judging it as the visitor mode and executing step S400, otherwise judging it as the personal mode and executing step S310; Step S310, reading the personal data and historical data of the target user; Step S400, inputting the original average pressure into the central processing unit module.

[0009] Optionally, the step S2 specifically includes the following steps: Step S500, comparing the inputted original average pressure with the first threshold, and determining whether the original average pressure exceeds the first threshold, if so, executing step S520, otherwise executing step S510; Step S510, inputting the original average pressure into the first blood pressure artificial neural network model, outputting the pressure difference after calculation, and executing step S530; Step S520, inputting the original average pressure into the second blood pressure artificial neural network model, and outputting the pressure difference after calculation; Step S530, input the pressure difference into the central processing module and store it in the data storage module.

[0010] Optionally, the step S3 specifically includes the following steps: Step S600, comparing the obtained pressure difference with the second threshold value to determine whether the obtained pressure difference exceeds the second threshold value, if so, executing step S610, otherwise executing step S700; Step S610, outputting an error, outputting an error prompt message to the target user, and returning to step S100; Step S700, calculating an estimated average pressure by using the obtained pressure difference and the original average pressure, and calibrating the estimated average pressure to obtain a calibrated estimated average pressure; Step S800, taking the obtained calibrated estimated mean pressure as the estimated value of diastolic pressure, correcting and calculating to obtain the diastolic pressure; Step S900, based on the obtained diastolic pressure and the calibrated estimated mean pressure, the systolic pressure is calculated, and the obtained diastolic pressure and systolic pressure are stored as processing results and output and displayed.

[0011] Optionally, the step S200 further includes the following steps: Step S210, performing a first filtering process on the collected raw air pressure data to obtain an air pressure sequence and a contact sequence; Step S220, performing a second filtering process on the air pressure sequence to obtain dynamic pressure data and static pressure data; Step S230, performing Gaussian fitting on the dynamic pressure data to obtain a Gaussian curve; Step S240, performing static pressure straight line fitting on the static pressure data to obtain a static pressure straight line; Step S250, obtaining the original average pressure through Gaussian curve calculation.

[0012] Optionally, the first filtering process of step S210 specifically includes: performing Gaussian filtering on the air pressure data in the original air pressure data to obtain an air pressure sequence; and performing mean filtering on the contact data in the original air pressure data to obtain a contact sequence.

[0013] Optionally, the second filtering process of step S220 includes: performing Butterworth filtering on the air pressure sequence to obtain dynamic pressure data; and performing N-order IIR filtering on the air pressure sequence to obtain static pressure data.

[0014] Optionally, the first blood pressure artificial neural network model includes a common blood pressure estimation module, and the second blood pressure artificial neural network model includes a high blood pressure estimation module and a common blood pressure estimation module.

[0015] Optionally, the step S800 specifically includes the following steps: Step S810, adjusting the preset temporary diastolic pressure amplitude coefficient according to the result of the visitor mode determination to obtain the diastolic pressure amplitude coefficient, and using the calibrated estimated mean pressure as the diastolic pressure estimated value, and reducing it by the diastolic pressure amplitude coefficient to obtain the reduced diastolic pressure estimated value; Step S820, performing offset correction on the reduced diastolic pressure estimate to obtain the diastolic pressure time; Step S830, calculating the diastolic pressure time to obtain the diastolic pressure.

[0016] According to another embodiment of the present invention, there is provided a device for executing the blood pressure estimation method, comprising: Air pressure detection module, used to detect and collect the original air pressure data of the target user, including piezoelectric micro pump, air bag and pressure sensor; The data preprocessing module is connected to the air pressure detection module, receives the original air pressure data of the target user and performs data preprocessing to obtain the original average pressure; The blood pressure artificial neural network module is connected to the data preprocessing module, receives the original average pressure obtained after preprocessing, performs pressure difference estimation, and obtains the pressure difference; The central processing unit module calculates and processes the results obtained by each module and controls each module; Data storage module, which stores the personal data of the target user and the data and results processed by each module; The data transmission module transmits the processed data and results to the external device for display according to the instructions of the central processing unit module.

[0017] According to another embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the blood pressure estimation method is implemented.

[0018] Compared with the prior art, the blood pressure estimation method, device and storage medium provided by the embodiments of the present invention have at least the following advantages.

[0019] 1. Effectively solve the problem of low stability of blood pressure estimation methods based on physiological parameters (such as PPG, ECG, etc.). Specifically, it solves the problem that individual differences and environmental factors, such as heart rate variability, vascular elasticity, etc., may lead to inaccurate blood pressure estimation, resulting in inaccurate measurement data and easy to be affected by existing non-airbag blood pressure detection methods.

[0020] 2. In the process of preprocessing the collected data, static pressure linear fitting and dynamic pressure Gaussian fitting are used respectively, and the characteristic parameters of the curve are used to characterize the blood pressure characteristics. The calculation is more accurate and has good physiological interpretability, and the final estimation result is also more accurate.

[0021] 3. The blood pressure estimation method of the present invention can collect air pressure data by means of a piezoelectric micropump and an airbag, and can be conveniently integrated on portable devices such as watches, and is highly convenient to use.

[0022] 4. The blood pressure estimation method of the present invention has high universality. In addition to wristband airbag measurement, it can also be used for pressure values ​​measured by devices such as cuff airbags to calculate accurate blood pressure values.

[0023] 5. The blood pressure estimation method of the present invention is suitable for all kinds of people and users. It distinguishes and calculates users and visitors through different calculation modes, and calculates the blood pressure of ordinary people and hypertensive people separately through different neural networks. It calculates the blood pressure of target customers and visitors, or hypertensive people and ordinary people separately, and provides more accurate results. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. The features and advantages of the present invention can be more clearly understood by referring to the drawings. The drawings are schematic and should not be understood as limiting the present invention in any way. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 A flow chart showing the main steps of a blood pressure estimation method provided according to an embodiment of the present invention.

[0026] Figure 2 A flow chart of a blood pressure estimation method provided according to an embodiment of the present invention is shown.

[0027] Figure 3 A flow chart of data preprocessing in a blood pressure estimation method provided according to an embodiment of the present invention is shown.

[0028] Figure 4 A flow chart of correcting a diastolic pressure estimation value in a blood pressure estimation method provided according to an embodiment of the present invention is shown.

[0029] Figure 5 A logic block diagram of a blood pressure estimation method provided according to an embodiment of the present invention is shown.

[0030] Figure 6 A graph of original air pressure data collected in an example of applying a blood pressure estimation method provided according to an embodiment of the present invention is shown.

[0031] Figure 7 A schematic diagram of peaks and troughs of dynamic pressure data after Butterworth filtering in an example of a blood pressure estimation method provided according to an embodiment of the present invention is shown.

[0032] Figure 8 The diagram shows an identification graph after removing pseudo peaks and pseudo troughs in an example of applying a blood pressure estimation method provided according to an embodiment of the present invention.

[0033] Fig. 9 The result diagram after data preprocessing in an example of applying the blood pressure estimation method provided according to an embodiment of the present invention is shown.

[0034] Reference numerals: 100. Blood pressure estimation device; 110. Air pressure detection module; 120. Data preprocessing module; 130. Blood pressure artificial neural network module; 140. Data storage module; 150. Data transmission module; 160. Central processing unit module. DETAILED DESCRIPTION

[0035] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0036] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0037] A blood pressure estimation method, device and storage medium provided according to an embodiment of the present invention are described in detail below with reference to the accompanying drawings.

[0038] Example 1 like Figures 1 to 4 In the shown embodiment 1, a blood pressure estimation method is provided according to an embodiment of the present invention. First, the original air pressure data of the target user is obtained, and then the original air pressure data is preprocessed. Then, the preprocessed data is input into the blood pressure artificial neural network module, and the pressure difference is obtained by calculation. Finally, the diastolic pressure and systolic pressure are obtained through the pressure difference.

[0039] Figure 1 and Figure 2 Detailed steps and processes of the blood pressure estimation method provided according to an embodiment of the present invention are shown. Figure 1 and Figure 2 The flowchart shown includes the following steps.

[0040] Step S1, collecting the original air pressure data of the measured object, and performing data preprocessing on the original air pressure data to obtain the original average pressure. The step S1 may specifically include the following steps S100 to S400. Optionally, the original air pressure data may be collected from the wrist of the measured object by using a piezoelectric micropump and an airbag. Also optionally, the original air pressure data may be collected from the arm of the measured object by using a cuff-type airbag.

[0041] Step S100, collecting raw air pressure data. Specifically, in this embodiment, the target user first wraps the blood pressure estimation device 100 around his wrist, clicks on start, and uses a piezoelectric micropump to inflate the airbag. During the inflation process, the measured value measured by the pressure sensor is the raw air pressure data of the target user. When starting to collect raw air pressure data, the user can also choose whether to use visitor mode. The measured values ​​measured by the pressure sensor may include: dynamic pressure and static pressure that constitute air pressure data. And the collected information may also include contact impedance values ​​measured by the tactile sensor to constitute contact data. Figure 6 This is a graph of air pressure data collected in an example of a blood pressure estimation method using this implementation. As shown in the figure, the curve represents the relationship between time and air pressure. The figure shows that the original air pressure data in this embodiment is pressurized smoothly, wherein the boost measurement method in the oscillometric method is used.

[0042] Step S200, data preprocessing, preprocessing the collected original air pressure data to obtain preprocessed data, the preprocessed data includes the original average pressure. Figure 3 , which shows the data preprocessing process. This data preprocessing step can improve the universality of the method and is suitable for processing and calculating the air pressure values ​​collected by all devices. It has high accuracy and the processed data calculation is more accurate. Figure 3 As shown in the flowchart, step S200 specifically includes the following steps S210 to S250.

[0043] Step S210, the collected raw air pressure data is subjected to a first filtering process to obtain an air pressure sequence and a contact sequence. The first filtering process includes: performing Gaussian filtering on the air pressure data in the raw air pressure data to obtain an air pressure sequence; and performing mean filtering on the contact data in the raw air pressure data to obtain a contact sequence. The obtained air pressure sequence and contact sequence are stored in a memory (e.g., flash). The contact sequence can be used to determine whether the airbag fits the user's wrist, realize the drift correction of the pulse wave curve, and avoid measurement errors caused by different wearing tightness.

[0044] Step S220, performing a second filtering process on the air pressure sequence to obtain dynamic pressure data and static pressure data. The second filtering process includes: performing a Butterworth filtering process on the air pressure sequence obtained in the above steps to obtain dynamic pressure data; and performing an N-order IIR (Infinite Impulse Response, recursive filter) filtering on the air pressure sequence to obtain static pressure data. Figure 7 , shows a schematic diagram of dynamic pressure data obtained by performing Butterworth filtering on an air pressure sequence in one example.

[0045] Step S230, performing Gaussian fitting on the dynamic pressure data to obtain a Gaussian curve. Figure 7 FIG. 1 is a schematic diagram of dynamic pressure data after Butterworth filtering in an example of a blood pressure estimation method of the present invention. Figure 7 As shown, the suspicious points of the peak and trough of the pulse wave are identified; Figure 8 is an identification diagram after removing pseudo peaks and pseudo troughs in an example of a blood pressure estimation method provided in an embodiment of the present invention, such as Figure 8 The following are the peaks and troughs after correction. Fig. 9 The curve shown in FIG. 1 is a schematic diagram showing a Gaussian curve drawn in one example, and Figure 7 and Figure 8 A schematic diagram of the process of performing Gaussian fitting in an example is shown. The steps of Gaussian fitting include: first, find the maximum point (maximum value) of the group of pulse wave signals, and record the amplitude and time of the point; given a known human heart rate (1-2 times / step S) and sampling rate, set the value between the two peaks; take the difference of the pulse wave signal sequence, and the position of the extreme point in the pulse wave sequence can be obtained by taking the opposite sign before and after; according to the maximum value time of the pulse wave and the set threshold, gradually search for the extreme value from the middle of the pulse wave sequence to both ends; correct the interference point; and obtain the position of all peak points. The above-mentioned pulse wave signal is a relationship diagram between the air pressure data and time measured by a pressure sensor (for example, Figure 6 as shown in Figure 2) as dynamic pressure data.

[0046] Step S240, perform static pressure straight line fitting on the static pressure data to obtain a static pressure straight line. Through static pressure straight line fitting, a mathematical model of the linear relationship between static pressure and position variables can be established. Fig. 9 The straight line shown in , shows a static pressure straight line obtained by static pressure straight line fitting in an example, and the static pressure straight line can be used to calculate the diastolic pressure and the systolic pressure.

[0047] Step S250, the original average pressure is obtained by Gaussian curve calculation. In the Gaussian curve obtained by Gaussian fitting the dynamic pressure data, the pressure value at the time corresponding to the highest point is the original average pressure.

[0048] The data preprocessing process of the above step S200 is explained as follows: the air pressure data in the collected original air pressure data is Gaussian filtered to obtain an air pressure sequence, and the contact data in the original air pressure data is mean filtered to obtain a contact sequence; then the air pressure sequence is Butterworth filtered to obtain dynamic pressure data, and the air pressure sequence is subjected to N-order IIR filtering to obtain static pressure data; Gaussian fitting is performed on the dynamic pressure data to obtain a Gaussian curve; a straight line fitting is performed on the static pressure data to obtain a static pressure straight line; the original average pressure is calculated by the air pressure value at the moment corresponding to the highest point of the Gaussian curve after Gaussian fitting.

[0049] Continue to see Figure 2, the blood pressure estimation method provided according to an embodiment of the present invention also includes the following steps.

[0050] Step S300, guest mode determination, determines whether the target user is in guest mode, if yes, determines it is guest mode and executes step S400, otherwise determines it is personal mode and executes step S310. Specifically, in this embodiment, in this step, the target user selects whether to use guest mode. Alternatively, when collecting the original air pressure data in step S100, the target user may be reminded to select whether to use guest mode.

[0051] Step S310, read the personal data and historical data of the target user. Specifically, in this embodiment, when the target user does not select the visitor mode when starting the test, the personal user mode is selected by default, and the historical data of the target user is read in this step. The personal user mode stores the personal data and historical data of the target user, wherein the historical data of the target user may include the personal basic information of the target user, and information such as whether the target user has high blood pressure, wherein the personal basic information may include: gender, age, height, weight, wrist circumference, body mass index BMI, and other information.

[0052] Step S400, data input, inputting the original average pressure into the central processing unit module.

[0053] Step S2, based on the original average pressure, the pressure difference is obtained by processing through the blood pressure artificial neural network module. The step S2 specifically includes the following steps S500 to S530.

[0054] Step S500, compare the inputted original average pressure with the first threshold value, and determine whether the original average pressure exceeds the first threshold value, if so, execute step S520, if not, execute step S510. Specifically, in this embodiment, the first threshold value can be pre-set based on the blood pressure value judgment standard of the statistical data and the user's historical data.

[0055] Step S510, import the original mean pressure into the first blood pressure artificial neural network model for calculation, obtain the pressure difference, and execute step S530. Specifically, in this embodiment, the first blood pressure artificial neural network model performs calculation processing when the original mean pressure is less than the first threshold. The first blood pressure artificial neural network model may include a common blood pressure estimation module, and a trained ANN neural network may be used. The pressure difference can be used as the difference between the original mean pressure and the estimated mean pressure.

[0056] Step S520, importing the original mean pressure into the second blood pressure artificial neural network model for calculation to obtain the pressure difference. Specifically, in this embodiment, the second blood pressure artificial neural network model performs calculation processing when the original mean pressure is greater than the first threshold. The second blood pressure artificial neural network model may include a high pressure estimation module and a normal blood pressure estimation module, and a trained ANN neural network may be used. The pressure difference can be used as the difference between the original mean pressure and the estimated mean pressure.

[0057] The above-mentioned blood pressure artificial neural network model may include 2-10 layers of neural networks, wherein each layer of the neural network may include 3-20 neurons.

[0058] In step S530, the central processing unit module receives the pressure difference and stores it in the data storage module 140. The result of step S510 or step S520 is obtained to obtain the pressure difference. By comparing the original average pressure with the first threshold value and then selecting the first blood pressure artificial neural network model or the second blood pressure artificial neural network model for processing according to the judgment result, the pressure difference can be calculated in a targeted manner according to the individual situation of the target user through the corresponding model, thereby obtaining a more accurate blood pressure estimation result.

[0059] Step S3, based on the original mean pressure and the pressure difference, the diastolic pressure and the systolic pressure are calculated as the processing result, and the processing result is stored and displayed. The step S3 specifically includes the following steps S600 to S900.

[0060] Step S600, compare the obtained pressure difference with the second threshold value, and determine whether the obtained pressure difference exceeds the second threshold value. If so, execute step S610, otherwise execute step S700. Specifically, in this embodiment, the second threshold value is pre-set according to statistical data. For example, the second threshold value can be set to 10mmHg to 125mmHg, so that when the pressure difference is less than 10mmHg, or the pressure difference is greater than 125mmHg, it is judged that it exceeds the second threshold value; and when the pressure difference is in the range of 10mmHg to 125mmHg, it is judged that it does not exceed the second threshold value. By comparing and judging the obtained pressure difference with the second threshold value, and then deciding whether to issue an error reminder or perform further processing based on the judgment result, problems in the processing process can be responded to in a timely manner to avoid invalid calculations.

[0061] Step S610: output an error, output an error prompt message to the target user, and return to step S100.

[0062] Step S700, calculating an estimated average pressure by using the obtained pressure difference and the original average pressure, and calibrating the estimated average pressure to obtain a calibrated estimated average pressure. The calibration is to convert the unlabeled data in the original data set into a structured, machine-understandable data set.

[0063] Step S800: The calibrated estimated mean pressure is used as the estimated diastolic pressure value, and is corrected and calculated to obtain the diastolic pressure. Figure 4 , shows the specific steps and process of correcting diastolic pressure in this embodiment. Figure 4 As shown in the flowchart, this step specifically includes the following steps S810 to S830.

[0064] Step S810, according to the judgment result of the visitor mode, adjust the preset temporary diastolic pressure amplitude coefficient to obtain the diastolic pressure amplitude coefficient, and use the calibrated estimated mean pressure as the diastolic pressure estimated value, and reduce it through the diastolic pressure amplitude coefficient to obtain the reduced diastolic pressure estimated value. This reduction is not a simple multiplication calculation. The temporary diastolic pressure amplitude coefficient is pre-set according to statistical data. In this embodiment, when it is judged that the target user is in visitor mode, the temporary diastolic pressure amplitude coefficient is directly used; otherwise, it is a personal user mode, and the temporary diastolic pressure amplitude coefficient can be modified according to the information preset or provided by the target user (for example, the personal data or historical data of the target user).

[0065] Step S820, offset correction is performed on the reduced diastolic pressure estimate to obtain the diastolic pressure time. Specifically, in this embodiment, the offset correction process is performed by a data analysis method. Optionally, the offset correction may include subtracting the time difference corresponding to the pressure difference from the time corresponding to the reduced estimated mean pressure to obtain the diastolic pressure time.

[0066] Step S830, calculating the time of the diastolic pressure to obtain the diastolic pressure. The calculation includes finding the static pressure value corresponding to the time of the diastolic pressure on the obtained static pressure straight line to obtain the diastolic pressure.

[0067] Step S900, based on the obtained diastolic pressure and the calibrated estimated mean pressure, the systolic pressure is calculated. Specifically, in this embodiment, the calibrated estimated mean pressure and the diastolic pressure are combined to calculate the difference, the difference is amplified by the systolic pressure accuracy coefficient, and the calibrated estimated mean pressure value is added to obtain the systolic pressure. The systolic pressure accuracy coefficient can be pre-set according to data statistics. This amplification is not a simple multiplication amplification.

[0068] The workflow of the blood pressure estimation method provided in this embodiment is as follows: first, the piezoelectric micropump of the blood pressure estimation device 100 inflates the airbag. During the inflation process, the pressure sensor and the tactile sensor measure and obtain the original air pressure data of the target user, and the original air pressure data includes air pressure data and contact data; the obtained original air pressure data is preprocessed, including filtering and fitting, to obtain the original average pressure; by comparing and judging the original average pressure with the first threshold, the first blood pressure artificial neural network model or the second blood pressure artificial neural network model is selected; then the original average pressure is input into the selected blood pressure artificial neural network model for processing to obtain the pressure difference, and then the pressure difference is compared with the second threshold to determine whether the pressure difference exceeds the range of the second threshold. If it exceeds the range, an error reminder is fed back and returned, otherwise the calibrated estimated average pressure is obtained by processing the pressure difference and the original average pressure; then the diastolic pressure is calculated by calibrating the estimated average pressure; finally, the systolic pressure is calculated using the diastolic pressure and the calibrated estimated average pressure. The obtained diastolic pressure, systolic pressure, and pulse rate results are output and displayed.

[0069] Example 2 Figure 5 1 is a logic block diagram of a blood pressure estimation device according to an embodiment of the present invention, which shows the specific components and processing process of the blood pressure estimation device 100 in this embodiment. Figure 5 , shows a blood pressure estimation device 100 for executing the blood pressure estimation method of the above-mentioned embodiment 1 provided according to another embodiment of the present invention, including: an air pressure detection module 110, used to detect and collect the original air pressure data of the target user; a data preprocessing module 120, connected to the air pressure detection module 110, receiving the original air pressure data of the target user and performing data preprocessing to obtain the original average pressure; a blood pressure artificial neural network module 130, connected to the data preprocessing module 120, receiving the original average pressure obtained after preprocessing to perform pressure difference estimation to obtain the pressure difference; a central processing unit module 160, calculating and processing the results obtained by each module, and controlling each module of the blood pressure estimation device 100; a data storage module 140, storing the personal data of the target user and the data and results obtained by processing each module; a data transmission module 150, transmitting data between modules within the device, and transmitting the processed data and results to an external device for display according to the instructions of the central processing unit module 160.

[0070] Optionally, the air pressure detection module 110 may include a piezoelectric micropump, an airbag and a pressure sensor. By using a piezoelectric micropump and a micro airbag, it is beneficial to obtain a more miniaturized blood pressure estimation device, which is convenient for integration or assembly into a miniature wearable device or medical device. Among them, the piezoelectric micropump in this embodiment can be a MEMS piezoelectric micropump. When in use, the target user first sets the air pressure detection module 110 of the blood pressure estimation device around the wrist. When starting to measure, the piezoelectric micropump is used to inflate the airbag. During the inflation process, the pressure sensor measures the original air pressure data of the target user.

[0071] like Figure 5 As shown, in this embodiment, the blood pressure estimation device 100 further includes a blood pressure artificial neural network module 130, and the blood pressure artificial neural network module 130 includes a first blood pressure artificial neural network model and a second blood pressure artificial neural network model, wherein the first blood pressure artificial neural network model includes a common blood pressure estimation module, and the second blood pressure artificial neural network model includes a high blood pressure estimation module and a common blood pressure estimation module. The blood pressure artificial neural network module 130 can be configured to use the first blood pressure artificial neural network model to calculate and estimate the blood pressure of a target user whose original average pressure is less than a first threshold, and the second blood pressure artificial neural network model to calculate and estimate the blood pressure of a target user whose original average pressure is greater than the first threshold. Optionally, the blood pressure artificial neural network used in this embodiment can be an ANN network, wherein the common blood pressure estimation module can be obtained by training the statistical data of the general population; and the high blood pressure estimation module can be obtained by training the statistical data of the high blood pressure population. By using different models for data processing and blood pressure estimation for target users with different blood pressure ranges, a more accurate estimation value can be obtained.

[0072] like Figure 5 As shown, the blood pressure estimation device 100 of this embodiment also includes a data preprocessing module 120, and the data preprocessing module 120 can call the central processing unit module 160 to perform data processing. The blood pressure estimation device 100 also includes a data storage module 140. In this embodiment, FLASH flash memory is used as the data storage module 140 to store the data obtained by each module. The blood pressure estimation device 100 also includes a data transmission module 150, and the data transmission module 150 may include wired data transmission and / or wireless data transmission. In this embodiment, the various modules inside the blood pressure estimation device 100 can transmit data via wired data, and the data transmission between the blood pressure estimation device 100 and the external device can be carried out using Bluetooth and wireless networks.

[0073] The working process of a blood pressure estimation device 100 of the present embodiment is as follows: after the target user wears the blood pressure estimation device 100 at the root of the ulnar styloid process of the wrist, he first selects to use the personal mode or the visitor mode; then clicks to start measuring blood pressure, at which time the piezoelectric micro pump of the air pressure detection module 110 in the blood pressure estimation device 100 starts to work and inflate the air bag. During this process, the pressure sensor measures and collects the original air pressure data of the target user, and inputs the collected original air pressure data into the data preprocessing module 120; the data preprocessing module 120 calls the central processing unit module 160 to perform data preprocessing to obtain the preprocessed data (i.e., the original average pressure); The preprocessed data is then compared with the first threshold value by the central processing unit module 160, and the first blood pressure artificial neural network model or the second blood pressure artificial neural network model in the blood pressure artificial neural network module 130 is selected according to the judgment result; according to the selection result, the preprocessed data is input into the blood pressure artificial neural network module 130, and the estimated pressure difference is output; the pressure difference is compared with the second threshold value, and an error reminder is executed and returned according to the judgment result, or blood pressure estimation is continued; the diastolic pressure and systolic pressure are calculated by the central processing unit module 160 using the mean pressure and the pressure difference, and the estimated results of the diastolic pressure and systolic pressure are output to the target user.

[0074] The following Table 1 shows the measurement results obtained by performing a blood pressure value estimation test 1 on an example blood pressure estimation device according to Embodiment 2 of the present invention. For the sake of brevity, only a part of the measurement results is cited here.

[0075] In this experiment 1, 255 people were randomly selected for auscultation comparison test. The auscultation method was performed by a special nurse and two people auscultated at the same time, and the average value was taken as the control group of the test. The measurement group was a control measurement, and the subjects were required to follow the standard measurement posture: sit still for more than 3 minutes; measure in the posture required by F1-PRO; keep still, do not talk, and do not move during the measurement; any measurable personnel were not excluded, and random sampling was performed; after statistical analysis of the deviations of the above measurement group and control group, the average deviation of systolic blood pressure was 0.85mmHg, the standard deviation of systolic blood pressure was 6.87mmHg, the average deviation of diastolic blood pressure was 2.41mmHg, and the standard deviation of diastolic blood pressure was 6.21mmHg. Compared with the standard document "YY 0670-2008", the results showed that the clinical test met the acceptance standards for medical-grade sphygmomanometers.

[0076] Table 1 Test data table for experiment 1

[0077] Table 2 below is a comparison table of test data of Experiment 2, in which the data of blood pressure calculation using the blood pressure estimation device according to Example 2 of the present invention and the measured data of the blood pressure measurement device of Omron Model 7121 are used as a reference to compare the test data of blood pressure estimation. The pressure difference between the measured values ​​of the blood pressure estimation device of Example 2 and Omron Model 7121 is small, and the standard deviation of the blood pressure estimation device of Example 2 is very small, indicating that the accuracy and consistency of the blood pressure estimation device of Example 2 are high.

[0078] Table 2 Comparison of test data of Experiment 2

[0079] Example 3 According to embodiment 3 of the present invention, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the method provided in embodiment 1 of the present invention can be implemented.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0081] All the above optional technical solutions can be arbitrarily combined to form optional embodiments of the present application, which will not be described one by one here.

[0082] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0083] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A blood pressure estimation method, characterized in that: The following steps are involved: Step S1, collecting the original air pressure data of the measured object, and performing data preprocessing on the original air pressure data to obtain the original average pressure; Step S2, based on the original average pressure, the pressure difference is obtained by processing through the blood pressure artificial neural network module; Step S3, based on the original mean pressure and the pressure difference, the diastolic pressure and the systolic pressure are calculated as the processing result, and the processing result is stored and displayed; Wherein, the step S1 specifically includes the following steps: Step S100, collecting original air pressure data, the collected original air pressure data includes air pressure data and contact data; Step S200, preprocessing the collected original air pressure data to obtain the original average pressure as preprocessed data; Step S300, judging the visitor mode, judging whether the target user is in the visitor mode according to the target user's selection, if yes, judging it as the visitor mode and executing step S400, otherwise judging it as the personal mode and executing step S310; Step S310, reading the personal data and historical data of the target user; Step S400, inputting the original average pressure into the central processing unit module.

2. The blood pressure estimation method according to claim 1, characterized in that: The step S2 specifically includes the following steps: Step S500, comparing the inputted original average pressure with the first threshold, and determining whether the original average pressure exceeds the first threshold, if so, executing step S520, otherwise executing step S510; Step S510, inputting the original average pressure into the first blood pressure artificial neural network model, outputting the pressure difference after calculation, and executing step S530; Step S520, inputting the original average pressure into the second blood pressure artificial neural network model, and outputting the pressure difference after calculation; Step S530, input the pressure difference into the central processing module and store it in the data storage module.

3. The blood pressure estimation method according to claim 2, characterized in that: The step S3 specifically comprises the following steps: Step S600, comparing the obtained pressure difference with the second threshold value to determine whether the obtained pressure difference exceeds the second threshold value, if so, executing step S610, otherwise executing step S700; Step S610, outputting an error, outputting an error prompt message to the target user, and returning to step S100; Step S700, calculating an estimated average pressure by using the obtained pressure difference and the original average pressure, and calibrating the estimated average pressure to obtain a calibrated estimated average pressure; Step S800, taking the obtained calibrated estimated mean pressure as the estimated value of diastolic pressure, correcting and calculating to obtain the diastolic pressure; Step S900, based on the obtained diastolic pressure and the calibrated estimated mean pressure, the systolic pressure is calculated, and the obtained diastolic pressure and systolic pressure are stored as processing results and output and displayed.

4. The blood pressure estimation method according to claim 1, characterized in that: The step S200 further includes the following steps: Step S210, performing a first filtering process on the collected raw air pressure data to obtain an air pressure sequence and a contact sequence; Step S220, performing a second filtering process on the air pressure sequence to obtain dynamic pressure data and static pressure data; Step S230, performing Gaussian fitting on the dynamic pressure data to obtain a Gaussian curve; Step S240, performing static pressure straight line fitting on the static pressure data to obtain a static pressure straight line; Step S250, obtaining the original average pressure through Gaussian curve calculation.

5. The blood pressure estimation method according to claim 4, characterized in that: The first filtering process of step S210 specifically includes: performing Gaussian filtering on the air pressure data in the original air pressure data to obtain an air pressure sequence; and performing mean filtering on the contact data in the original air pressure data to obtain a contact sequence.

6. The blood pressure estimation method according to claim 4, characterized in that: The second filtering process of step S220 includes: performing Butterworth filtering on the air pressure sequence to obtain dynamic pressure data; and performing N-order IIR filtering on the air pressure sequence to obtain static pressure data.

7. The blood pressure estimation method according to claim 2, characterized in that: The first blood pressure artificial neural network model includes a common blood pressure estimation module, and the second blood pressure artificial neural network model includes a high blood pressure estimation module and a common blood pressure estimation module.

8. The blood pressure estimation method according to claim 3, characterized in that: The step S800 specifically includes the following steps: Step S810, adjusting the preset temporary diastolic pressure amplitude coefficient according to the result of the visitor mode determination to obtain the diastolic pressure amplitude coefficient, and using the calibrated estimated mean pressure as the diastolic pressure estimated value, and reducing it by the diastolic pressure amplitude coefficient to obtain the reduced diastolic pressure estimated value; Step S820, performing offset correction on the reduced diastolic pressure estimate to obtain the diastolic pressure time; Step S830, calculating the diastolic pressure time to obtain the diastolic pressure.

9. A device for executing the blood pressure estimation method according to any one of claims 1 to 8, characterized in that: include: Air pressure detection module, used to detect and collect the original air pressure data of the target user, including piezoelectric micro pump, air bag and pressure sensor; The data preprocessing module is connected to the air pressure detection module, receives the original air pressure data of the target user and performs data preprocessing to obtain the original average pressure; The blood pressure artificial neural network module is connected to the data preprocessing module, receives the original average pressure obtained after preprocessing, performs pressure difference estimation, and obtains the pressure difference; The central processing unit module calculates and processes the results obtained by each module and controls each module; Data storage module, which stores the personal data of the target user and the data and results processed by each module; The data transmission module transmits the processed data and results to the external device for display according to the instructions of the central processing unit module.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the blood pressure estimation method according to any one of claims 1 to 8 is implemented.

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