A blood pressure measurement method and device combining pulse wave characteristic data
By combining pulse wave characteristic data, the maximum slope of the rising edge and the waveform area of the systolic period are added as auxiliary methods for blood pressure estimation, and comprehensive decisions are made, which solves the problem of poor anti-interference in traditional oscilloscope detection methods and improves the adaptability and accuracy of blood pressure measurement.
Patent Information
- Application Number
- CN202110137351.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2021-02-01
- Publication Date
- 2025-04-25
- Estimated Expiration
- 2041-02-01
AI Technical Summary
Traditional oscilloscope detection methods have poor anti-interference when estimating blood pressure and are susceptible to factors such as breathing, movement or mood swings, resulting in inaccurate measurements.
Combined with the characteristic data of pulse waves, two measurement methods are added: estimate the correspondence between the maximum slope of the rising edge of the pulse wave and the cuff pressure, and estimate the correspondence between the waveform area and cuff pressure during the systolic period of the pulse wave, and make a comprehensive decision through three sets of measurement results to obtain the final blood pressure measurement result.
Improves the adaptability and accuracy of blood pressure measurements and reduces measurement errors caused by interference factors.
Smart Images
Figure CN114831608B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of data processing, and in particular to a blood pressure measurement method and device combining pulse wave characteristic data. Background Art
[0002] Blood pressure is an important physiological parameter that reflects the state of the cardiovascular system. Clinically, blood pressure measurement techniques can be divided into direct and indirect methods. The direct measurement method is to insert a catheter connected to a pressure sensor directly into the aorta or heart to detect blood pressure signals. This measurement method obtains the most accurate blood pressure data, but this method will cause trauma to the measured object, so it is only suitable for critically ill patients. The commonly used indirect measurement method is the oscillometric detection method, which estimates blood pressure data through the corresponding relationship between systolic pressure, diastolic pressure and cuff pressure. The measurement process of the traditional oscillometric detection method is to tie an inflatable cuff on the arm of the test subject, and raise the cuff pressure by inflating the cuff. After the cuff pressure is higher than the systolic pressure threshold to block the arterial blood flow, the cuff is slowly deflated in stages until the cuff pressure is lower than the diastolic pressure. During the entire deflation process, the cuff pressure is detected by the pressure sensing device, and the pulse wave is obtained by the pulse wave sensing device. Finally, the cuff pressure corresponding to the maximum amplitude of the pulse wave during the deflation process is used as the average pressure data estimation result, and then the corresponding systolic and diastolic pressure data are further estimated based on the empirical coefficients of the average pressure, systolic pressure, and diastolic pressure.
[0003] In the measurement process of the traditional oscillometric detection method, because the blood pressure data is estimated only based on the corresponding relationship between the pulse wave amplitude and the cuff pressure, the anti-interference ability of the estimation is poor. For example, when the pulse wave baseline drifts due to respiratory problems, or when the pulse wave amplitude changes or the duration ratio of the systolic period to the diastolic period of the pulse wave changes due to motion interference or emotional fluctuations, the traditional oscillometric detection method will have inaccurate estimation. Summary of the invention
[0004] The purpose of the present invention is to address the defects of the prior art and provide a blood pressure measurement method, device, electronic device, computer program product and computer-readable storage medium that combine pulse wave characteristic data. In addition to the traditional measurement method of estimating based on the correspondence between the pulse wave amplitude and the cuff pressure, two more measurement methods are added: a measurement method of estimating based on the correspondence between the maximum slope of the pulse wave rising edge and the cuff pressure, and a measurement method of estimating based on the correspondence between the pulse wave systolic waveform area and the cuff pressure. Finally, a comprehensive decision is made based on the three groups of measurement results to obtain the final blood pressure measurement result; thereby, while improving the adaptability of blood pressure measurement, the purpose of improving the accuracy of blood pressure measurement is also achieved.
[0005] To achieve the above object, a first aspect of an embodiment of the present invention provides a blood pressure measurement method combined with pulse wave characteristic data, the method comprising:
[0006] Acquire a plurality of first cuff pressure data and corresponding first pulse wave data;
[0007] For each of the first pulse wave data, pulse wave characteristics and reliability analysis processing are performed to generate a corresponding first characteristic data set; the first characteristic data set includes first characteristic amplitude data, first characteristic slope data, first characteristic area data and first reliability coefficient, second reliability coefficient, and third reliability coefficient;
[0008] Calculate the sum of all the first reliability coefficients to generate a first coefficient; calculate the sum of all the second reliability coefficients to generate a second coefficient; calculate the sum of all the third reliability coefficients to generate a third coefficient;
[0009] Generate a first trend curve reflecting the change of characteristic amplitude with cuff pressure according to the correspondence between all the first cuff pressure data and all the first characteristic amplitude data; and generate a second trend curve reflecting the change of characteristic slope with cuff pressure according to the correspondence between all the first cuff pressure data and all the first characteristic slope data; and generate a third trend curve reflecting the change of characteristic area with cuff pressure according to the correspondence between all the first cuff pressure data and all the first characteristic area data;
[0010] According to the first trend curve, a first blood pressure estimation process is performed to generate first mean pressure data, first systolic pressure data, and first diastolic pressure data; and according to the second trend curve, a second blood pressure estimation process is performed to generate second mean pressure data, second systolic pressure data, and second diastolic pressure data; and according to the third trend curve, a third blood pressure estimation process is performed to generate third mean pressure data, third systolic pressure data, and third diastolic pressure data;
[0011] Blood pressure decision processing is performed based on the first, second, and third coefficients, the first, second, and third average pressure data, the first, second, and third systolic pressure data, and the first, second, and third diastolic pressure data to obtain blood pressure measurement result data.
[0012] Preferably, the pulse wave feature and reliability analysis processing is performed on each of the first pulse wave data to generate a corresponding first feature data set, specifically including:
[0013] For each of the first pulse wave data, pulse wave amplitude difference feature extraction processing is performed to generate the first feature amplitude data; and pulse wave reliability analysis processing is performed based on the pulse wave amplitude difference to generate the first reliability coefficient;
[0014] For each of the first pulse wave data, a rising edge maximum slope feature extraction process is performed to generate the first characteristic slope data; and a pulse wave reliability analysis process is performed based on the rising edge maximum slope to generate the second reliability coefficient;
[0015] For each of the first pulse wave data, a pulse systolic waveform area feature extraction process is performed to generate the first feature area data; and a pulse wave reliability analysis process is performed based on the pulse systolic waveform area to generate the third reliability coefficient;
[0016] The first characteristic data set is composed of the first characteristic amplitude data, the first characteristic slope data, the first characteristic area data, the first reliability coefficient, the second reliability coefficient, and the third reliability coefficient.
[0017] Preferably, generating a first trend curve reflecting the change of the characteristic amplitude with the cuff pressure according to the correspondence between all the first cuff pressure data and all the first characteristic amplitude data specifically includes:
[0018] A first data group is composed of each first cuff pressure data and the corresponding first characteristic amplitude data, and a first data group sequence is composed of all the first data groups; and the first data group sequence is subjected to envelope fitting processing to obtain the first trend curve that can reflect the change of the characteristic amplitude with the cuff pressure.
[0019] Preferably, generating a second trend curve reflecting the change of the characteristic slope with the cuff pressure according to the correspondence between all the first cuff pressure data and all the first characteristic slope data specifically includes:
[0020] A second data group is formed by each of the first cuff pressure data and the corresponding first characteristic slope data, and a second data group sequence is formed by all of the second data groups; and envelope fitting processing is performed on the second data group sequence to obtain the second trend curve that can reflect the change of the characteristic slope with the cuff pressure.
[0021] Preferably, generating a third trend curve reflecting the change of the characteristic area with the cuff pressure according to the correspondence between all the first cuff pressure data and all the first characteristic area data specifically includes:
[0022] A third data group is formed by each of the first cuff pressure data and the corresponding first characteristic area data, and a third data group sequence is formed by all of the third data groups; and envelope fitting processing is performed on the third data group sequence to obtain the third trend curve that can reflect the change of the characteristic area with the cuff pressure.
[0023] Preferably, performing a first blood pressure estimation process according to the first trend curve to generate first mean pressure data, first systolic pressure data and first diastolic pressure data specifically includes:
[0024] In the first trend curve that can reflect the change of characteristic amplitude with cuff pressure, the cuff pressure at the location of the maximum characteristic amplitude is extracted as the first average pressure data; and on the half side of the curve where the cuff pressure is higher than the first average pressure data, the cuff pressure at the location of the maximum characteristic amplitude *Rs1 is extracted as the first systolic pressure data; and on the half side of the curve where the cuff pressure is lower than the first average pressure data, the cuff pressure at the location of the maximum characteristic amplitude *Rd1 is extracted as the first diastolic pressure data; wherein Rs1 is the first systolic pressure coefficient and Rd1 is the first diastolic pressure coefficient.
[0025] Preferably, performing a second blood pressure estimation process according to the second trend curve to generate second mean pressure data, second systolic pressure data and second diastolic pressure data specifically includes:
[0026] In the second trend curve that can reflect the change of the characteristic slope with the cuff pressure, the cuff pressure at the location where the characteristic slope is the maximum characteristic slope value is extracted as the second average pressure data; and on the half side of the curve where the cuff pressure is higher than the second average pressure data, the cuff pressure at the location where the characteristic slope is the maximum characteristic slope value *Rs2 is extracted as the second systolic pressure data; and on the half side of the curve where the cuff pressure is lower than the second average pressure data, the cuff pressure at the location where the characteristic slope is the maximum characteristic slope value *Rd2 is extracted as the second diastolic pressure data; wherein Rs2 is the second systolic pressure coefficient and Rd2 is the second diastolic pressure coefficient.
[0027] Preferably, performing a third blood pressure estimation process according to the third trend curve to generate third mean pressure data, third systolic pressure data and third diastolic pressure data specifically includes:
[0028] In the third trend curve that can reflect the change of characteristic area with cuff pressure, the cuff pressure at which the characteristic area is the maximum characteristic area value is extracted as the third average pressure data; and on the half side of the curve where the cuff pressure is higher than the third average pressure data, the cuff pressure at which the characteristic area is the maximum characteristic area value *Rs3 is extracted as the third systolic pressure data; and on the half side of the curve where the cuff pressure is lower than the third average pressure data, the cuff pressure at which the characteristic area is the maximum characteristic area value *Rd3 is extracted as the third diastolic pressure data; wherein Rs3 is the third systolic pressure coefficient and Rd3 is the third diastolic pressure coefficient.
[0029] Preferably, the blood pressure decision processing is performed according to the first, second, and third coefficients, the first, second, and third average pressure data, the first, second, and third systolic pressure data, and the first, second, and third diastolic pressure data to obtain the blood pressure measurement result data, specifically including:
[0030] According to the average pressure decision calculation formula, the decision average pressure data = (first average pressure data * first coefficient + second average pressure data * second coefficient + third average pressure data * third coefficient) / (first coefficient + second coefficient + third coefficient), the decision average pressure data is calculated and generated;
[0031] According to the systolic pressure decision calculation formula, the decision systolic pressure data = (first systolic pressure data * first coefficient + second systolic pressure data * second coefficient + third systolic pressure data * third coefficient) / (first coefficient + second coefficient + third coefficient), the decision systolic pressure data is calculated and generated;
[0032] According to the diastolic pressure decision calculation formula, the decision diastolic pressure data = (first diastolic pressure data * first coefficient + second diastolic pressure data * second coefficient + third diastolic pressure data * third coefficient) / (first coefficient + second coefficient + third coefficient), the decision diastolic pressure data is calculated and generated;
[0033] The blood pressure measurement result data is composed of the decided mean pressure data, the decided systolic pressure data and the decided diastolic pressure data.
[0034] A second aspect of an embodiment of the present invention provides a blood pressure measurement device combined with pulse wave characteristic data, comprising:
[0035] The acquisition module is used to acquire a plurality of first cuff pressure data and corresponding first pulse wave data;
[0036] The first data processing module is used to perform pulse wave feature and reliability analysis processing on each of the first pulse wave data to generate a corresponding first feature data set; the first feature data set includes first feature amplitude data, first feature slope data, first feature area data and first reliability coefficient, second reliability coefficient, and third reliability coefficient;
[0037] The second data processing module is used to calculate the sum of all the first reliability coefficients to generate a first coefficient; calculate the sum of all the second reliability coefficients to generate a second coefficient; calculate the sum of all the third reliability coefficients to generate a third coefficient;
[0038] The third data processing module is used to generate a first trend curve reflecting the change of characteristic amplitude with cuff pressure according to the corresponding relationship between all the first cuff pressure data and all the first characteristic amplitude data; and to generate a second trend curve reflecting the change of characteristic slope with cuff pressure according to the corresponding relationship between all the first cuff pressure data and all the first characteristic slope data; and to generate a third trend curve reflecting the change of characteristic area with cuff pressure according to the corresponding relationship between all the first cuff pressure data and all the first characteristic area data;
[0039] The blood pressure estimation processing module is used to perform a first blood pressure estimation process according to the first trend curve to generate first mean pressure data, first systolic pressure data and first diastolic pressure data; and to perform a second blood pressure estimation process according to the second trend curve to generate second mean pressure data, second systolic pressure data and second diastolic pressure data; and to perform a third blood pressure estimation process according to the third trend curve to generate third mean pressure data, third systolic pressure data and third diastolic pressure data;
[0040] The blood pressure decision processing module is used to perform blood pressure decision processing based on the first, second, and third coefficients, the first, second, and third average pressure data, the first, second, and third systolic pressure data, and the first, second, and third diastolic pressure data to obtain blood pressure measurement result data.
[0041] A third aspect of an embodiment of the present invention provides an electronic device, including: a memory, a processor, and a transceiver;
[0042] The processor is used to couple with the memory, read and execute instructions in the memory, so as to implement the method steps described in the first aspect above;
[0043] The transceiver is coupled to the processor, and the processor controls the transceiver to send and receive messages.
[0044] A fourth aspect of an embodiment of the present invention provides a computer program product, which includes a computer program code. When the computer program code is executed by a computer, the computer executes the method described in the first aspect.
[0045] A fifth aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions. When the computer instructions are executed by a computer, the computer executes the method described in the first aspect above.
[0046] The embodiments of the present invention provide a blood pressure measurement method, device, electronic device, computer program product and computer-readable storage medium combined with pulse wave characteristic data. In addition to the traditional measurement method of estimating based on the correspondence between the pulse wave amplitude and the cuff pressure, two auxiliary measurement methods are added: a measurement method of estimating based on the correspondence between the maximum slope of the pulse wave rising edge and the cuff pressure, and a measurement method of estimating based on the correspondence between the pulse wave systolic waveform area and the cuff pressure. Finally, a comprehensive decision is made based on the three groups of measurement results to obtain the final blood pressure measurement result. In this way, the adaptability of blood pressure measurement can be improved, and the accuracy of blood pressure measurement can also be improved. BRIEF DESCRIPTION OF THE DRAWINGS
[0047] Figure 1 A schematic diagram of a blood pressure measurement method combined with pulse wave characteristic data provided in Embodiment 1 of the present invention;
[0048] Figure 2 A schematic diagram of cuff pressure and pulse wave provided in Embodiment 1 of the present invention;
[0049] Figure 3a A schematic diagram of a first trend curve provided in Embodiment 1 of the present invention;
[0050] Figure 3b A schematic diagram of a second trend curve provided in Embodiment 1 of the present invention;
[0051] Figure 3c A schematic diagram of a third trend curve provided in Embodiment 1 of the present invention;
[0052] Figure 4 A module structure diagram of a blood pressure measurement device combined with pulse wave characteristic data provided in the second embodiment of the present invention;
[0053] Figure 5 A schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION
[0054] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0055] Before measuring the blood pressure of the test subject, an inflatable cuff is first tied to the arm of the test subject, and the cuff pressure is raised by inflating the cuff. After the cuff pressure is higher than the systolic pressure threshold and the arterial blood flow is blocked, the inflation is stopped, and then the cuff is slowly deflated in stages until the cuff pressure is lower than the diastolic pressure. During the entire deflation process, the cuff pressure data of each stage, that is, the first cuff pressure data, can be obtained through the pressure sensing device, and the waveform sampling data of the pulse wave, that is, the first pulse wave data corresponding to the first cuff pressure data of each stage can be obtained through the pulse wave sensing device. After obtaining multiple first cuff pressure data and corresponding first pulse wave data, blood pressure measurement can be performed using a blood pressure measurement method combined with pulse wave characteristic data provided in Example 1 of the present invention. Figure 1 A schematic diagram of a blood pressure measurement method combining pulse wave characteristic data provided in the first embodiment of the present invention is shown in FIG. Figure 1 As shown, this method mainly includes the following steps:
[0056] Step 1: Acquire a plurality of first cuff pressure data and corresponding first pulse wave data.
[0057] Here, if Figure 2 As shown in the schematic diagram of cuff pressure and pulse wave provided in the first embodiment of the present invention, because the cuff pressure in each stage is stable when the cuff is slowly deflated in stages, the first cuff pressure data used to characterize the cuff pressure in each stage is a specific pressure value; because each first pulse wave data is generated by continuously sampling all pulse waves within the stage time in each deflation stage, the first pulse wave data should specifically be a data sequence, which is composed of multiple pulse wave sampling data, and each sampling data corresponds to a pulse wave amplitude at a sampling time point.
[0058] Step 2, performing pulse wave feature and reliability analysis on each first pulse wave data to generate a corresponding first feature data set;
[0059] The first characteristic data set includes first characteristic amplitude data, first characteristic slope data, first characteristic area data, and first reliability coefficient, second reliability coefficient, and third reliability coefficient;
[0060] Specifically, the method comprises: step 21, performing pulse wave amplitude difference feature extraction processing on each first pulse wave data to generate first feature amplitude data; and performing pulse wave reliability analysis processing based on the pulse wave amplitude difference to generate a first reliability coefficient;
[0061] Here, when performing pulse wave amplitude difference feature extraction processing, firstly, the pulse wave sampling data in the first pulse wave data is subjected to pulse wave recognition processing to obtain multiple sub-pulse wave data sequences; then, the peak and trough position recognition processing is performed on each sub-pulse wave data sequence, and the absolute difference between the pulse wave amplitudes of the two sampling data corresponding to the identified peak and trough positions is used as the sub-pulse wave amplitude difference data of the pulse wave corresponding to the current sub-pulse wave data sequence; and then, the obtained multiple sub-pulse wave amplitude difference data are subjected to feature value processing, such as mean processing. , weighted mean processing, etc., can obtain the first characteristic amplitude data; when performing pulse wave reliability analysis based on the pulse wave amplitude difference, in the data sequence composed of the above-mentioned multiple sub-pulse wave amplitude difference data, multi-order difference calculation is performed on adjacent sub-pulse wave amplitude difference data, and according to whether the difference result deviates from the preset threshold range, it is determined whether multiple pulse waves in the first pulse wave data have obvious baseline drift, and a first reliability coefficient is obtained according to the degree of baseline drift. The higher the first reliability coefficient, the less obvious the baseline drift and the higher the waveform reliability;
[0062] Step 22, performing rising edge maximum slope feature extraction processing on multiple pulse wave waveforms in each first pulse wave data to generate first characteristic slope data; and performing pulse wave reliability analysis processing based on the rising edge maximum slope to generate a second reliability coefficient;
[0063] Here, when performing the rising edge maximum slope feature extraction processing, firstly, the pulse wave rising edge recognition processing is performed on the pulse wave sampling data in the first pulse wave data to obtain multiple sub-pulse wave rising edge sequences; then, the slope is calculated according to the adjacent pulse wave sampling data in each sub-pulse wave rising edge sequence to obtain multiple slope data, and the maximum value is extracted therefrom as the sub-pulse wave maximum slope value corresponding to the sub-pulse wave rising edge sequence; then, the obtained multiple sub-pulse wave maximum slope values are subjected to feature value processing, such as mean processing, weighted mean processing, etc. processing, etc., to obtain the first characteristic slope data; when performing pulse wave reliability analysis based on the maximum slope of the rising edge, in the data sequence composed of the above-mentioned multiple sub-pulse wave maximum slope values, multi-order difference calculation is performed on the maximum slope values of adjacent sub-pulse waves, and according to whether the difference result deviates from the preset threshold range, it is determined whether the multiple pulse waves in the first pulse wave data have obvious amplitude deformation, and the second reliability coefficient is obtained according to the degree of amplitude deformation. The higher the second reliability coefficient, the less obvious the amplitude deformation is and the higher the waveform reliability is;
[0064] Step 23, performing pulse systolic waveform area feature extraction processing on multiple pulse wave waveforms in each first pulse wave data to generate first feature area data; and performing pulse wave reliability analysis processing based on the pulse systolic waveform area to generate a third reliability coefficient;
[0065] Here, when performing pulse systolic waveform area feature extraction processing, firstly, the first pulse wave data is subjected to pulse wave recognition processing to obtain multiple sub-pulse wave sequences; then, the start and end positions of the pulse wave systolic period are identified for each sub-pulse wave sequence, and the pulse wave sampling data between the start and end positions are extracted as the sub-pulse wave systolic period data sequence; then, the waveform area of each sub-pulse wave systolic period data sequence is estimated, and then the sub-pulse wave systolic period waveform area value of each sub-pulse wave is obtained; and then, feature value processing is performed on all the obtained sub-pulse wave systolic period waveform area values. , such as mean processing, weighted mean processing, etc., can obtain the first characteristic area data; when performing pulse wave reliability analysis based on the pulse systolic waveform area, in the data sequence composed of the above-mentioned multiple sub-pulse wave systolic waveform area values, multi-order difference calculation is performed on the adjacent sub-pulse wave systolic waveform area values, and according to whether the difference result deviates from the preset threshold range, it is determined whether the multiple pulse waves in the first pulse wave data have obvious width deformation, and a third reliability coefficient is obtained according to the degree of width deformation. The higher the third reliability coefficient, the less obvious the width deformation and the higher the waveform reliability;
[0066] Step 24, forming a first characteristic data set from the first characteristic amplitude data, the first characteristic slope data, the first characteristic area data and the first reliability coefficient, the second reliability coefficient and the third reliability coefficient.
[0067] Step 3, calculate the sum of all first reliability coefficients to generate the first coefficient; calculate the sum of all second reliability coefficients to generate the second coefficient; calculate the sum of all third reliability coefficients to generate the third coefficient.
[0068] Here, the first coefficient is the sum of the pulse wave baseline drift reliability coefficients of all deflation stages in the current measurement cycle, the second coefficient is the sum of the pulse wave amplitude deformation reliability coefficients of all deflation stages, and the third coefficient is the sum of the pulse wave width deformation reliability coefficients of all deflation stages. The higher each sum is, the higher the corresponding reliability of all pulse waves obtained in the current measurement cycle is.
[0069] For example, Figure 2 As shown, in step 1, the first cuff pressure data and the corresponding first pulse wave data of the six deflation stages are obtained, and then six groups of first characteristic data sets can be obtained through calculation in step 2. The first coefficient should be the cumulative sum of the six first reliability coefficients in the six groups of first characteristic data sets, the second coefficient should be the cumulative sum of the six second reliability coefficients, and the third coefficient should be the cumulative sum of the six third reliability coefficients.
[0070] Step 4, generating a first trend curve reflecting the change of characteristic amplitude with cuff pressure according to the correspondence between all first cuff pressure data and all first characteristic amplitude data; generating a second trend curve reflecting the change of characteristic slope with cuff pressure according to the correspondence between all first cuff pressure data and all first characteristic slope data; generating a third trend curve reflecting the change of characteristic area with cuff pressure according to the correspondence between all first cuff pressure data and all first characteristic area data;
[0071] Specifically, the method comprises: step 41, forming a first data group from each first cuff pressure data and the corresponding first characteristic amplitude data, and forming a first data group sequence from all the first data groups; and performing envelope fitting processing on the first data group sequence to obtain a first trend curve that can reflect the change of the characteristic amplitude with the cuff pressure;
[0072] For example, Figure 2 As shown, in step 1, the first cuff pressure data of 6 deflation stages and the corresponding 6 first pulse wave data are obtained. Each first pulse wave data can obtain a first characteristic amplitude data, so the first data group sequence should be [(the 1st first cuff pressure data, the 1st first characteristic amplitude data), (the 2nd first cuff pressure data, the 2nd first characteristic amplitude data), (the 3rd first cuff pressure data, the 3rd first characteristic amplitude data), (the 4th first cuff pressure data, the 4th first characteristic amplitude data), (the 5th first cuff pressure data, the 5th first characteristic amplitude data), (the 6th first cuff pressure data, the 6th first characteristic amplitude data)], with the cuff pressure as the horizontal axis and the characteristic amplitude as the vertical axis, the first data group sequence is subjected to envelope fitting processing, and the obtained fitting curve is the first trend curve that can reflect the change of the characteristic amplitude with the cuff pressure, as shown in FIG. Figure 3a As shown in the first trend curve schematic diagram provided in the first embodiment of the present invention, point 11 The coordinates of are (the first cuff pressure data, the first characteristic amplitude data), point 12 The coordinates of point are (the second first cuff pressure data, the second first characteristic amplitude data), and so on. 16 The coordinates are (the sixth first cuff pressure data, the sixth first characteristic amplitude data); wherein, from the first first cuff pressure data to the sixth first cuff pressure data, the specific cuff pressure values of the cuff pressure data are in a decreasing trend;
[0073] Step 42, each first cuff pressure data and the corresponding first characteristic slope data form a second data group, and all the second data groups form a second data group sequence; and the second data group sequence is subjected to envelope fitting processing to obtain a second trend curve reflecting the characteristic slope changing with the cuff pressure;
[0074] For example, Figure 2As shown, in step 1, the first cuff pressure data of 6 deflation stages and the corresponding 6 first pulse wave data are obtained. Each first pulse wave data can obtain a first characteristic slope data, so the second data group sequence should be [(the 1st first cuff pressure data, the 1st first characteristic slope data), (the 2nd first cuff pressure data, the 2nd first characteristic slope data), (the 3rd first cuff pressure data, the 3rd first characteristic slope data), (the 4th first cuff pressure data, the 4th first characteristic slope data), (the 5th first cuff pressure data, the 5th first characteristic slope data), (the 6th first cuff pressure data, the 6th first characteristic slope data)], with the cuff pressure as the horizontal axis and the characteristic slope as the vertical axis, the second data group sequence is subjected to envelope fitting processing, and the obtained fitting curve is the second trend curve that can reflect the change of the characteristic slope with the cuff pressure, as shown in FIG. Figure 3b As shown in the second trend curve schematic diagram provided in the first embodiment of the present invention, point 21 The coordinates of are (the first cuff pressure data, the first characteristic slope data), point 22 The coordinates of point are (the second first cuff pressure data, the second first characteristic slope data), and so on. 26 The coordinates are (the sixth first cuff pressure data, the sixth first characteristic slope data); wherein, from the first first cuff pressure data to the sixth first cuff pressure data, the specific cuff pressure values of the cuff pressure data are in a decreasing trend;
[0075] Step 43, each first cuff pressure data and the corresponding first characteristic area data form a third data group, and all the third data groups form a third data group sequence; and the third data group sequence is subjected to envelope fitting processing to obtain a third trend curve that can reflect the change of the characteristic area with the cuff pressure.
[0076] For example, Figure 2 As shown, in step 1, the first cuff pressure data of 6 deflation stages and the corresponding 6 first pulse wave data are obtained. Each first pulse wave data can obtain a first characteristic area data, so the third data group sequence should be [(the 1st first cuff pressure data, the 1st first characteristic area data), (the 2nd first cuff pressure data, the 2nd first characteristic area data), (the 3rd first cuff pressure data, the 3rd first characteristic area data), (the 4th first cuff pressure data, the 4th first characteristic area data), (the 5th first cuff pressure data, the 5th first characteristic area data), (the 6th first cuff pressure data, the 6th first characteristic area data)], with the cuff pressure as the horizontal axis and the characteristic area as the vertical axis, the third data group sequence is subjected to envelope fitting processing, and the obtained fitting curve is the third trend curve that can reflect the change of the characteristic area with the cuff pressure, as shown in FIG. Figure 3cAs shown in the third trend curve schematic diagram provided in the first embodiment of the present invention, point 31 The coordinates of point are (the first cuff pressure data, the first characteristic area data), 32 The coordinates of point are (the second first cuff pressure data, the second first characteristic area data), and so on. 36 The coordinates are (the 6th first cuff pressure data, the 6th first characteristic area data); wherein, from the 1st first cuff pressure data to the 6th first cuff pressure data, the specific cuff pressure values of the cuff pressure data are in a decreasing trend.
[0077] Step 5, performing a first blood pressure estimation process according to the first trend curve to generate first mean pressure data, first systolic pressure data, and first diastolic pressure data; and performing a second blood pressure estimation process according to the second trend curve to generate second mean pressure data, second systolic pressure data, and second diastolic pressure data; and performing a third blood pressure estimation process according to the third trend curve to generate third mean pressure data, third systolic pressure data, and third diastolic pressure data;
[0078] Specifically, step 51, in a first trend curve that can reflect the change of characteristic amplitude with cuff pressure, extracting the cuff pressure at the point where the characteristic amplitude is the maximum characteristic amplitude as the first average pressure data; and extracting the characteristic amplitude as the maximum characteristic amplitude * Rs on the half side of the curve where the cuff pressure is higher than the first average pressure data 1- The cuff pressure at the position where the cuff pressure is lower than the first average pressure data is taken as the first systolic pressure data; and on the half-side curve where the cuff pressure is lower than the first average pressure data, the cuff pressure at the position where the characteristic amplitude is the maximum characteristic amplitude *Rd1 is extracted as the first diastolic pressure data;
[0079] Among them, Rs1 is the first systolic pressure coefficient, and Rd1 is the first diastolic pressure coefficient.
[0080] Here, the first systolic pressure coefficient Rs1 and the first diastolic pressure coefficient Rd1 are conventional corresponding coefficients of systolic pressure and diastolic pressure, which can be obtained by querying a preset blood pressure coefficient table;
[0081] For example, the first trend curve is Figure 3a As shown, point 14 The characteristic amplitude at is the maximum characteristic amplitude, and the point 11 The characteristic amplitude at is the maximum characteristic amplitude*Rs1, and the point 16 The characteristic amplitude at is the maximum characteristic amplitude * Rd1, then the first average pressure data is point 14 The cuff pressure data, the first systolic pressure data is point 11 The cuff pressure data, the first diastolic pressure data is point 16 Cuff pressure data;
[0082] Step 52, in the second trend curve that can reflect the change of the characteristic slope with the cuff pressure, extract the cuff pressure at the position where the characteristic slope is the maximum characteristic slope value as the second average pressure data; and on the half side of the curve where the cuff pressure is higher than the second average pressure data, extract the cuff pressure at the position where the characteristic slope is the maximum characteristic slope value *Rs2 as the second systolic pressure data; and on the half side of the curve where the cuff pressure is lower than the second average pressure data, extract the cuff pressure at the position where the characteristic slope is the maximum characteristic slope value *Rd2 as the second diastolic pressure data;
[0083] Among them, Rs2 is the second systolic pressure coefficient, and Rd2 is the second diastolic pressure coefficient;
[0084] Here, the second systolic pressure coefficient Rs2 and the second diastolic pressure coefficient Rd2 are conventional corresponding coefficients of systolic pressure and diastolic pressure, which can be obtained by querying a preset blood pressure coefficient table;
[0085] For example, the second trend curve is Figure 3b As shown, point 24 The characteristic slope at is the maximum characteristic slope value, and the point 21 The characteristic slope at is the maximum characteristic slope value*Rs2, point 26 The characteristic slope at is the maximum characteristic slope value*Rd2, so the second average pressure data is point 24 The cuff pressure data is the second systolic pressure data. 21 The cuff pressure data is the second diastolic pressure data. 26 Cuff pressure data;
[0086] Step 53, in the third trend curve that can reflect the change of the characteristic area with the cuff pressure, extract the cuff pressure at the place where the characteristic area is the maximum characteristic area value as the third average pressure data; and on the half side of the curve where the cuff pressure is higher than the third average pressure data, extract the cuff pressure at the place where the characteristic area is the maximum characteristic area value *Rs3 as the third systolic pressure data; and on the half side of the curve where the cuff pressure is lower than the third average pressure data, extract the cuff pressure at the place where the characteristic area is the maximum characteristic area value *Rd3 as the third diastolic pressure data;
[0087] Among them, Rs3 is the third systolic pressure coefficient, and Rd3 is the third diastolic pressure coefficient.
[0088] Here, the third systolic pressure coefficient Rs3 and the third diastolic pressure coefficient Rd3 may be conventional corresponding coefficients of systolic pressure and diastolic pressure, and may be obtained by querying a preset blood pressure coefficient table;
[0089] For example, the third trend curve is Figure 3c As shown, point 34 The characteristic area at is the maximum characteristic area value, and the point 31 The characteristic area at is the maximum characteristic area value*Rs3, and the point36 The characteristic area at is the maximum characteristic area value*Rd3, then the third average pressure data is point 34 The cuff pressure data is the third systolic pressure data. 31 The cuff pressure data is the third diastolic pressure data. 36 cuff pressure data.
[0090] Step 6, performing blood pressure decision processing according to the first, second, and third coefficients, the first, second, and third average pressure data, the first, second, and third systolic pressure data, and the first, second, and third diastolic pressure data to obtain blood pressure measurement result data;
[0091] Specifically, the method comprises: step 61, according to the average pressure decision calculation formula, the decision average pressure data = (first average pressure data * first coefficient + second average pressure data * second coefficient + third average pressure data * third coefficient) / (first coefficient + second coefficient + third coefficient), to calculate and generate the decision average pressure data;
[0092] Here, the first coefficient / (first coefficient+second coefficient+third coefficient), the second coefficient / (first coefficient+second coefficient+third coefficient), and the third coefficient / (first coefficient+second coefficient+third coefficient) are the normalized ratios of the three reliability sum coefficients. The decision average pressure data is the sum of the products of the three normalized ratios and the corresponding average pressures, that is, the result of fine-tuning the average pressures estimated by the three methods through the three reliability sum data. The estimation result obtained by the estimation method with higher reliability has the greatest influence on the final decision average pressure data, thereby achieving the purpose of improving the accuracy of the final measurement result through the three estimation methods;
[0093] Step 62, according to the systolic pressure decision calculation formula, decision systolic pressure data = (first systolic pressure data * first coefficient + second systolic pressure data * second coefficient + third systolic pressure data * third coefficient) / (first coefficient + second coefficient + third coefficient), calculate and generate the decision systolic pressure data;
[0094] Here, similar to step 61, the decision systolic blood pressure data is the sum of the products of the three normalized ratios and the corresponding systolic blood pressure, that is, the result of fine-tuning the systolic blood pressure estimated by the three methods through the three reliability sum data. The estimation result obtained by the estimation method with higher reliability has the greatest impact on the final decision systolic blood pressure data, thereby achieving the purpose of improving the accuracy of the final measurement result through the three estimation methods;
[0095] Step 63, according to the diastolic pressure decision calculation formula, decision diastolic pressure data = (first diastolic pressure data*first coefficient+second diastolic pressure data*second coefficient+third diastolic pressure data*third coefficient) / (first coefficient+second coefficient+third coefficient), calculate and generate decision diastolic pressure data.
[0096] Here, similar to step 61, the decision diastolic pressure data is the sum of the products of the three normalized ratios and the corresponding diastolic pressure, that is, the result of fine-tuning the diastolic pressure estimated by the three methods using the three reliability sum data. The estimation result obtained by the estimation method with higher reliability has the greatest impact on the final decision diastolic pressure data, thereby achieving the purpose of improving the accuracy of the final measurement result through the three estimation methods.
[0097] Figure 4 The module structure diagram of a blood pressure measurement device combined with pulse wave characteristic data provided in the second embodiment of the present invention, the device can be a terminal device or server implementing the method of the embodiment of the present invention, or a device connected to the above terminal device or server to implement the method of the embodiment of the present invention, for example, the device can be a device or chip system of the above terminal device or server. Figure 4 As shown, the device includes: an acquisition module 201, a first data processing module 202, a second data processing module 203, a third data processing module 204, a blood pressure estimation processing module 205 and a blood pressure decision processing module 206.
[0098] The acquisition module 201 is used to acquire a plurality of first cuff pressure data and corresponding first pulse wave data.
[0099] The first data processing module 202 is used to perform pulse wave feature and reliability analysis on each first pulse wave data to generate a corresponding first feature data set; the first feature data set includes first feature amplitude data, first feature slope data, first feature area data and first reliability coefficient, second reliability coefficient, and third reliability coefficient.
[0100] The second data processing module 203 is used to calculate the sum of all first reliability coefficients to generate the first coefficient; calculate the sum of all second reliability coefficients to generate the second coefficient; calculate the sum of all third reliability coefficients to generate the third coefficient.
[0101] The third data processing module 204 is used to generate a first trend curve reflecting the change of characteristic amplitude with cuff pressure based on the correspondence between all the first cuff pressure data and all the first characteristic amplitude data; and to generate a second trend curve reflecting the change of characteristic slope with cuff pressure based on the correspondence between all the first cuff pressure data and all the first characteristic slope data; and to generate a third trend curve reflecting the change of characteristic area with cuff pressure based on the correspondence between all the first cuff pressure data and all the first characteristic area data.
[0102] The blood pressure estimation processing module 205 is used to perform a first blood pressure estimation process according to a first trend curve to generate first average pressure data, first systolic pressure data and first diastolic pressure data; and to perform a second blood pressure estimation process according to a second trend curve to generate second average pressure data, second systolic pressure data and second diastolic pressure data; and to perform a third blood pressure estimation process according to a third trend curve to generate third average pressure data, third systolic pressure data and third diastolic pressure data.
[0103] The blood pressure decision processing module 206 is used to perform blood pressure decision processing according to the first, second, and third coefficients, the first, second, and third average pressure data, the first, second, and third systolic pressure data, and the first, second, and third diastolic pressure data to obtain blood pressure measurement result data.
[0104] A blood pressure measurement device combined with pulse wave characteristic data provided in an embodiment of the present invention can execute the method steps in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here.
[0105] It should be noted that it should be understood that the division of the various modules of the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. And these modules can all be implemented in the form of software called by processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware. For example, the acquisition module can be a separately established processing element, or it can be integrated in a chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and called and executed by a processing element of the above device. The function of the above-mentioned module is determined. The implementation of other modules is similar. In addition, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.
[0106] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASIC), or one or more digital signal processors (DSP), or one or more field programmable gate arrays (FPGA). For another example, when a module above is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
[0107] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in accordance with the embodiment of the present invention is generated in whole or in part. The above computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The above computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the above computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, Bluetooth, microwave, etc.) mode to another website site, computer, server or data center. The above computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, data center, etc. that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium (eg, a solid state disk (SSD)).
[0108] Figure 5 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention. The electronic device may be the aforementioned terminal device or server, or may be a terminal device or server connected to the aforementioned terminal device or server to implement the method of the embodiment of the present invention. Figure 5As shown, the electronic device may include: a processor 31 (such as a CPU), a memory 32, and a transceiver 33; the transceiver 33 is coupled to the processor 31, and the processor 31 controls the transceiver 33. Various instructions may be stored in the memory 32 to complete various processing functions and implement the methods and processing procedures provided in the above embodiments of the present invention. Preferably, the electronic device involved in the embodiment of the present invention also includes: a power supply 34, a system bus 35 and a communication port 36. The system bus 35 is used to realize the communication connection between components. The above-mentioned communication port 36 is used for connecting and communicating between the electronic device and other peripherals.
[0109] exist Figure 5 The system bus mentioned in the figure can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. The communication interface is used to realize the communication between the database access device and other devices (such as clients, read-write libraries, and read-only libraries). The memory may include random access memory (RAM) and may also include non-volatile memory (Non-Volatile Memory), such as at least one disk storage.
[0110] The above-mentioned processor can be a general-purpose processor, including a central processing unit CPU, a network processor (NP), etc.; it can also be a digital signal processor DSP, an application-specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
[0111] It should be noted that an embodiment of the present invention further provides a computer-readable storage medium, in which instructions are stored. When the storage medium is run on a computer, the computer executes the method and processing process provided in the above embodiments.
[0112] An embodiment of the present invention further provides a chip for executing instructions, and the chip is used to execute the method and processing process provided in the above embodiment.
[0113] An embodiment of the present invention also provides a program product, which includes a computer program stored in a storage medium. At least one processor can read the computer program from the storage medium, and the at least one processor executes the method and processing process provided in the above embodiment.
[0114] The embodiments of the present invention provide a blood pressure measurement method, device, electronic device, computer program product and computer-readable storage medium combined with pulse wave characteristic data. In addition to the traditional measurement method of estimating based on the correspondence between the pulse wave amplitude and the cuff pressure, two auxiliary measurement methods are added: a measurement method of estimating based on the correspondence between the maximum slope of the pulse wave rising edge and the cuff pressure, and a measurement method of estimating based on the correspondence between the pulse wave systolic waveform area and the cuff pressure. Finally, a comprehensive decision is made based on the three groups of measurement results to obtain the final blood pressure measurement result. In this way, the adaptability of blood pressure measurement can be improved, and the accuracy of blood pressure measurement can also be improved.
[0115] The professionals should further realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.
[0116] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
[0117] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.
Claims
1. A blood pressure measurement method in combination with pulse wave characteristic data, characterized in that: The method comprises: Acquire a plurality of first cuff pressure data and corresponding first pulse wave data; For each of the first pulse wave data, pulse wave characteristics and reliability analysis processing is performed to generate a corresponding first characteristic data set; the first characteristic data set includes first characteristic amplitude data, first characteristic slope data, first characteristic area data and first reliability coefficient, second reliability coefficient, and third reliability coefficient; Calculate the sum of all the first reliability coefficients to generate a first coefficient; calculate the sum of all the second reliability coefficients to generate a second coefficient; calculate the sum of all the third reliability coefficients to generate a third coefficient; Generate a first trend curve reflecting the change of characteristic amplitude with cuff pressure according to the correspondence between all the first cuff pressure data and all the first characteristic amplitude data; and generate a second trend curve reflecting the change of characteristic slope with cuff pressure according to the correspondence between all the first cuff pressure data and all the first characteristic slope data; and generate a third trend curve reflecting the change of characteristic area with cuff pressure according to the correspondence between all the first cuff pressure data and all the first characteristic area data; According to the first trend curve, a first blood pressure estimation process is performed to generate first mean pressure data, first systolic pressure data, and first diastolic pressure data; and according to the second trend curve, a second blood pressure estimation process is performed to generate second mean pressure data, second systolic pressure data, and second diastolic pressure data; and according to the third trend curve, a third blood pressure estimation process is performed to generate third mean pressure data, third systolic pressure data, and third diastolic pressure data; Performing blood pressure decision processing according to the first, second, and third coefficients, the first, second, and third average pressure data, the first, second, and third systolic pressure data, and the first, second, and third diastolic pressure data to obtain blood pressure measurement result data; The step of performing a first blood pressure estimation process according to the first trend curve to generate first mean pressure data, first systolic pressure data and first diastolic pressure data specifically includes: In the first trend curve that can reflect the change of characteristic amplitude with cuff pressure, the cuff pressure at the location of the maximum characteristic amplitude is extracted as the first average pressure data; and on the half-curve where the cuff pressure is higher than the first average pressure data, the cuff pressure at the location of the maximum characteristic amplitude * Rs1 is extracted as the first systolic pressure data; and on the half-curve where the cuff pressure is lower than the first average pressure data, the cuff pressure at the location of the maximum characteristic amplitude * Rd1 is extracted as the first diastolic pressure data; wherein Rs1 is the first systolic pressure coefficient, and Rd1 is the first diastolic pressure coefficient; The performing a second blood pressure estimation process according to the second trend curve to generate second mean pressure data, second systolic pressure data and second diastolic pressure data specifically includes: In the second trend curve that can reflect the characteristic slope changing with the cuff pressure, the cuff pressure at the location where the characteristic slope is the maximum characteristic slope value is extracted as the second average pressure data; and on the half side of the curve where the cuff pressure is higher than the second average pressure data, the cuff pressure at the location where the characteristic slope is the maximum characteristic slope value * Rs2 is extracted as the second systolic pressure data; and on the half side of the curve where the cuff pressure is lower than the second average pressure data, the cuff pressure at the location where the characteristic slope is the maximum characteristic slope value * Rd2 is extracted as the second diastolic pressure data; wherein Rs2 is the second systolic pressure coefficient, and Rd2 is the second diastolic pressure coefficient; The performing a third blood pressure estimation process according to the third trend curve to generate third mean pressure data, third systolic pressure data and third diastolic pressure data specifically includes: In the third trend curve that can reflect the change of characteristic area with cuff pressure, the cuff pressure at the place where the characteristic area is the maximum characteristic area value is extracted as the third average pressure data; and on the half-curve where the cuff pressure is higher than the third average pressure data, the cuff pressure at the place where the characteristic area is the maximum characteristic area value * Rs3 is extracted as the third systolic pressure data; and on the half-curve where the cuff pressure is lower than the third average pressure data, the cuff pressure at the place where the characteristic area is the maximum characteristic area value * Rd3 is extracted as the third diastolic pressure data; wherein Rs3 is the third systolic pressure coefficient, and Rd3 is the third diastolic pressure coefficient; The blood pressure decision processing is performed according to the first, second, and third coefficients, the first, second, and third average pressure data, the first, second, and third systolic pressure data, and the first, second, and third diastolic pressure data to obtain blood pressure measurement result data, specifically including: According to the average pressure decision calculation formula, the decision average pressure data = (first average pressure data * first coefficient + second average pressure data * second coefficient + third average pressure data * third coefficient) / (first coefficient + second coefficient + third coefficient), the decision average pressure data is calculated and generated; According to the systolic pressure decision calculation formula, the decision systolic pressure data = (first systolic pressure data * first coefficient + second systolic pressure data * second coefficient + third systolic pressure data * third coefficient) / (first coefficient + second coefficient + third coefficient), the decision systolic pressure data is calculated and generated; According to the diastolic pressure decision calculation formula, the decision diastolic pressure data = (first diastolic pressure data * first coefficient + second diastolic pressure data * second coefficient + third diastolic pressure data * third coefficient) / (first coefficient + second coefficient + third coefficient), the decision diastolic pressure data is calculated and generated; The blood pressure measurement result data is composed of the decided mean pressure data, the decided systolic pressure data and the decided diastolic pressure data.
2. The blood pressure measurement method in combination with pulse wave characteristic data according to claim 1, characterized in that: The pulse wave feature and reliability analysis processing is performed on each of the first pulse wave data to generate a corresponding first feature data set, specifically including: For each of the first pulse wave data, pulse wave amplitude difference feature extraction processing is performed to generate the first feature amplitude data; and pulse wave reliability analysis processing is performed based on the pulse wave amplitude difference to generate the first reliability coefficient; For each of the first pulse wave data, a rising edge maximum slope feature extraction process is performed to generate the first characteristic slope data; and a pulse wave reliability analysis process is performed based on the rising edge maximum slope to generate the second reliability coefficient; For each of the first pulse wave data, a pulse systolic waveform area feature extraction process is performed to generate the first feature area data; and a pulse wave reliability analysis process is performed based on the pulse systolic waveform area to generate the third reliability coefficient; The first characteristic data set is composed of the first characteristic amplitude data, the first characteristic slope data, the first characteristic area data, the first reliability coefficient, the second reliability coefficient, and the third reliability coefficient.
3. The blood pressure measurement method in combination with pulse wave characteristic data according to claim 1, characterized in that: The generating, according to the correspondence between all the first cuff pressure data and all the first characteristic amplitude data, a first trend curve reflecting the change of the characteristic amplitude with the cuff pressure specifically includes: A first data group is composed of each first cuff pressure data and the corresponding first characteristic amplitude data, and a first data group sequence is composed of all the first data groups; and the first data group sequence is subjected to envelope fitting processing to obtain the first trend curve that can reflect the change of the characteristic amplitude with the cuff pressure.
4. The blood pressure measurement method in combination with pulse wave characteristic data according to claim 1, characterized in that: Generating, according to the correspondence between all the first cuff pressure data and all the first characteristic slope data, a second trend curve reflecting the change of the characteristic slope with the cuff pressure specifically includes: A second data group is formed by each of the first cuff pressure data and the corresponding first characteristic slope data, and a second data group sequence is formed by all of the second data groups; and envelope fitting processing is performed on the second data group sequence to obtain the second trend curve that can reflect the change of the characteristic slope with the cuff pressure.
5. The blood pressure measurement method in combination with pulse wave characteristic data according to claim 1, characterized in that: Generating a third trend curve reflecting changes in characteristic area with cuff pressure according to the correspondence between all the first cuff pressure data and all the first characteristic area data specifically includes: A third data group is formed by each of the first cuff pressure data and the corresponding first characteristic area data, and a third data group sequence is formed by all of the third data groups; and envelope fitting processing is performed on the third data group sequence to obtain the third trend curve that can reflect the change of the characteristic area with the cuff pressure.
6. A device for executing the blood pressure measurement method combined with pulse wave characteristic data as described in any one of claims 1 to 5, characterized in that: include: The acquisition module is used to acquire a plurality of first cuff pressure data and corresponding first pulse wave data; The first data processing module is used to perform pulse wave feature and reliability analysis processing on each of the first pulse wave data to generate a corresponding first feature data set; the first feature data set includes first feature amplitude data, first feature slope data, first feature area data and first reliability coefficient, second reliability coefficient, and third reliability coefficient; The second data processing module is used to calculate the sum of all the first reliability coefficients to generate a first coefficient; calculate the sum of all the second reliability coefficients to generate a second coefficient; calculate the sum of all the third reliability coefficients to generate a third coefficient; The third data processing module is used to generate a first trend curve reflecting the change of characteristic amplitude with cuff pressure according to the corresponding relationship between all the first cuff pressure data and all the first characteristic amplitude data; and to generate a second trend curve reflecting the change of characteristic slope with cuff pressure according to the corresponding relationship between all the first cuff pressure data and all the first characteristic slope data; and to generate a third trend curve reflecting the change of characteristic area with cuff pressure according to the corresponding relationship between all the first cuff pressure data and all the first characteristic area data; The blood pressure estimation processing module is used to perform a first blood pressure estimation process according to the first trend curve to generate first mean pressure data, first systolic pressure data and first diastolic pressure data; and performing a second blood pressure estimation process according to the second trend curve to generate second mean pressure data, second systolic pressure data, and second diastolic pressure data; and performing a third blood pressure estimation process according to the third trend curve to generate third mean pressure data, third systolic pressure data and third diastolic pressure data; The blood pressure decision processing module is used to perform blood pressure decision processing based on the first, second, and third coefficients, the first, second, and third average pressure data, the first, second, and third systolic pressure data, and the first, second, and third diastolic pressure data to obtain blood pressure measurement result data.
7. An electronic device, characterized in that: include: memory, processors, and transceivers; The processor is used to couple with the memory, read and execute instructions in the memory, so as to implement the method according to any one of claims 1 to 5; The transceiver is coupled to the processor, and the processor controls the transceiver to send and receive messages.
8. A computer program product, characterized in that The computer program product comprises computer program codes, and when the computer program codes are executed by a computer, the computer is enabled to perform the method according to any one of claims 1 to 5.
9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a computer, the computer is enabled to execute the method according to any one of claims 1 to 5.
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