Non-invasive blood pressure measurement method, system and device, computer equipment, medium and program product

Abstract

The invention relates to the technical field of measurement and detection, in particular to a non-invasive blood pressure measurement method, system and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring pressure measurement data, wherein the pressure measurement data comprises a sampling pressure value acquired based on a preset sampling frequency; determining a target acquisition interval based on a difference value between the continuous sampling pressure values, and obtaining a plurality of groups of pressure acquisition data in the target acquisition interval; according to extreme value information in each group of pressure acquisition data, determining difference value data of each group of pressure acquisition data to obtain a difference value array; a preset fitting algorithm is applied to obtain a fitting curve of the difference value array, and target blood pressure data is determined based on the fitting curve. By adopting the method, the application adaptability of the measurement method can be improved, the influence of environmental interference is reduced, and the accuracy of a measurement result is improved.

Description

Technical Field

[0001] The present application relates to the field of measurement and detection technology, and in particular to a non-invasive blood pressure measurement method, system, device, computer equipment, storage medium and computer program product. Background Art

[0002] Blood pressure is an important physiological parameter for assessing cardiovascular health and is crucial for preventing and managing a variety of health problems. In daily life, timely monitoring of blood pressure can help identify potential health problems and facilitate early detection and intervention of health risks such as high blood pressure and low blood pressure. Electronic blood pressure monitors are commonly used blood pressure monitoring tools in modern times. They are easy to use and intuitive to operate, making them suitable for home use. Electronic blood pressure monitors provide a convenient solution for family monitoring and management of blood pressure. Regularly measuring blood pressure can help people obtain health information in a timely manner and take appropriate lifestyle adjustments and medical measures.

[0003] In the related art, the existing electronic sphygmomanometer uses the oscillometric method, which can also be called the vibration method or the vibration measurement method. It is combined with a microprocessor and can be applied to rapid blood pressure measurement. At present, this method has been widely used in blood pressure monitors and home sphygmomanometers. The principle of the blood pressure measurement system based on the oscillometric method is very similar to the Korotkoff sound method. The blood pressure measurement is achieved by inflating the cuff to block the arterial blood flow: a cuff connected to a rubber catheter is put on the upper arm of the subject. The cuff has a built-in pressure sensor. The signal detected by the pressure sensor is the effect of the superposition of the static pressure of the cuff and the arterial pressure. Different from the Korotkoff sound method, the oscillometric method is controlled by a microprocessor through the measurement value of the pressure sensor to control the measurement process. The continuous change of arterial pressure with the static pressure of the cuff can be obtained. The systolic pressure, diastolic pressure, mean pressure and heart rate can be calculated through the algorithm. It is not easily disturbed by external sounds, has strong anti-interference, good repeatability, and small measurement error. Therefore, the oscillometric method is currently recognized as a non-invasive automatic blood pressure measurement method for monitors at home and abroad.

[0004] However, the current blood pressure measurement method has the following technical problems:

[0005] The accuracy of the results of existing blood pressure measurement methods needs to be improved. Summary of the invention

[0006] Based on this, it is necessary to provide a non-invasive blood pressure measurement method, device, computer equipment, computer-readable storage medium and computer program product that can improve the application adaptability of the measurement method, reduce the impact of environmental interference, and improve the accuracy of the measurement results in response to the above technical problems.

[0007] In a first aspect, the present application provides a non-invasive blood pressure measurement method. The method comprises:

[0008] Acquiring pressure measurement data, wherein the pressure measurement data includes sampled pressure values ​​acquired based on a preset sampling frequency;

[0009] Determine a target acquisition interval based on the difference between the continuous sampled pressure values, and obtain several groups of pressure acquisition data within the target acquisition interval;

[0010] Determine the difference data of each group of the pressure acquisition data according to the maximum value information in each group of the pressure acquisition data to obtain a difference array;

[0011] A preset fitting algorithm is applied to obtain a fitting curve of the difference array, and target blood pressure data is determined based on the fitting curve.

[0012] In one embodiment, the step of applying a preset fitting algorithm to obtain a fitting curve of the difference array and determining the target blood pressure data based on the fitting curve further includes:

[0013] The difference array is filtered based on a preset first filtering condition to obtain the filtered difference array, wherein the first filtering condition is used to filter out the difference data corresponding to an incomplete cardiac cycle.

[0014] In one embodiment, applying a preset fitting algorithm to obtain a fitting curve of the difference array, and determining the target blood pressure data based on the fitting curve includes:

[0015] Performing Gaussian fitting based on the filtered difference array to obtain a first fitting curve;

[0016] Filtering the difference array based on a preset second filtering condition and the first fitting curve to obtain the difference array filtered twice, wherein the second filtering condition is used to filter out the difference data whose discreteness with the first fitting curve exceeds a preset standard;

[0017] A second fitting is performed on the difference array that has been filtered twice to obtain a second fitting curve and a corresponding Gaussian equation, and the target blood pressure data is determined according to the Gaussian equation.

[0018] In one embodiment, the method of applying a preset fitting algorithm to obtain a fitting curve of the difference array and determining the target blood pressure data based on the fitting curve further includes:

[0019] determining mean arterial pressure data according to the target blood pressure data;

[0020] The target blood pressure data is verified based on a preset empirical algorithm and the mean arterial pressure data.

[0021] In one embodiment, the obtaining of pressure measurement data, wherein the pressure measurement data includes a sampled pressure value obtained based on a preset sampling frequency, includes:

[0022] Based on a preset default initial value, obtaining the pressure measurement data;

[0023] Or in response to obtaining setting information for the target device, obtaining the pressure measurement data based on a target initial value associated with the setting information.

[0024] In a second aspect, the present application also provides a non-invasive blood pressure measurement system, the system comprising:

[0025] A pressure detection module, used to obtain pressure detection data of a target object, the pressure detection module includes a filtering unit, the filtering unit is used to filter out interference information, the interference information includes environmental interference information;

[0026] A control module is connected to the pressure detection module, and is used to obtain the pressure measurement data output by the pressure detection module, and calculate the target blood pressure data of the target object according to a non-invasive blood pressure measurement method as described in any one of the first aspects.

[0027] In a third aspect, the present application also provides a non-invasive blood pressure measurement device. The device comprises:

[0028] A pressure measurement module, used to obtain pressure measurement data, wherein the pressure measurement data includes a sampled pressure value obtained based on a preset sampling frequency;

[0029] A data acquisition module, used to determine a target acquisition interval based on the difference between the continuous sampled pressure values, and obtain a plurality of groups of pressure acquisition data within the target acquisition interval;

[0030] A difference calculation module, used to determine the difference data of each group of the pressure acquisition data according to the maximum value information in each group of the pressure acquisition data, and obtain a difference array;

[0031] The fitting analysis module is used to apply a preset fitting algorithm to obtain a fitting curve of the difference array, and determine the target blood pressure data based on the fitting curve.

[0032] In one embodiment, before the fitting analysis module, it also includes:

[0033] The difference filtering module is used to filter the difference array based on a preset first filtering condition to obtain the filtered difference array, wherein the first filtering condition is used to filter out the difference data corresponding to an incomplete cardiac cycle.

[0034] In one embodiment, the fitting analysis module includes:

[0035] A primary fitting module, used for performing Gaussian fitting based on the filtered difference array to obtain a first fitting curve;

[0036] a residual filtering module, configured to filter the difference array based on a preset second filtering condition and the first fitting curve to obtain the difference array filtered twice, wherein the second filtering condition is configured to filter out the difference data whose discreteness from the first fitting curve exceeds a preset standard;

[0037] The quadratic fitting module is used to perform a quadratic fitting on the difference array that has been filtered twice to obtain a second fitting curve and a corresponding Gaussian equation, and determine the target blood pressure data according to the Gaussian equation.

[0038] In one embodiment, after the fitting analysis module, it also includes:

[0039] A mean arterial pressure module, used to determine mean arterial pressure data according to the target blood pressure data;

[0040] An empirical verification module is used to verify the target blood pressure data based on a preset empirical algorithm and the mean arterial pressure data.

[0041] In one embodiment, the pressure measurement module includes:

[0042] A default initialization module, used for obtaining the pressure measurement data based on a preset default initial value;

[0043] Or a setting initialization module is used for obtaining the pressure measurement data based on a target initial value associated with the setting information in response to obtaining setting information for the target device.

[0044] In a fourth aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps in the non-invasive blood pressure measurement method as described in any one of the embodiments in the first aspect are implemented.

[0045] In a fifth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the non-invasive blood pressure measurement method as described in any one of the embodiments in the first aspect are implemented.

[0046] In a sixth aspect, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps in the non-invasive blood pressure measurement method as described in any one of the embodiments in the first aspect are implemented.

[0047] The above-mentioned non-invasive blood pressure measurement method, device, computer equipment, storage medium and computer program product can achieve the following beneficial effects corresponding to the technical problems in the background technology by deducing the technical features in the claims:

[0048] The present application provides a non-invasive blood pressure measurement method, including obtaining pressure measurement data, wherein the pressure measurement data includes sampled pressure values ​​obtained based on a preset sampling frequency; determining a target acquisition interval based on the difference between the continuous sampled pressure values, and obtaining a plurality of groups of pressure acquisition data within the target acquisition interval; determining the difference data of each group of the pressure acquisition data according to the maximum value information in each group of the pressure acquisition data, and obtaining a difference array; applying a preset fitting algorithm to obtain a fitting curve of the difference array, and determining the target blood pressure data based on the fitting curve. In implementation, a group of pressure measurement data can be first obtained through a hardware device, and the pressure measurement data at this time is pressure data of a complete measurement process including multiple solenoid valve deflations, wherein some data are unstable due to the deflation process and have a low reference value, so a relatively stable target acquisition interval is screened out through the difference data between the continuous sampled pressure values, and the data in the target acquisition interval is obtained as an analysis object. After obtaining the pressure acquisition data, each group of pressure acquisition data can correspond to at least one cardiac cycle, and the pressure fluctuation amplitude caused by the heart beat can be obtained by obtaining the difference information of the maximum value and the minimum value of each group of pressure acquisition data. Finally, a set of difference data can be obtained, and the target blood pressure data can be determined by analyzing the fitting curve obtained by fitting the difference data. The target blood pressure data finally obtained can be adapted to individuals with different differences because the complete pressure measurement data can be analyzed and processed, and effective data can be screened according to the actual calculation situation, which helps to improve the application adaptability of the measurement method, reduce the impact of environmental interference, and improve the accuracy of the measurement results. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1is an application environment diagram of a non-invasive blood pressure measurement method in an embodiment;

[0051] Figure 2 is a first flow chart of a non-invasive blood pressure measurement method in one embodiment;

[0052] Figure 3 is a second flow chart of a non-invasive blood pressure measurement method in another embodiment;

[0053] Figure 4 is a third flow chart of a non-invasive blood pressure measurement method in another embodiment;

[0054] Figure 5 is a fourth flow chart of a non-invasive blood pressure measurement method in another embodiment;

[0055] Figure 6 is a fifth flow chart of a non-invasive blood pressure measurement method in another embodiment;

[0056] Figure 7 is a flow chart of a blood pressure measurement algorithm in a most specific embodiment;

[0057] Figure 8 is a flow chart of a blood pressure interval screening algorithm in a most specific embodiment;

[0058] Fig. 9 It is a flowchart of a blood pressure maximum value search algorithm in a most specific embodiment;

[0059] Fig.10 This is a schematic diagram of the relationship between cardiac cycle and blood pressure data;

[0060] Fig.11 This is a schematic diagram of the relationship between the cuff deflation pressure and the sampling point;

[0061] Fig.12 Schematic diagram of the process of deflation of cuff pressure;

[0062] Fig.13 It is a schematic diagram of the fitting curve obtained by one fitting;

[0063] Fig.14 It is a schematic diagram of the fitting curve obtained by quadratic fitting;

[0064] Fig.15 is a structural block diagram of a non-invasive blood pressure measurement device in one embodiment;

[0065] Fig.16 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0067] In the related art, the existing electronic sphygmomanometer uses the oscillometric method, which can also be called the vibration method or the vibration measurement method. It is combined with a microprocessor and can be applied to rapid blood pressure measurement. At present, this method has been widely used in blood pressure monitors and home sphygmomanometers. The principle of the blood pressure measurement system based on the oscillometric method is very similar to the Korotkoff sound method. The blood pressure measurement is achieved by inflating the cuff to block the arterial blood flow: a cuff connected to a rubber catheter is put on the upper arm of the subject. The cuff has a built-in pressure sensor. The signal detected by the pressure sensor is the effect of the superposition of the static pressure of the cuff and the arterial pressure. Different from the Korotkoff sound method, the oscillometric method is controlled by a microprocessor through the measurement value of the pressure sensor to control the measurement process. The continuous change of arterial pressure with the static pressure of the cuff can be obtained. The systolic pressure, diastolic pressure, mean pressure and heart rate can be calculated through the algorithm. It is not easily disturbed by external sounds, has strong anti-interference, good repeatability, and small measurement error. Therefore, the oscillometric method is currently recognized as a non-invasive automatic blood pressure measurement method for monitors at home and abroad.

[0068] However, the current blood pressure measurement method has the following technical problems:

[0069] The accuracy of the results of existing blood pressure measurement methods needs to be improved.

[0070] Based on this, embodiments of the present application provide a non-invasive blood pressure measurement method, system, apparatus, computer device, storage medium and computer program product.

[0071] The non-invasive blood pressure measurement method provided in the embodiment of the present application can be applied to Figure 1 A non-invasive blood pressure measurement system is shown. The system includes: a pressure detection module and a control module, specifically: the pressure detection module is used to obtain pressure detection data of a target object, the pressure detection module includes a filter unit, the filter unit is used to filter out interference information, and the interference information includes environmental interference information; the control module is connected to the pressure detection module, and is used to obtain the pressure measurement data output by the pressure detection module and calculate the target blood pressure data of the target object.

[0072] In one embodiment, Figure 2 As shown, a non-invasive blood pressure measurement method is provided, which is applied to Figure 1 The control module in the example is used to illustrate, including the following steps:

[0073] Step 202: Obtain pressure measurement data, where the pressure measurement data includes sampled pressure values ​​obtained based on a preset sampling frequency.

[0074] Exemplarily, the control module may acquire pressure measurement data, wherein the pressure measurement data includes sampled pressure values ​​acquired based on a preset sampling frequency, and the sampled pressure values ​​at this time have been preliminarily filtered out of part of environmental interference information through self-filtering processing by the pressure detection module.

[0075] Step 204: determining a target acquisition interval based on the difference between the consecutive sampled pressure values, and acquiring a plurality of groups of pressure acquisition data within the target acquisition interval.

[0076] Exemplarily, in order to filter out the blood pressure measurement data in the constant interval during the non-deflation process, the control module can obtain it by comparing the three consecutive sampled pressure values ​​A, B, and C. Because during the deflation process, the value of point C usually decreases by a large margin, the previous difference must be smaller than the next difference, and the next difference is usually greater than 4 mmHg, so it is judged whether the current data is in the decreasing interval by this, and the data is not collected. When the data interval tends to be stable and there is not much fluctuation, that is, the absolute value of the difference between the three points is small, and the sum is not greater than 5, the algorithm determines that the data is in a stable interval, and data can be collected until the next difference is much larger than the previous difference, and it is judged that it starts to decrease and the collection is stopped. The units of the numbers such as 4 and 5 in the algorithm are mmHg, and their values ​​are related to the deflation time of the solenoid valve and the type of pressure sensor. In a blood pressure measurement process, the number of intervals is related to the tightness of the cuff and the width of the human arm circumference, and is a variable. Therefore, each new pressure interval is conducive to the next step of finding the maximum value, and is also friendly to different test groups.

[0077] In this way, the control module can determine the target acquisition interval that meets the acquisition requirements and acquire several groups of pressure acquisition data.

[0078] Step 206: Determine the difference data of each group of the pressure acquisition data according to the maximum value information in each group of the pressure acquisition data to obtain a difference array.

[0079] As a premise, the generation of the maximum blood pressure value is related to the human cardiac cycle. Every time the heart beats, the blood vessels in the human body will expand, the blood pumping equivalent will increase, and the pressure on the surface of the blood vessels will increase. The peak pressure represents the systolic pressure of the human body; when the heart enters the diastolic phase, the surface of the blood vessels shrinks from the expanded state to the normal state, and the blood vessel pressure at this time is the diastolic pressure. When the cuff is pressed against the blood vessels of the upper arm of the human body, different vascular pressure fluctuation amplitudes can be obtained from the constant period of different cuff pressures. And the relationship between the pressure fluctuation amplitude and the pressure when the cuff is deflated must satisfy a Gaussian curve that rises first and then falls. And the curve must have a maximum value; in the oscillometric method, the maximum pressure fluctuation amplitude represents the mean arterial pressure MAP of the human body.

[0080] Exemplarily, after acquiring the pressure acquisition data, the control module may determine the difference data of each group of the pressure acquisition data according to the maximum value information in each group of the pressure acquisition data to obtain a difference array.

[0081] Step 208: Apply a preset fitting algorithm to obtain a fitting curve of the difference array, and determine target blood pressure data based on the fitting curve.

[0082] Exemplarily, after obtaining the difference array, the control module may apply a preset fitting algorithm to obtain a fitting curve of the difference array, and determine the target blood pressure data based on the fitting curve.

[0083] In the above non-invasive blood pressure measurement method, reasonable deduction is performed in combination with the technical features in the embodiment to achieve the following beneficial effects of solving the technical problems raised in the background technology:

[0084] The present application provides a non-invasive blood pressure measurement method, including obtaining pressure measurement data, wherein the pressure measurement data includes sampled pressure values ​​obtained based on a preset sampling frequency; determining a target acquisition interval based on the difference between the continuous sampled pressure values, and obtaining a plurality of groups of pressure acquisition data within the target acquisition interval; determining the difference data of each group of the pressure acquisition data according to the maximum value information in each group of the pressure acquisition data, and obtaining a difference array; applying a preset fitting algorithm to obtain a fitting curve of the difference array, and determining the target blood pressure data based on the fitting curve. In implementation, a group of pressure measurement data can be first obtained through a hardware device, and the pressure measurement data at this time is pressure data of a complete measurement process including multiple solenoid valve deflations, wherein some data are unstable due to the deflation process and have a low reference value, so a relatively stable target acquisition interval is screened out through the difference data between the continuous sampled pressure values, and the data in the target acquisition interval is obtained as an analysis object. After obtaining the pressure acquisition data, each group of pressure acquisition data can correspond to at least one cardiac cycle, and the pressure fluctuation amplitude caused by the heart beat can be obtained by obtaining the difference information of the maximum value and the minimum value of each group of pressure acquisition data. Finally, a set of difference data can be obtained, and the target blood pressure data can be determined by analyzing the fitting curve obtained by fitting the difference data. The target blood pressure data finally obtained can be adapted to individuals with different differences because the complete pressure measurement data can be analyzed and processed, and effective data can be screened according to the actual calculation situation, which helps to improve the application adaptability of the measurement method, reduce the impact of environmental interference, and improve the accuracy of the measurement results.

[0085] In one embodiment, if Figure 3 As shown, before step 208, the following steps are also included:

[0086] Step 302: Filter the difference array based on a preset first filtering condition to obtain the filtered difference array.

[0087] The first filtering condition may be used to filter out the difference data corresponding to an incomplete cardiac cycle. In a specific implementation, a difference threshold may be set to filter out data with too large or too small difference, which usually indicates abnormal fluctuations or noise and is invalid information.

[0088] Exemplarily, the control module may filter the difference value array based on a preset first filtering condition to obtain the filtered difference value array.

[0089] In this embodiment, before performing the fitting process, the difference data is first filtered. The difference data corresponding to the incomplete cardiac cycle is filtered out through the first filtering, which helps to enhance the efficiency of the data, reduce interference factors in the calculation, and ultimately help to improve the accuracy of blood pressure measurement.

[0090] In one embodiment, if Figure 4 As shown, step 208 includes:

[0091] Step 402: Perform Gaussian fitting based on the filtered difference array to obtain a first fitting curve.

[0092] Exemplarily, the control module may perform Gaussian fitting based on the filtered difference array to obtain a first fitting curve. Gaussian fitting is performed on the filtered data, which may be achieved by a least square method or the like. The fitting result provides a curve representing the data distribution.

[0093] Step 404: filtering the difference array based on a preset second filtering condition and the first fitting curve to obtain the difference array filtered twice.

[0094] The second filtering condition is used to filter out the difference data whose degree of discreteness from the first fitting curve exceeds a preset standard.

[0095] Exemplarily, the control module may calculate the residual between the first fitting curve and the difference array, and filter out values ​​greater than or less than a standard deviation threshold, which can further remove outliers and enhance the reliability of the data.

[0096] Step 406: Perform a quadratic fit on the difference array that has been filtered twice to obtain a second fitting curve and a corresponding Gaussian equation, and determine the target blood pressure data according to the Gaussian equation.

[0097] Exemplarily, the control module may perform Gaussian fitting on the data that has been filtered twice, so as to obtain a more accurate second fitting curve and Gaussian equation.

[0098] In this embodiment, in the specific fitting process, two Gaussian fittings are performed and the fitting residuals are filtered after the first Gaussian fitting, which helps to remove outliers in the data again on the basis of difference filtering, thereby enhancing the reliability of the data. Finally, a secondary fitting is performed on the data after the two filtrations to obtain the final fitting curve and Gaussian equation, thereby improving the accuracy of the blood pressure measurement results.

[0099] In one embodiment, if Figure 5 As shown, after step 208, the following steps are further included:

[0100] Step 502: Determine mean arterial pressure data according to the target blood pressure data.

[0101] Exemplarily, the control module may extract a maximum value from the final Gaussian equation, which maximum value represents the mean arterial pressure (MAP).

[0102] Step 504: Verify the target blood pressure data based on a preset empirical algorithm and the mean arterial pressure data.

[0103] Exemplarily, the control module may verify the target blood pressure data based on a preset empirical algorithm and the mean arterial pressure data. The empirical algorithm may be as shown in the following formula:

[0104] Mean arterial pressure (MAP) =

[0105] In this embodiment, after obtaining the blood pressure data, the target blood pressure data is verified based on the empirical algorithm. The combined verification method helps to determine the correlation between the algorithm results and the empirically derived results. If the correlation is good, it can provide a certain reliability support for the algorithm, which helps to improve the accuracy of the blood pressure measurement results.

[0106] In one embodiment, if Figure 6 As shown, step 202 includes:

[0107] Step 602: Based on a preset default initial value, obtain the pressure measurement data.

[0108] Step 604: Or in response to obtaining setting information for the target device, obtaining the pressure measurement data based on a target initial value associated with the setting information.

[0109] In this embodiment, before performing pressure measurement, different initial values ​​can be set and selected so that the obtained pressure measurement data is more consistent with the individual characteristics of the measurement object, which helps to improve the overall efficiency of subsequent algorithm processing.

[0110] In a most specific embodiment, a non-invasive blood pressure measurement method provided in the embodiment of the present application mainly includes: a pulse wave screening algorithm, a pulse wave difference extraction algorithm, a pulse wave first Gaussian fitting method, a pulse wave filtering algorithm, a pulse wave second Gaussian fitting algorithm, and a blood pressure value search algorithm. The specific values ​​involved in the specific examples are all example values ​​and are not unique. The data selected in actual applications need to be set by technical personnel.

[0111] Specifically, Figure 7As shown in the figure, when a blood pressure measurement is completed, the system records a set of pressure sensor data and filters this set of data. Before starting to measure blood pressure, preset the measurement mode, the default is adult mode, and the pressure range can be selected. If it is a child or newborn measurement, the measurement mode needs to be changed to the corresponding measurement. When the pressure is greater than 75mmHg, the solenoid valve deflation time is short. This is due to the nonlinearity of the cuff deflation rate. When the pressure in the cuff is small, the deflation volume will become less and less during the same deflation time, so the sampling interval will increase, affecting the measurement accuracy. Therefore, when the pressure is less than 75mmHg, increase the cuff pressure deflation time to ensure that the sampling interval is evenly distributed.

[0112] Specifically, Figure 8 As shown in the figure, in order to filter out the blood pressure measurement data in the constant interval during the non-deflation process, it can be obtained by comparing every three values. Because during the deflation process, the value of point C usually decreases significantly, the previous difference must be smaller than the next difference, and the next difference is usually greater than 4mmHg, so this is used to determine whether the current data is in a decreasing interval and the data is not collected. When the data interval tends to be stable and there is not much fluctuation, that is, the absolute value of the difference between the three points is small, and the sum is not greater than 5, the algorithm determines that the data is in a stable interval and can start collecting data until the next difference is much larger than the previous difference, and it is judged that it starts to decrease and stops collecting. The units of the numbers such as 4 and 5 in the algorithm are mmHg, and their values ​​are related to the deflation time of the solenoid valve and the type of pressure sensor. In a blood pressure measurement process, the number of intervals is related to the tightness of the cuff and the width of the human arm circumference, and is a variable. Therefore, each new pressure interval is conducive to the next step of finding the maximum value, and is also friendly to different test groups. The control module can compare the difference between every three data. When the first difference of the two differences is less than 4 and the second difference is greater than 4, the interval is judged to be in the deflation interval and no data is collected. When both differences are less than 4 and the sum is less than 5, the interval is judged to be in the stable interval and the data of this interval is collected. The cycle is repeated until the last set of data is collected.

[0113] Specifically, Fig. 9 As shown in , when the interval screening is completed, N groups of data are obtained, where N represents the number of intervals, which is related to the number of discharges. The difference of these N groups of data is calculated. Fig.10 As shown in the figure, in the search algorithm, the peak is determined by three points, that is, the peak is greater than the previous point and the next point at the same time. When the peak is found, the minimum value of the next 4-7 points needs to be obtained. Since the cardiac cycle varies from person to person, the algorithm determines that the minimum value is within this range. Then the maximum and minimum difference of a cardiac cycle is obtained. Fig.11 and 12 As shown, Fig.11 and 12The actual pressure value of the pressure sensor during the deflation process of the cuff and the sampling point diagram. When it is judged that the 7 points after the peak do not exist or are missing, it is determined that the data collection of this peak is insufficient and this peak is filtered. Enter a set of data, and judge the relationship between the middle value and other values ​​for every three values. When the middle value is greater than both the previous value and the next value, the middle value is judged as the peak value. After recording the peak value, judge the minimum value between the 4, 5, 6, and 7 points after the peak value. The judgment of the 4th to 7th values ​​is related to the number of heartbeats per minute. The slower the heartbeat, the farther the minimum value. Then get the difference between the maximum and minimum values ​​of this cardiac cycle. In this cycle, get a set of difference data.

[0114] Specifically, by searching for the difference of each cardiac cycle, an array containing all the difference values ​​is obtained, and then the array is analyzed, filtered, and fitted. Fig.13 As shown in the figure below, it is observed that the difference first increases and then decreases, and there must be a maximum value. When we directly fit according to the figure below, we find that the root mean square error of the discrete fitting curve is very large. According to the principle of oscillometric method, the pressure interval where the maximum value is located, that is, the cuff pressure where the 16th cardiac cycle is located, is the mean arterial pressure interval of the tester. However, in order to obtain systolic and diastolic pressures, the control module still needs to Fig.13 Data processing is performed to filter out non-full cardiac cycles. Fig.13 As shown in , during the process of the difference rising, it often happens that when the previous point has a maximum value, the next point is often smaller than the previous maximum value. This is because the change in vascular pressure caused by the first pulse fluctuation after the cuff pressure is deflated is insufficient; during the first cardiac cycle after the cuff pressure is deflated, the blood has not yet completely returned to the entire blood vessel, and the change in vascular pressure caused by the cardiac cycle is insufficient. In the next two heart beats, the blood gradually returns to the entire blood vessel, so our pulsation intensity will gradually increase during the time when the cuff pressure is maintained in a constant interval after the cuff pressure is deflated. Based on this, we can filter out the first two points with small differences, and retain the point with the largest difference in each constant interval, such as Fig.14 As shown. After screening the maximum points and performing Gaussian fitting, we get the Gaussian fitting curve of the maximum sampling points. We find the discrete fitting curve, which shows that it is fitted through these 12 points, and the continuous fitting curve is fitted by setting the sampling rate to 100 times that of the discrete curve. Then, according to the maximum value point in the curve, 0.55 times is the interval where the systolic pressure is located, and it must be greater than the mean arterial pressure. Then search forward to get the systolic pressure, and then the interval where 0.75 times the maximum value is located is the diastolic pressure, which must be less than the mean arterial pressure. Search backward to get the diastolic pressure and compare it with the following empirical formula:

[0115] Mean arterial pressure (MAP) =

[0116] The control module obtains the systolic pressure and MAP through the Gaussian curve, and then obtains the empirical diastolic pressure through the empirical formula. Similarly, the control module can search for the data on the right side of the maximum value, take the pressure value corresponding to the point of 0.8 times the maximum value, which is the diastolic pressure, and obtain the empirical systolic pressure in the same way as above. In this way, the implementation of the blood pressure algorithm is completed once, and 5 pressure values ​​are obtained.

[0117] Finally, the blood pressure measurement of the target object is achieved.

[0118] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0119] Based on the same inventive concept, the embodiment of the present application also provides a non-invasive blood pressure measurement device for implementing the non-invasive blood pressure measurement method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more embodiments of a non-invasive blood pressure measurement device provided below can refer to the limitations of a non-invasive blood pressure measurement method above, and will not be repeated here.

[0120] In one embodiment, Fig.15 As shown, a non-invasive blood pressure measurement device is provided, including: a pressure measurement module, a data acquisition module, a difference calculation module and a fitting analysis module, wherein:

[0121] A pressure measurement module, used to obtain pressure measurement data, wherein the pressure measurement data includes a sampled pressure value obtained based on a preset sampling frequency;

[0122] A data acquisition module, used to determine a target acquisition interval based on the difference between the continuous sampled pressure values, and obtain a plurality of groups of pressure acquisition data within the target acquisition interval;

[0123] A difference calculation module, used to determine the difference data of each group of the pressure acquisition data according to the maximum value information in each group of the pressure acquisition data, and obtain a difference array;

[0124] The fitting analysis module is used to apply a preset fitting algorithm to obtain a fitting curve of the difference array, and determine the target blood pressure data based on the fitting curve.

[0125] In one embodiment, before the fitting analysis module, it also includes:

[0126] The difference filtering module is used to filter the difference array based on a preset first filtering condition to obtain the filtered difference array, wherein the first filtering condition is used to filter out the difference data corresponding to an incomplete cardiac cycle.

[0127] In one embodiment, the fitting analysis module includes:

[0128] A primary fitting module, used for performing Gaussian fitting based on the filtered difference array to obtain a first fitting curve;

[0129] a residual filtering module, configured to filter the difference array based on a preset second filtering condition and the first fitting curve to obtain the difference array filtered twice, wherein the second filtering condition is configured to filter out the difference data whose discreteness from the first fitting curve exceeds a preset standard;

[0130] The quadratic fitting module is used to perform a quadratic fitting on the difference array that has been filtered twice to obtain a second fitting curve and a corresponding Gaussian equation, and determine the target blood pressure data according to the Gaussian equation.

[0131] In one embodiment, after the fitting analysis module, it also includes:

[0132] A mean arterial pressure module, used to determine mean arterial pressure data according to the target blood pressure data;

[0133] An empirical verification module is used to verify the target blood pressure data based on a preset empirical algorithm and the mean arterial pressure data.

[0134] In one embodiment, the pressure measurement module includes:

[0135] A default initialization module, used for obtaining the pressure measurement data based on a preset default initial value;

[0136] Or a setting initialization module is used for obtaining the pressure measurement data based on a target initial value associated with the setting information in response to obtaining setting information for the target device.

[0137] Each module in the above-mentioned non-invasive blood pressure measurement device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module above.

[0138] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig.16 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a non-invasive blood pressure measurement method is realized. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0139] Those skilled in the art will understand that Fig.16 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0140] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0141] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0142] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0143] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0144] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0145] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0146] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A non-invasive blood pressure measurement method, characterized in that: The method comprises: Acquiring pressure measurement data, wherein the pressure measurement data includes sampled pressure values ​​acquired based on a preset sampling frequency; Determine a target acquisition interval based on the difference between the continuous sampled pressure values, and obtain several groups of pressure acquisition data within the target acquisition interval; Determine the difference data of each group of the pressure acquisition data according to the maximum value information in each group of the pressure acquisition data to obtain a difference array; A preset fitting algorithm is applied to obtain a fitting curve of the difference array, and target blood pressure data is determined based on the fitting curve.

2. The method according to claim 1, characterized in that The step of applying a preset fitting algorithm to obtain a fitting curve of the difference array and determining the target blood pressure data based on the fitting curve further includes: The difference array is filtered based on a preset first filtering condition to obtain the filtered difference array, wherein the first filtering condition is used to filter out the difference data corresponding to an incomplete cardiac cycle.

3. The method according to claim 2, characterized in that The step of applying a preset fitting algorithm to obtain a fitting curve of the difference array, and determining target blood pressure data based on the fitting curve comprises: Performing Gaussian fitting based on the filtered difference array to obtain a first fitting curve; Filtering the difference array based on a preset second filtering condition and the first fitting curve to obtain the difference array filtered twice, wherein the second filtering condition is used to filter out the difference data whose discreteness with the first fitting curve exceeds a preset standard; A second fitting is performed on the difference array that has been filtered twice to obtain a second fitting curve and a corresponding Gaussian equation, and the target blood pressure data is determined according to the Gaussian equation.

4. The method according to claim 1, characterized in that The method further comprises: applying a preset fitting algorithm to obtain a fitting curve of the difference array, and determining the target blood pressure data based on the fitting curve, and further comprising: determining mean arterial pressure data according to the target blood pressure data; The target blood pressure data is verified based on a preset empirical algorithm and the mean arterial pressure data.

5. The method according to any one of claims 1 to 4, characterized in that: The obtaining of pressure measurement data, wherein the pressure measurement data includes a sampled pressure value obtained based on a preset sampling frequency, includes: Based on a preset default initial value, obtaining the pressure measurement data; Or in response to obtaining setting information for the target device, obtaining the pressure measurement data based on a target initial value associated with the setting information.

6. A non-invasive blood pressure measurement system, characterized in that: The system comprises: A pressure detection module, used to obtain pressure detection data of a target object, the pressure detection module includes a filtering unit, the filtering unit is used to filter out interference information, the interference information includes environmental interference information; A control module is connected to the pressure detection module, and is used to obtain the pressure measurement data output by the pressure detection module, and calculate the target blood pressure data of the target object according to a non-invasive blood pressure measurement method as described in any one of claims 1 to 5.

7. A non-invasive blood pressure measurement device, characterized in that: The device comprises: A pressure measurement module, used to obtain pressure measurement data, wherein the pressure measurement data includes a sampled pressure value obtained based on a preset sampling frequency; A data acquisition module, used to determine a target acquisition interval based on the difference between the continuous sampled pressure values, and obtain a plurality of groups of pressure acquisition data within the target acquisition interval; A difference calculation module, used to determine the difference data of each group of the pressure acquisition data according to the maximum value information in each group of the pressure acquisition data, and obtain a difference array; The fitting analysis module is used to apply a preset fitting algorithm to obtain a fitting curve of the difference array, and determine the target blood pressure data based on the fitting curve.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images