CN114403826B - Blood pressure measurement method and device - Google Patents

Abstract

The embodiment of the invention relates to a blood pressure measurement method and device, wherein the method comprises the following steps: calculating the credibility data of the pulse wave signal group; performing envelope curve fitting processing on the pulse wave signal group; extracting maximum blood pressure data from the envelope; calculating systolic pressure, diastolic pressure and average pressure data according to the maximum blood pressure data and the systolic pressure, diastolic pressure proportional threshold; a blood pressure data set is formed by credibility, average pressure, systolic pressure and diastolic pressure data; processing the plurality of blood pressure data sets according to a trusted decision mode: in a first mode, extracting data from the blood pressure data set with highest credibility to generate a blood pressure measurement result; in the second mode, mean value calculation is carried out on the plurality of extracted reasonable credibility blood pressure data sets to obtain a blood pressure measurement result. The invention replaces the conventional signal filtering mode by the mode of carrying out feasibility calculation on the pulse wave signal group, calculates the pulse wave blood pressure value by taking the credibility as a reference, and improves the measurement accuracy of the blood pressure measurement equipment when the patient is in arrhythmia.

Description

Blood pressure measurement method and device

Technical Field

The present invention relates to the field of signal processing technologies, and in particular, to a blood pressure measurement method and apparatus.

Background

Pulse waves are formed by the peripheral propagation of the heart's pulsations along arterial blood vessels and blood flow, the propagation speed of which depends on the elasticity of the artery, the size of the lumen, the density and viscosity of the blood, etc., and in particular is closely related to the elasticity, caliber and thickness of the arterial wall. Under the condition that the heart is in normal rhythm, the conventional blood pressure measuring device can obtain accurate diastolic pressure, systolic pressure and average pressure value by measuring regular pulse waves. However, when the heart is in an arrhythmia state (such as atrial fibrillation), because the heartbeat frequency is irregular, the pulse wave related to the heartbeat frequency is also irregular, and the electronic oscillometric measurement method of the conventional blood pressure measurement device filters the irregular pulse wave as noise or interference, so that the conventional blood pressure measurement device cannot obtain accurate blood pressure data.

Disclosure of Invention

The invention aims at overcoming the defects of the prior art, and provides a blood pressure measuring method, a device, electronic equipment, a computer program product and a computer readable storage medium, wherein a conventional signal filtering mode is replaced by a mode of calculating the feasibility of pulse waves, and the blood pressure value of the pulse waves is calculated by taking the credibility as a reference, so that the measuring accuracy of the blood pressure measuring equipment in arrhythmia of a patient is improved.

To achieve the above object, a first aspect of an embodiment of the present invention provides a blood pressure measurement method, including:

acquiring a pulse wave signal group; the pulse wave signal group comprises a plurality of pulse wave signals;

according to the time interval data of the adjacent pulse wave signals in the pulse wave signal group, performing first trusted parameter calculation to generate first trusted parameter data;

according to the maximum amplitude data of the pulse wave signals adjacent to the pulse wave signal group, performing second credible parameter calculation to generate second credible parameter data;

according to the time width data of the adjacent pulse wave signals in the pulse wave signal group, third trusted parameter calculation is carried out, and third trusted parameter data are generated;

performing reliability calculation according to the first, second and third credible parameter data to generate credibility data;

performing envelope curve fitting processing on the pulse wave signal group to generate a signal envelope curve;

extracting the maximum amplitude data of the signal envelope curve to generate maximum blood pressure data P _MAX The method comprises the steps of carrying out a first treatment on the surface of the According to the systolic pressure ratio threshold and the P _MAX Performing systolic pressure calculation to generate systolic pressure data; according to the diastolic pressure ratio threshold and the P _MAX Calculating the diastolic blood pressure to generate diastolic blood pressure data; according to the systolic pressure data and the diastolic pressure data, carrying out average pressure calculation to generate average pressure data; forming a blood pressure data set from the credibility data, the average pressure data, the systolic pressure data and the diastolic pressure data;

acquiring a trusted decision mode;

when the credible decision mode is a first mode, screening all blood pressure data sets with highest credibility to generate a first blood pressure measurement result data set;

and when the credible decision mode is a second mode, carrying out reasonable credibility blood pressure data set average value processing on all the blood pressure data sets according to a reasonable credibility threshold value to generate a second blood pressure measurement result data set.

Preferably, the calculating the first trusted parameter according to the time interval data of the pulse wave signals adjacent to the pulse wave signal group to generate first trusted parameter data specifically includes:

sequentially counting time interval data of adjacent pulse wave signals in the pulse wave signal group to generate first time interval data; calculating absolute difference values of adjacent first time interval data to generate first differential data; generating first change rate data according to the ratio of the first differential data to the corresponding first time interval data;

And calculating the average value of all the first change rate data, and generating the first credible parameter data.

Preferably, the calculating the second trusted parameter according to the maximum amplitude data of the pulse wave signals adjacent to the pulse wave signal group to generate second trusted parameter data specifically includes:

sequentially extracting maximum amplitude data of the pulse wave signals from the pulse wave signal group to generate first amplitude data; calculating absolute difference values of the adjacent first amplitude data to generate second differential data; generating second change rate data according to the ratio of the second differential data to the corresponding first amplitude data;

and calculating the average value of all the second change rate data, and generating the second credible parameter data.

Preferably, the calculating the third trusted parameter according to the time width data of the pulse wave signals adjacent to the pulse wave signal group to generate third trusted parameter data specifically includes:

sequentially extracting time width data of the pulse wave signals in the pulse wave signal group to generate first time width data; calculating absolute difference values of the adjacent first time width data to generate third differential data; generating third change rate data according to the ratio of the third differential data to the corresponding first time width data;

And calculating the average value of all the third change rate data, and generating the third credible parameter data.

Preferably, the calculating the reliability according to the first, second and third reliability parameter data to generate reliability data specifically includes:

summing up the first, second and third trusted parameter data to generate trusted parameter sum data; and generating the credibility data according to the credibility data calculation formula and the credibility data= (1-credibility parameter sum data) multiplied by 100%.

Further, the method further comprises: and when the credibility parameter sum data is not less than 1, the credibility data is 0.

Preferably, the performing envelope fitting processing on the pulse wave signal set to generate a signal envelope specifically includes:

and in the pulse wave signal group, the maximum amplitude point of each pulse wave signal is smoothly connected, and the signal envelope curve is generated.

Preferably, said ratio threshold according to systolic pressure and said P _MAX Performing systolic pressure calculation to generate systolic pressure data, wherein the method specifically comprises the following steps:

according to the systolic pressure proportion threshold and the P _MAX According to the calculation formula of the systolic pressure data, systolic pressure data = systolic pressure proportion threshold value multiplied by P _MAX And generating the systolic pressure data.

Preferably, said threshold value and said P are based on a diastolic pressure ratio _MAX Is used for relaxingCalculating the tension and pressure to generate diastolic blood pressure data, which specifically comprises the following steps:

according to the diastolic blood pressure proportion threshold value and the P _MAX According to a calculation formula of the diastolic blood pressure data, the diastolic blood pressure data=diastolic blood pressure proportion threshold value multiplied by P _MAX And generating the diastolic blood pressure data.

Preferably, when the trusted decision mode is the first mode, the filtering processing of the blood pressure data set with the highest credibility is performed on all the blood pressure data sets, so as to generate a first blood pressure measurement result data set, which specifically includes:

when the credible decision mode is the first mode, selecting the blood pressure data set with the largest numerical value of the credibility data from all the blood pressure data sets as a screening result data set; and extracting the average pressure data, the systolic pressure data and the diastolic pressure data from the screening result data set to form the first blood pressure measurement result data set.

Preferably, when the trusted decision mode is the second mode, performing, according to a reasonable confidence threshold, mean processing of the reasonable confidence blood pressure data sets on all the blood pressure data sets, to generate a second blood pressure measurement result data set, including:

When the credible decision mode is the second mode, selecting the blood pressure data sets meeting the reasonable credibility threshold value from all the blood pressure data sets, and generating a reasonable credibility blood pressure data set sequence; in the reasonable credibility blood pressure data set sequence, the difference value between the credibility data of the maximum value and the credibility data of the minimum value does not exceed the reasonable credibility threshold;

in the reasonable credibility blood pressure data set sequence, carrying out average value calculation on all the average pressure data to generate average value average pressure data; calculating the average value of all the systolic pressure data to generate average value systolic pressure data; calculating the average value of all the diastolic blood pressure data to generate average diastolic blood pressure data;

and forming the second blood pressure measurement result data set by the mean average pressure data, the mean systolic pressure data and the mean diastolic pressure data.

A second aspect of an embodiment of the present invention provides a blood pressure measurement device, including:

the acquisition module is used for acquiring the pulse wave signal group; the pulse wave signal group comprises a plurality of pulse wave signals;

the trusted module is used for performing first trusted parameter calculation according to the time interval data of the adjacent pulse wave signals in the pulse wave signal group to generate first trusted parameter data; according to the maximum amplitude data of the pulse wave signals adjacent to the pulse wave signal group, performing second credible parameter calculation to generate second credible parameter data; according to the time width data of the adjacent pulse wave signals in the pulse wave signal group, third trusted parameter calculation is carried out, and third trusted parameter data are generated; performing reliability calculation according to the first, second and third credible parameter data to generate credibility data;

The first measuring and calculating module is used for carrying out envelope curve fitting processing on the pulse wave signal group to generate a signal envelope curve; extracting the maximum amplitude data of the signal envelope curve to generate maximum blood pressure data P _MAX The method comprises the steps of carrying out a first treatment on the surface of the According to the systolic pressure ratio threshold and the P _MAX Performing systolic pressure calculation to generate systolic pressure data; according to the diastolic pressure ratio threshold and the P _MAX Calculating the diastolic blood pressure to generate diastolic blood pressure data; according to the systolic pressure data and the diastolic pressure data, carrying out average pressure calculation to generate average pressure data; forming a blood pressure data set from the credibility data, the average pressure data, the systolic pressure data and the diastolic pressure data;

the second measuring and calculating module is used for acquiring a trusted decision mode; when the credible decision mode is a first mode, screening all blood pressure data sets with highest credibility to generate a first blood pressure measurement result data set; and when the credible decision mode is a second mode, carrying out reasonable credibility blood pressure data set average value processing on all the blood pressure data sets according to a reasonable credibility threshold value to generate a second blood pressure measurement result data set.

A third aspect of an embodiment of the present invention provides an electronic device, including: memory, processor, and transceiver;

The processor is configured to couple to the memory, and read and execute the instructions in the memory, so as to implement the method steps described in the first aspect;

the transceiver is coupled to the processor and is controlled by the processor to transmit and receive messages.

A fourth aspect of the embodiments of the present invention provides a computer program product comprising computer program code which, when executed by a computer, causes the computer to perform the method of the first aspect described above.

A fifth aspect of the embodiments of the present invention provides a computer-readable storage medium storing computer instructions that, when executed by a computer, cause the computer to perform the method of the first aspect described above.

The embodiment of the invention provides a blood pressure measuring method, a blood pressure measuring device, electronic equipment, a computer program product and a computer readable storage medium, wherein a conventional signal filtering mode is replaced by a mode of calculating the feasibility degree of pulse waves, and the blood pressure value of the pulse waves is calculated by taking the credibility degree as a reference, so that the measuring accuracy of the blood pressure measuring equipment in arrhythmia of a patient is improved.

Drawings

Fig. 1 is a schematic diagram of a blood pressure measurement method according to a first embodiment of the present invention;

fig. 2a is a schematic diagram of a pulse wave signal set and a pulse wave signal according to a first embodiment of the present invention;

FIG. 2b shows a pulse wave signal set, a signal envelope and P according to an embodiment of the present invention _MAX A schematic diagram;

fig. 3 is a block diagram of a blood pressure measuring device according to a second embodiment of the present invention;

fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The first embodiment of the invention provides a blood pressure measuring method, when measuring an obtained pulse wave signal group, adopting a mode of calculating reliability data and average pressure, systolic pressure and diastolic pressure data of each pulse wave signal to replace a conventional mode of directly filtering irregular pulse waves, expanding the original data range of blood pressure measuring equipment, and being beneficial to improving the reliability and precision of blood pressure measurement; when continuous blood pressure monitoring is carried out on a patient, a plurality of groups of pulse wave signal groups are obtained, and a plurality of groups of blood pressure data groups (the plurality of groups of blood pressure data groups comprise credible data and average pressure, systolic pressure and diastolic pressure data) can be obtained, and the embodiment of the invention provides a credible decision mode to carry out credible data statistics on the plurality of groups of blood pressure data groups, screens the most reliable data as a measurement result, and improves the measurement accuracy of blood pressure measurement equipment: when the trusted decision mode is the first mode, selecting average pressure, systolic pressure and diastolic pressure data of a blood pressure data set with the maximum trusted data as a blood pressure measurement result; when the trusted decision mode is the second mode, a plurality of groups of blood pressure data groups within a reasonable credibility threshold range are selected, average pressure, systolic pressure and diastolic pressure data average calculation is carried out, and average pressure, average systolic pressure and average diastolic pressure data are obtained and are used as blood pressure measurement results.

As shown in fig. 1, which is a schematic diagram of a blood pressure measurement method according to a first embodiment of the present invention, the method mainly includes the following steps:

step 1, acquiring a pulse wave signal group;

the pulse wave signal group comprises a plurality of pulse wave signals.

Specific: the blood pressure measuring device can acquire the pulse wave signal group of the patient through directly acquiring the patient, can also acquire the pulse wave signal group of the patient through connecting other pulse wave signal acquisition devices, and can also acquire the pulse wave signal group stored in the database through connecting the database. Here, as shown in fig. 2a, the obtained pulse wave signal set is a schematic diagram of the pulse wave signal set and the pulse wave signal set provided in the first embodiment of the present invention, and is composed of a plurality of continuous pulse wave signals.

Here, the blood pressure measuring device is a terminal device or a server for blood pressure monitoring and/or measurement, and can be used for carrying out single blood pressure measurement on a patient, carrying out multiple blood pressure measurement on the patient by setting automatic measurement times, and also can be used for acquiring a single blood pressure measuring instruction or multiple blood pressure measuring instructions from an electrocardio monitoring device connected with the blood pressure measuring device to start a local single blood pressure measuring process or multiple blood pressure measuring process.

When the blood pressure measuring device performs single blood pressure measurement, a plurality of continuous pulse wave signals are acquired by collecting a single pulse wave signal group of a patient, and blood pressure data calculation is performed on the pulse wave signal group to obtain a measurement result. If the current single measurement is initiated locally by the blood pressure measurement equipment, locally displaying the measurement result; when the current single measurement is initiated from the electrocardio monitoring equipment connected with the current single measurement, the measurement result is sent to the electrocardio monitoring equipment.

When the blood pressure measuring equipment performs multiple blood pressure measurements, pulse wave signal sets are acquired for a patient every other fixed rest time, and the acquisition times are equal to the required measurement times; after the pulse wave signal group acquisition is finished, carrying out blood pressure measurement on the currently acquired pulse wave signal group to obtain a corresponding measurement result; after the measurement is finished, the blood pressure measurement device performs credible statistics on the measurement results according to the credible decision mode, and finally obtains a statistical measurement result. If the current multiple measurement is initiated locally by the blood pressure measurement device, locally displaying the statistical measurement result and/or the fractional measurement result; if the current multiple measurements are initiated from the electrocardiographic monitoring device connected with the current multiple measurements, the statistical measurement results and/or the fractional measurement results are transmitted to the electrocardiographic monitoring device.

Step 2, according to the time interval data of adjacent pulse wave signals in the pulse wave signal group, performing first trusted parameter calculation to generate first trusted parameter data;

here, the first credible parameter data is one of parameters for calculating credible data of the pulse wave signal group, and is used for identifying the probability of occurrence of continuous wave internal peak point deformation of the current pulse wave signal group, and the lower the first credible parameter data is, the lower the probability of occurrence of continuous wave internal peak point deformation of the current pulse wave signal group is, otherwise, the higher the first credible parameter data is, the higher the probability of occurrence of continuous wave internal peak point deformation of the current pulse wave signal group is;

the method specifically comprises the following steps: step 21, counting time interval data of adjacent pulse wave signals in sequence in a pulse wave signal group to generate first time interval data;

here, the time interval of the adjacent pulse wave signals may be a time interval between maximum amplitude points of the adjacent pulse wave signals; the time interval data is a specific value of this interval;

for example, the obtained pulse wave signal group includes 10 pulse wave signals, and the time interval between the maximum amplitude points of the adjacent pulse wave signals is taken as the time interval of the adjacent pulse wave signals, so that 9 pieces of first time interval data are generated by the current pulse wave signal group:

The 1 st first time interval data is the time interval from the maximum amplitude point of the 1 st pulse wave signal to the maximum amplitude point of the 2 nd pulse wave signal,

the 2 nd first time interval data is the time interval from the maximum amplitude point of the 2 nd pulse wave signal to the maximum amplitude point of the 3 rd pulse wave signal,

by analogy in turn,

the 9 th first time interval data is the time interval from the maximum amplitude point of the 9 th pulse wave signal to the maximum amplitude point of the 10 th pulse wave signal;

step 22, calculating absolute difference values of adjacent first time interval data to generate first differential data;

for example, from 9 first time interval data, 8 first differential data are generated:

the 1 st first differential data= |2 nd first time interval data-1 st first time interval data|,

the 2 nd first differential data= |3 rd first time interval data-2 nd first time interval data|,

by analogy in turn,

the 8 th first differential data is the 9 th first time interval data-the 8 th first time interval data, and the symbol of the 8 th first time interval data is an absolute value symbol;

step 23, generating first change rate data according to the ratio of the first differential data to the corresponding first time interval data;

Here, the first differential data is obtained by differentiating two adjacent first time interval data, and two choices are provided for the first time interval data corresponding to the first differential data, and the first time interval data can be the one which is the first time interval data in the adjacent two first time interval data and can be the one which is the last time; here, the one with the earlier time is selected as the first time interval data corresponding to the first differential data;

for example, if 8 first differential data are obtained from 9 first time interval data, the first time interval data corresponding to the 1 st to 8 first differential data should be the 1 st to 8 first time interval data, and 8 first rate of change data are further obtained:

first change rate data 1 st = first differential data 1 st/first time interval data 1 st,

first rate of change data 2 = first differential data 2/first time interval data 2,

and so on,

first rate of change data 8 = first differential data 8/first time interval data 8;

and step 24, calculating the average value of all the first change rate data to generate first credible parameter data.

For example, the obtained pulse wave signal group includes 10 pulse wave signals, 9 first time interval data are generated, 8 first differential data are obtained from the 9 first time interval data, 8 first change rate data are obtained from the 8 first differential data, and then the first trusted parameter data are average values of the 8 first change rate data.

Step 3, calculating a second trusted parameter according to the maximum amplitude data of the adjacent pulse wave signals in the pulse wave signal group, and generating second trusted parameter data;

here, the second credible parameter data is one of parameters for calculating credible data of the pulse wave signal group, and is used for identifying the probability of continuous deformation of the current pulse wave signal group, and the lower the second credible parameter data is, the lower the probability of continuous deformation of the current pulse wave signal group is, otherwise, the higher the second credible parameter data is, the higher the probability of continuous deformation of the current pulse wave signal group is;

the method specifically comprises the following steps: step 31, sequentially extracting maximum amplitude data of pulse wave signals in the pulse wave signal group to generate first amplitude data;

for example, the obtained pulse wave signal group includes 10 pulse wave signals, and sequentially extracting the maximum amplitude data of the pulse wave signals, 10 first amplitude data are obtained: first amplitude data 1 to 10:

step 32, calculating absolute difference values of adjacent first amplitude data to generate second differential data;

for example, from 10 first magnitude data, 9 second differential data are generated:

The 1 st second differential data= |2 nd first amplitude data-1 st first amplitude data|,

the 2 nd second differential data= |3 rd first amplitude data-2 nd first amplitude data|,

by analogy in turn,

the 9 th second differential data= |10 th first amplitude data-9 th first amplitude data|;

step 33, generating second change rate data according to the ratio of the second differential data to the corresponding first amplitude data;

here, the second differential data is obtained by differentiating two adjacent first amplitude data, and the first amplitude data corresponding to the second differential data has two choices, and may be the one of the two adjacent first amplitude data, which is the one that is the front of time, or the one that is the back of time; here, the one with the earlier time is selected as the first amplitude data corresponding to the second differential data;

for example, if 9 second differential data are obtained from 10 first amplitude data, the first amplitude data corresponding to the 1 st to 9 th second differential data should be the 1 st to 9 th first amplitude data, and further 9 second change rate data are obtained:

second change rate data 1 st = second difference data 1 st/first amplitude data 1 st,

Second rate of change data 2 = second differential data 2/first amplitude data 2,

and so on,

second change rate data 9 = second difference data 9/first amplitude data 9;

and step 34, calculating the average value of all the second change rate data to generate second credible parameter data.

For example, the obtained pulse wave signal group includes 10 pulse wave signals, 10 first amplitude data are generated, 9 second differential data are obtained from the 10 first amplitude data, 9 second change rate data are obtained from the 9 second differential data, and then the second credible parameter data are average values of the 9 second change rate data.

Step 4, calculating a third trusted parameter according to the time width data of the adjacent pulse wave signals in the pulse wave signal group to generate third trusted parameter data;

here, the third trusted parameter data is one of the parameters for calculating the trusted data of the pulse wave signal group, and is used for identifying the probability of the continuous deformation of the width of the current pulse wave signal group, and the lower the third trusted parameter data is, the lower the probability of the continuous deformation of the width of the current pulse wave signal group is, otherwise, the higher the third trusted parameter data is, the higher the probability of the continuous deformation of the width of the current pulse wave signal group is;

The method specifically comprises the following steps: step 41, sequentially extracting time width data of pulse wave signals in the pulse wave signal group to generate first time width data;

here, the time width of the pulse wave signal is the time interval from the start point to the end point of the pulse wave signal, and the time width data is a specific value of the interval;

for example, the obtained pulse wave signal group includes 10 pulse wave signals, and sequentially extracting the time width data of the pulse wave signals, 10 first time width data are obtained: first time width data 1 to 10:

step 42, calculating the absolute difference value of the adjacent first time width data to generate third differential data;

for example, from 10 first time width data, 9 third differential data are generated:

the 1 st third differential data= |2 nd first time width data-1 st first time width data|,

the 2 nd third differential data= |3 rd first time width data-2 nd first time width data|,

by analogy in turn,

the 9 th third differential data= |10 th first time width data-9 th first time width data|;

step 43, generating third change rate data according to the ratio of the third differential data to the corresponding first time width data;

Here, the third differential data is obtained by differentiating two adjacent first time width data, and two choices are provided for the first time width data corresponding to the third differential data, and the first time width data may be the one of the two adjacent first time width data, which is the front time or the rear time; here, the one with the earlier time is selected as the first time width data corresponding to the third differential data;

for example, if 9 third differential data are obtained from 10 first time-width data, the first time-width data corresponding to the 1 st to 9 th third differential data should be the 1 st to 9 th first time-width data, and further 9 third rate-of-change data are obtained:

third change rate data 1 st = third differential data 1 st/first time width data 1 st,

third rate of change data 2 = third differential data 2/first time width data 2,

and so on,

the 9 th third rate of change data=the 9 th third differential data/the 9 th first time width data;

and step 44, calculating the average value of all the third change rate data to generate third credible parameter data.

For example, the obtained pulse wave signal group includes 10 pulse wave signals, 10 first time width data are generated, 9 third differential data are obtained from the 10 first time width data, and 9 third change rate data are obtained from the 9 third differential data, so that the third credible parameter data are average values of the 9 third change rate data.

Step 5, performing reliability calculation according to the first, second and third credible parameter data to generate credibility data;

the method specifically comprises the following steps: step 51, summing up the first, second and third trusted parameter data to generate trusted parameter sum data;

here, the trusted parameter sum data=first trusted parameter data+second trusted parameter data+third trusted parameter data; here, the trusted parameter sum data is used for the next trusted data calculation process, and if the trusted parameter sum data is greater than 1, the obtained trusted data is negative, so when the value of the trusted parameter sum data is greater than 1, the embodiment of the present invention will force the trusted parameter sum data to be set to 1;

and step 52, generating reliability data according to a reliability data calculation formula and reliability data= (1-reliability parameter sum data) x 100 percent.

Here, when the trusted parameter sum data is not less than 1, the trusted parameter sum data is forcedly set to 1 to ensure that the trusted parameter sum data is not less than 0 all the time.

Here, the blood pressure measurement device can perform real-time reliable signal identification on each pulse wave signal group according to the obtained reliability data, when the reliability data is lower than the reliability signal reliability threshold (the experience threshold of the reliability signal, commonly is 50%), it is indicated that the pulse wave signal in the current pulse wave signal group has obvious continuous deformation, the blood pressure measurement device can consider that the current pulse wave signal group is unreliable and exit the current blood pressure measurement processing process, and simultaneously prompts a patient or a user through prompt information of unreliable display signals; when the reliability data is higher than the reliability threshold value of the reliable signal, the continuous deformation trend of the pulse wave signals in the current pulse wave signal group is controlled, and the blood pressure measuring equipment can continue to execute the current blood pressure measuring process; in some special cases (e.g., continuous blood pressure monitoring of critically ill patients with a major goal of achieving a continuous trend of patient blood pressure change), the blood pressure measurement device may also ignore the real-time reliability of the current pulse wave signal set, and continue to perform the current blood pressure measurement process even if the reliability data is below the reliability signal reliability threshold.

Step 6, carrying out envelope curve fitting processing on the pulse wave signal group to generate a signal envelope curve;

the method specifically comprises the following steps: in the pulse wave signal group, the maximum amplitude points of the pulse wave signals are smoothly connected to generate a signal envelope.

Here, as shown in fig. 2b, the pulse wave signal set, the signal envelope and the P according to the first embodiment of the present invention _MAX And (3) carrying out smooth connection among maximum amplitude points of all pulse waves to obtain a signal envelope curve.

Step 7, extracting the maximum amplitude data of the signal envelope curve to generate maximum blood pressure data P _MAX 。

Here, as shown in fig. 2b, the blood pressure value at the maximum amplitude point of the signal envelope is the maximum blood pressure data P _MAX 。

Step 8, according to the systolic pressure ratio threshold and P _MAX Performing systolic pressure calculation to generate systolic pressure data;

the method specifically comprises the following steps: according to the systolic pressure ratio threshold and P _MAX According to the calculation formula of the systolic pressure data, systolic pressure data = systolic pressure proportion threshold value multiplied by P _MAX Systolic blood pressure data is generated.

Here, the systolic pressure ratio threshold is a ratio threshold of systolic pressure to average pressure, and is based on P _MAX An empirical threshold of the systolic pressure is calculated, the systolic pressure ratio threshold is smaller than 1, and the value of the systolic pressure ratio threshold is usually in the range of 0.4 to 0.7.

For example, P _MAX At 175 mmHg, the systolic blood pressure ratio threshold was 0.6, and the systolic blood pressure was about 105 mmHg.

Step 9, according to the diastolic pressure ratio threshold value and P _MAX Calculating the diastolic blood pressure to generate diastolic blood pressure data;

the method specifically comprises the following steps: according to the diastolic pressure ratio threshold value and P _MAX According to a calculation formula of the diastolic blood pressure data, the diastolic blood pressure data=diastolic blood pressure proportion threshold value multiplied by P _MAX Diastolic blood pressure data is generated.

Here, the diastolic pressure ratio threshold is a ratio threshold of diastolic pressure to average pressure, and is based on P _MAX The empirical threshold of the diastolic blood pressure is calculated, the diastolic blood pressure ratio threshold is smaller than 1, and the value range of the diastolic blood pressure ratio threshold is usually between 0.4 and 0.8.

For example, P _MAX For 175 mmHg, the systolic pressure ratio threshold is 0.4, and the diastolic pressure is about 70 mmHg.

And step 10, carrying out average pressure calculation according to the systolic pressure data and the diastolic pressure data to generate average pressure data.

There are various calculation modes of average pressure, and a common estimation mode is cited herein for explanation, and average pressure= (systolic pressure+2×diastolic pressure)/3; for example, a systolic pressure of 105 mmHg and a diastolic pressure of 70 mmHg would mean a pressure of about 82 mmHg.

And 11, forming a blood pressure data set by the reliability data, the average pressure data, the systolic pressure data and the diastolic pressure data.

Here, the blood pressure data set is a single blood pressure measurement result obtained after the blood pressure measurement device directly measures the current pulse wave signal set.

When the blood pressure measuring device performs single blood pressure measurement, step 12 is performed to enter a subsequent processing procedure after 1 blood pressure data set is obtained; when the blood pressure measuring device performs a plurality of blood pressure measurements not less than 2 times, the steps 1 to 11 are repeated not less than 2 times, and the process proceeds to step 12 to enter the subsequent processing after the corresponding plurality of sets of blood pressure data sets are obtained.

And step 12, acquiring a trusted decision mode.

Here, the trusted decision mode is a preset value stored locally to the blood pressure measuring device, which can be set and selected by the user when using the blood pressure measuring device for blood pressure measurement. There are two modes of trusted decision mode: when the trusted decision mode is the first mode, the blood pressure measuring device selects data (average pressure, systolic pressure and diastolic pressure data) of a blood pressure data group with the maximum trusted data from a plurality of blood pressure data groups as a blood pressure measuring result; when the trusted decision mode is the second mode, the blood pressure measuring device screens out a plurality of groups of blood pressure data groups with the trusted data within a reasonable threshold range of the trusted data, and carries out mean value calculation on the data (average pressure, systolic pressure and diastolic pressure data) of the screened blood pressure data groups to obtain mean average pressure, systolic pressure and diastolic pressure data as blood pressure measuring results.

When a patient is in an arrhythmia state (e.g., atrial fibrillation), conventional blood pressure detection devices have difficulty accurately measuring blood pressure data of the patient because they filter unreliable pulse waves on the one hand and do not perform statistical calculations of the two modes based on confidence data on the measurement results on the other hand. However, with the blood pressure measurement device of the embodiment of the present invention, statistical calculation can be performed on the plurality of blood pressure data sets based on two modes provided by the trusted decision mode, thereby helping the device to improve the blood pressure measurement precision and accuracy when the patient is arrhythmia, and enabling the device to screen or calculate the most reliable blood pressure data from the plurality of measurement data.

Step 13, when the trusted decision mode is the first mode, screening the blood pressure data sets with the highest credibility for all the blood pressure data sets to generate a first blood pressure measurement result data set;

the method specifically comprises the following steps: step 131, selecting the blood pressure data set with the highest numerical value of the credibility data from all the blood pressure data sets as a screening result data set;

for example, the blood pressure measuring device monitors the blood pressure of the patient 5 times to obtain 5 blood pressure data sets, and the 5 credible data in the 1 st to 5 th blood pressure data sets are respectively: 45% of the 1 st credibility data, 50% of the 2 nd credibility data, 55% of the 3 rd credibility data, 60% of the 4 th credibility data and 65% of the 5 th credibility data; the screening result data set is the 5 th blood pressure data set corresponding to the 5 th credibility data with the largest credibility data;

Step 132, extracting average pressure data, systolic pressure data and diastolic pressure data from the screening result data set to form a first blood pressure measurement result data set.

Here, the first blood pressure measurement result data set is a final blood pressure measurement result obtained by calculating the blood pressure measurement device using the first mode.

For example, if the screening result data set is the 5 th blood pressure data set, the first blood pressure measurement result data set as the final measurement output result should be the average pressure data, the systolic pressure data, and the diastolic pressure data in the 5 th blood pressure data set.

After the blood pressure measuring device obtains the first blood pressure measuring result data set, when the current measuring processing process is initiated locally by the blood pressure measuring device, the blood pressure measuring device can locally display the content of the first blood pressure measuring result data set; when the current measurement process is initiated by an electrocardiograph monitoring device connected to the blood pressure measurement device, the blood pressure measurement device transmits a first blood pressure measurement result data set to the electrocardiograph monitoring device connected to the blood pressure measurement device.

Step 14, when the trusted decision mode is the second mode, carrying out mean value processing on all blood pressure data sets according to a reasonable credibility threshold value to generate a second blood pressure measurement result data set;

The method specifically comprises the following steps: step 141, selecting a blood pressure data set meeting a reasonable credibility threshold value from all blood pressure data sets, and generating a reasonable credibility blood pressure data set sequence;

the difference value between the reliability data of the maximum value and the reliability data of the minimum value in the reasonable reliability blood pressure data set sequence does not exceed a reasonable reliability threshold;

here, the reasonable confidence threshold is an empirical threshold (e.g., 5%) used to further screen from a plurality of blood pressure data sets to obtain a subset of more reliable blood pressure data sets (reasonable confidence data set sequence) in which the difference between the maximum and minimum confidence data does not exceed the reasonable confidence threshold; here, the reasonable reliability threshold is actually used for carrying out one-time data noise reduction treatment on a plurality of groups of blood pressure data sets: discrete data is removed from the original data, so that the reliability and the precision of the data are improved;

for example, the blood pressure measurement device acquires 5 pulse wave signal sets of the patient, and obtains 5 blood pressure data sets, wherein 5 credible data of the 1 st to 5 th blood pressure data sets are respectively: 41% of the 1 st credibility data, 49% of the 2 nd credibility data, 55% of the 3 rd credibility data, 56% of the 4 th credibility data and 57% of the 5 th credibility data; in addition, if the reasonable reliability threshold is set to 5%, 3 groups of blood pressure data groups included in the screened reasonable reliability blood pressure data group sequence are: a blood pressure data set with 55% credibility data of 3, a blood pressure data set with 56% credibility data of 4, and a blood pressure data set with 57% credibility data of 5;

Step 142, in the reasonable credibility blood pressure data set sequence, calculating the average value of all average pressure data to generate average value average pressure data; calculating the average value of all the systolic pressure data to generate average value systolic pressure data; calculating the average value of all the diastolic blood pressure data to generate average diastolic blood pressure data;

for example, the blood pressure measurement device acquires 5 pulse wave signal sets of the patient, and the blood pressure data sets included in the reasonable reliability blood pressure data set sequence have 3 sets: blood pressure data sets of groups 3, 4 and 5; the mean average pressure data is the mean value of the average pressure data in the blood pressure data groups 3, 4 and 5; the mean systolic pressure data are the mean values of systolic pressure data in blood pressure data groups 3, 4 and 5; the mean diastolic blood pressure data is the mean value of diastolic blood pressure data in the blood pressure data groups 3, 4 and 5;

step 143, forming a second blood pressure measurement result data set from the mean average pressure data, the mean systolic pressure data and the mean diastolic pressure data.

Here, the second blood pressure measurement data set is calculated by the blood pressure measurement device using the second mode to obtain a final blood pressure measurement result.

After the blood pressure measuring device obtains the second blood pressure measuring result data set, when the current measuring processing process is initiated locally by the blood pressure measuring device, the blood pressure measuring device can locally display the content of the second blood pressure measuring result data set; when the current measurement process is initiated by an electrocardiograph monitoring device connected to the blood pressure measurement device, the blood pressure measurement device transmits a second blood pressure measurement result data set to the electrocardiograph monitoring device connected to the blood pressure measurement device.

Fig. 3 is a block diagram of a blood pressure measurement device according to a second embodiment of the present invention, where the device may be a terminal device or a server described in the foregoing embodiment, or may be a device capable of enabling the terminal device or the server to implement the method according to the embodiment of the present invention, and for example, the device may be a device or a chip system of the terminal device or the server. As shown in fig. 3, the apparatus includes:

the acquisition module 301 is configured to acquire a pulse wave signal set; the pulse wave signal group comprises a plurality of pulse wave signals.

The trusted module 302 is configured to perform first trusted parameter calculation according to time interval data of adjacent pulse wave signals in the pulse wave signal group, and generate first trusted parameter data; according to the maximum amplitude data of the adjacent pulse wave signals in the pulse wave signal group, performing second trusted parameter calculation to generate second trusted parameter data; according to the time width data of the adjacent pulse wave signals in the pulse wave signal group, third trusted parameter calculation is carried out, and third trusted parameter data are generated; and performing reliability calculation according to the first, second and third reliability parameter data to generate reliability data.

In one specific implementation provided in this embodiment, the trusted module 302 is specifically configured to:

sequentially counting time interval data of adjacent pulse wave signals in the pulse wave signal group to generate first time interval data; calculating absolute difference values of adjacent first time interval data to generate first differential data; generating first change rate data according to the ratio of the first differential data to the corresponding first time interval data; and calculating the average value of all the first change rate data to generate first credible parameter data.

In yet another specific implementation provided in this embodiment, the trusted module 302 is further specifically configured to:

sequentially extracting maximum amplitude data of pulse wave signals in a pulse wave signal group to generate first amplitude data; calculating absolute difference values of adjacent first amplitude data to generate second differential data; generating second change rate data according to the ratio of the second differential data to the corresponding first amplitude data; and calculating the average value of all the second change rate data to generate second credible parameter data.

In yet another specific implementation provided in this embodiment, the trusted module 302 is further specifically configured to:

sequentially extracting time width data of pulse wave signals in a pulse wave signal group to generate first time width data; calculating absolute difference values of adjacent first time width data to generate third differential data; generating third change rate data according to the ratio of the third differential data to the corresponding first time width data; and calculating the average value of all the third change rate data, and generating third credible parameter data.

In yet another specific implementation provided in this embodiment, the trusted module 302 is further specifically configured to:

summing the first, second and third trusted parameter data to generate trusted parameter sum data; and generating the credibility data according to a credibility data calculation formula and the credibility data= (1-credibility parameter sum data) multiplied by 100 percent according to the credibility parameter sum data.

In yet another specific implementation provided in this embodiment, the trusted module 302 is further specifically configured to: when the trusted parameter sum data is not less than 1, the trusted data is set to 0.

The first measurement module 303 is configured to perform envelope fitting processing on the pulse wave signal set to generate a signal envelope; extracting maximum amplitude data of signal envelope line to generate maximum blood pressure data P _MAX The method comprises the steps of carrying out a first treatment on the surface of the According to the systolic pressure ratio threshold and P _MAX Performing systolic pressure calculation to generate systolic pressure data; according to the diastolic pressure ratio threshold value and P _MAX Calculating the diastolic blood pressure to generate diastolic blood pressure data; according to the systolic pressure data and the diastolic pressure data, carrying out average pressure calculation to generate average pressure data; the blood pressure data set is composed of credibility data, average pressure data, systolic pressure data and diastolic pressure data.

In yet another specific implementation manner provided in this embodiment, the first measurement module 303 is specifically configured to: in the pulse wave signal group, the maximum amplitude points of the pulse wave signals are smoothly connected to generate a signal envelope.

In yet another specific implementation manner provided in this embodiment, the first measurement module 303 is further specifically configured to: according to the systolic pressure ratio threshold and P _MAX According to the calculation formula of the systolic pressure data, systolic pressure data = systolic pressure proportion threshold value multiplied by P _MAX Systolic blood pressure data is generated.

In yet another implementation manner provided in this embodiment, the first measurement module 303 furtherThe method is particularly used for: according to the diastolic pressure ratio threshold value and P _MAX According to a calculation formula of the diastolic blood pressure data, the diastolic blood pressure data=diastolic blood pressure proportion threshold value multiplied by P _MAX Diastolic blood pressure data is generated.

The second measurement module 304 is configured to obtain a trusted decision mode; when the credible decision mode is a first mode, screening all blood pressure data sets with highest credibility to generate a first blood pressure measurement result data set; and when the trusted decision mode is the second mode, carrying out reasonable credibility blood pressure data set average processing on all the blood pressure data sets according to a reasonable credibility threshold value to generate a second blood pressure measurement result data set.

In yet another specific implementation manner provided in this embodiment, the second measurement module 304 is specifically configured to:

when the credible decision mode is a first mode, selecting a blood pressure data set with the largest credibility data value from all blood pressure data sets as a screening result data set; and extracting average pressure data, systolic pressure data and diastolic pressure data from the screening result data set to form a first blood pressure measurement result data set.

In another specific implementation manner provided in this embodiment, the second measurement module 304 is further specifically configured to:

when the credible decision mode is the second mode, selecting a blood pressure data set meeting a reasonable credibility threshold value from all blood pressure data sets, and generating a reasonable credibility blood pressure data set sequence; in the reasonable credibility blood pressure data group sequence, the difference value between the credibility data of the maximum value and the credibility data of the minimum value does not exceed a reasonable credibility threshold value; in a reasonable credibility blood pressure data set sequence, carrying out average value calculation on all average pressure data to generate average value average pressure data; calculating the average value of all the systolic pressure data to generate average value systolic pressure data; calculating the average value of all the diastolic blood pressure data to generate average diastolic blood pressure data; and forming a second blood pressure measurement result data set by the mean average pressure data, the mean systolic pressure data and the mean diastolic pressure data.

The blood pressure measuring device provided by the embodiment of the invention can execute the method steps in the method embodiment, and the implementation principle and the technical effect are similar, and are not repeated here.

It should be noted that, it should be understood that the division of the modules of the above apparatus is merely a division of a logic function, and may be fully or partially integrated into a physical entity or may be physically separated. And these modules may all be implemented in software in the form of calls by the processing element; or can be realized in hardware; the method can also be realized in a form of calling software by a processing element, and the method can be realized in a form of hardware by a part of modules. For example, the acquisition module may be a processing element that is set up separately, may be implemented in a chip of the above apparatus, or may be stored in a memory of the above apparatus in the form of program code, and may be called by a processing element of the above apparatus and execute the functions of the above determination module. The implementation of the other modules is similar. In addition, all or part of the modules can be integrated together or can be independently implemented. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in a software form.

For example, the modules above may be one or more integrated circuits configured to implement the methods above, such as: one or more specific integrated circuits (Application Specific Integrated Circuit, ASIC), or one or more digital signal processors (Digital Signal Processor, DSP), or one or more field programmable gate arrays (Field Programmable Gate Array, FPGA), etc. For another example, when a module above is implemented in the form of a processing element scheduler code, the processing element may be a general purpose processor, such as a central processing unit (Central Processing Unit, CPU) or other processor that may invoke the program code. For another example, the modules may be integrated together and implemented in the form of a System-on-a-chip (SOC).

In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may 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 loaded and executed on a computer, produces, in whole or in part, the processes or functions described in accordance with embodiments of the present invention. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may 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 computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (e.g., coaxial cable, fiber optic, digital subscriber line ((Digital Subscriber Line, DSL)), or wireless (e.g., infrared, wireless, bluetooth, microwave, etc.) means, the computer-readable storage medium may be any available medium that can be accessed by the computer or a data storage device such as a server, data center, etc., that contains an integration of one or more available media, the available media may be magnetic media (e.g., floppy disk, hard disk, tape), optical media (e.g., DVD), or semiconductor media (e.g., solid state disk, SSD), etc.

Fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment 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 for implementing the method of the embodiment of the present invention. As shown in fig. 4, the electronic device may include: a processor 41 (e.g., CPU), a memory 42, a transceiver 43; the transceiver 43 is coupled to the processor 41, and the processor 41 controls the transceiving operation of the transceiver 43. The memory 42 may store various instructions for performing various processing functions and implementing the methods and processes provided in the above-described embodiments of the present invention. Preferably, the electronic device according to the embodiment of the present invention further includes: a power supply 44, a system bus 45, and a communication port 46. The system bus 45 is used to enable communication connections between the elements. The communication port 46 is used for connection communication between the electronic device and other peripheral devices.

The system bus referred to in fig. 4 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, or the like. The system bus may be classified into an address bus, a data bus, a control bus, and the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus. The communication interface is used to enable communication between the database access apparatus and other devices (e.g., clients, read-write libraries, and read-only libraries). The Memory may comprise random access Memory (Random Access Memory, RAM) and may also include Non-Volatile Memory (Non-Volatile Memory), such as at least one disk Memory.

The processor may be a general-purpose processor, including a Central Processing Unit (CPU), a network processor (Network Processor, NP), etc.; but may also be a digital signal processor DSP, an application specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

It should be noted that the embodiments of the present invention also provide a computer readable storage medium having instructions stored therein, which when executed on a computer, cause the computer to perform the methods and processes provided in the above embodiments.

The embodiment of the invention also provides a chip for running the instructions, which is used for executing the method and the processing procedure provided in the embodiment.

The embodiment of the present invention also provides a program product, which includes a computer program stored in a storage medium, from which at least one processor can read the computer program, and the at least one processor performs the method and the process provided in the embodiment.

The embodiment of the invention provides a blood pressure measuring method, a blood pressure measuring device, electronic equipment, a computer program product and a computer readable storage medium, wherein a conventional signal filtering mode is replaced by a mode of calculating the feasibility degree of pulse waves, and the blood pressure value of the pulse waves is calculated by taking the credibility degree as a reference, so that the measuring accuracy of the blood pressure measuring equipment in arrhythmia of a patient is improved.

Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of function in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (3)

1. A blood pressure measurement device, comprising:

the acquisition module is used for acquiring the pulse wave signal group; the pulse wave signal group comprises a plurality of pulse wave signals;

the trusted module is used for performing first trusted parameter calculation according to the time interval data of the adjacent pulse wave signals in the pulse wave signal group to generate first trusted parameter data; according to the maximum amplitude data of the pulse wave signals adjacent to the pulse wave signal group, performing second credible parameter calculation to generate second credible parameter data; according to the time width data of the adjacent pulse wave signals in the pulse wave signal group, third trusted parameter calculation is carried out, and third trusted parameter data are generated; performing reliability calculation according to the first, second and third credible parameter data to generate credibility data;

the first measuring and calculating module is used for carrying out envelope curve fitting processing on the pulse wave signal group to generate a signal envelope curve; extracting the maximum amplitude data of the signal envelope curve to generate maximum blood pressure data P _MAX The method comprises the steps of carrying out a first treatment on the surface of the According to the systolic pressure ratio threshold and the P _MAX Performing systolic pressure calculation to generate systolic pressure data; according to the diastolic pressure ratio threshold and the P _MAX Calculating the diastolic blood pressure to generate diastolic blood pressure data; according to the systolic pressure data and the diastolic pressure data, carrying out average pressure calculation to generate average pressure data; forming a blood pressure data set from the credibility data, the average pressure data, the systolic pressure data and the diastolic pressure data;

the second measuring and calculating module is used for acquiring a trusted decision mode; when the credible decision mode is a first mode, screening all blood pressure data sets with highest credibility to generate a first blood pressure measurement result data set; when the credible decision mode is a second mode, carrying out reasonable credibility blood pressure data set average value processing on all the blood pressure data sets according to a reasonable credibility threshold value to generate a second blood pressure measurement result data set;

the step of calculating a first trusted parameter according to time interval data of the pulse wave signals adjacent to each other in the pulse wave signal group to generate first trusted parameter data specifically includes:

sequentially counting time interval data of adjacent pulse wave signals in the pulse wave signal group to generate first time interval data; calculating absolute difference values of adjacent first time interval data to generate first differential data; generating first change rate data according to the ratio of the first differential data to the corresponding first time interval data;

Calculating the average value of all the first change rate data to generate the first credible parameter data;

and performing second trusted parameter calculation according to the maximum amplitude data of the pulse wave signals adjacent to the pulse wave signal group to generate second trusted parameter data, wherein the method specifically comprises the following steps of:

sequentially extracting maximum amplitude data of the pulse wave signals from the pulse wave signal group to generate first amplitude data; calculating absolute difference values of the adjacent first amplitude data to generate second differential data; generating second change rate data according to the ratio of the second differential data to the corresponding first amplitude data;

calculating the average value of all the second change rate data to generate second credible parameter data;

and performing third trusted parameter calculation according to time width data of the pulse wave signals adjacent to the pulse wave signal group to generate third trusted parameter data, wherein the method specifically comprises the following steps of:

sequentially extracting time width data of the pulse wave signals in the pulse wave signal group to generate first time width data; calculating absolute difference values of the adjacent first time width data to generate third differential data; generating third change rate data according to the ratio of the third differential data to the corresponding first time width data;

Calculating the average value of all the third change rate data to generate third credible parameter data;

the reliability calculation is performed according to the first, second and third reliability parameter data, and the reliability data is generated, which specifically includes:

summing up the first, second and third trusted parameter data to generate trusted parameter sum data; generating the credibility data according to the credibility data calculation formula and the credibility data= (1-credibility parameter sum data) x 100%;

further comprises: when the credibility parameter sum data is not less than 1, the credibility data is 0;

and performing envelope curve fitting processing on the pulse wave signal group to generate a signal envelope curve, wherein the method specifically comprises the following steps of:

in the pulse wave signal group, smoothly connecting the maximum amplitude point of each pulse wave signal to generate the signal envelope;

said according to a systolic pressure ratio threshold and said P _MAX Performing systolic pressure calculation to generate systolic pressure data, wherein the method specifically comprises the following steps:

according to the systolic pressure proportion threshold and the P _MAX According to the calculation formula of the systolic pressure data, systolic pressure data = systolic pressure proportion threshold value multiplied by P _MAX Generating the systolic pressure data;

said threshold value according to diastolic pressure ratio and said P _MAX Performing diastolic blood pressure calculation to generate diastolic blood pressure data, including:

according to the diastolic blood pressure proportion threshold value and the P _MAX According to a calculation formula of the diastolic blood pressure data, the diastolic blood pressure data=diastolic blood pressure proportion threshold value multiplied by P _MAX Generating the diastolic blood pressure data;

when the trusted decision mode is a first mode, performing highest-confidence blood pressure data set screening processing on all the blood pressure data sets to generate a first blood pressure measurement result data set, wherein the method specifically comprises the following steps of:

when the credible decision mode is the first mode, selecting the blood pressure data set with the largest numerical value of the credibility data from all the blood pressure data sets as a screening result data set; extracting the average pressure data, the systolic pressure data and the diastolic pressure data from the screening result data set to form the first blood pressure measurement result data set;

when the trusted decision mode is the second mode, performing reasonable reliability blood pressure data set average processing on all the blood pressure data sets according to a reasonable reliability threshold value to generate a second blood pressure measurement result data set, wherein the method specifically comprises the following steps of:

When the credible decision mode is the second mode, selecting the blood pressure data sets meeting the reasonable credibility threshold value from all the blood pressure data sets, and generating a reasonable credibility blood pressure data set sequence; in the reasonable credibility blood pressure data set sequence, the difference value between the credibility data of the maximum value and the credibility data of the minimum value does not exceed the reasonable credibility threshold;

in the reasonable credibility blood pressure data set sequence, carrying out average value calculation on all the average pressure data to generate average value average pressure data; calculating the average value of all the systolic pressure data to generate average value systolic pressure data; calculating the average value of all the diastolic blood pressure data to generate average diastolic blood pressure data;

and forming the second blood pressure measurement result data set by the mean average pressure data, the mean systolic pressure data and the mean diastolic pressure data.

2. An electronic device, comprising: memory, processor, and transceiver;

the processor is configured to couple with the memory, read and execute instructions in the memory to implement the apparatus of claim 1;

The transceiver is coupled to the processor and is controlled by the processor to transmit and receive messages.

3. A computer-readable storage medium storing computer instructions that, when executed by a computer, cause the computer to perform the instructions of the apparatus of claim 1.