Blood pressure measurement system and method based on oscillatory wave intelligent simplification and transmission

Abstract

The invention provides a blood pressure measurement system and method based on oscillatory wave intelligent simplification and transmission, and relates to the technical field of wearable medical health monitoring. Comprising a pressure sensing module, a data preprocessing module, a signal quality evaluation module, a waveform simplification module, an availability evaluation module and an information processing module. The method comprises the following steps: continuously acquiring cuff pressure data on an arm of a user in real time through a cuff pressure sensor, acquiring real-time cuff pressure, and acquiring a real-time value of cuff pressure oscillatory waves; an air pressure sensor is arranged on an air pipe of a sphygmomanometer cuff, a voltage comparator is additionally arranged in the air pressure sensor to monitor the pressure change in the cuff, a serial port chip converts data transmitted by a chip SOC and serial port data mutually, and through a 4G / 5G antenna, a GPS / GNSS antenna and an identity recognition module which are integrated, the pressure change in the cuff is monitored. Transmitting the output simplified oscillation wave waveform signal, the cuff pressure signal, the real-time position information and the personal information of the testee to a health cloud system, and establishing a personal blood pressure monitoring database.

Description

Technical Field

[0001] The present invention relates to the technical field of wearable medical health monitoring, and particularly to a blood pressure measurement system and method based on intelligent simplification and transmission of oscillatory waves. Background Art

[0002] In the wave of digital medical development, intelligent sphygmomanometers have been widely popularized, which can conveniently collect and record blood pressure data. However, there are prominent technical problems in this field at present. The function of uploading data to the network is not yet mature, it is difficult to dock data with health cloud services, and the analysis and diagnosis of data rely on professional doctors, which not only increases the burden on medical resources but also reduces the user experience. If you want to use health cloud services, you need to purchase another networked device, which increases the economic cost. Although the traditional oscillatory wave method is widely used, the original oscillatory wave waveform signal has a huge amount of data and a large amount of calculation. This requires high storage capacity for the device, increasing the storage cost and complexity. When calculating, it also consumes a large amount of resources and time, with low efficiency. When uploading to the cloud, the speed is slow and it is easy to interrupt, which is not conducive to real-time sharing of data and the development of telemedicine.

[0003] Chinese Patent CN112185499A can collect users' blood pressure data at a preset location, identify personal information and create an electronic file, and can also remotely monitor dynamic blood pressure to update the file, providing convenience for the data control of patients with hypertension and chronic diseases. However, it cannot save the oscillatory wave waveform data, which is not conducive to obtaining accurate blood pressure values using optimization algorithms, and it is difficult to simply transmit waveform data. Chinese Patent CN118919075A uses a variety of components to analyze the correlation between blood pressure fluctuations and noise-sensitive characteristics, realizing comparative analysis of blood pressure at different times and warning prompts, improving the monitoring effectiveness. However, the amount of original oscillatory wave waveform data it processes is huge, and the requirements for device performance are too high, which is not suitable for personal use scenarios. Chinese Patent CN118749931A can obtain patients' blood pressure data in real time, construct a model to predict abnormal blood pressure moments after preprocessing and calibration, optimizing the medical process. However, the data generated by data processing is huge, the requirements for the storage capacity of the storage device are high, and it is easy to have problems such as slow speed and interruption during transmission, which also limits the lightweight and portability of the device.

[0004] In the prior art, for the preservation and transmission of blood pressure data, the directly measured SBP and DBP values are mostly used, rather than transmitting oscillatory wave signals. The results measured by different measurement devices and methods vary greatly, which is not conducive to the management and analysis of blood pressure. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a blood pressure measurement system and method based on intelligent simplification and transmission of oscillatory waves, realizing simple and feasible data reduction and blood pressure network monitoring with strong adaptability.

[0006] On the one hand, a blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves, comprising a pressure sensing module, a data preprocessing module, a signal quality assessment module, a waveform simplification module, an availability assessment module, and an information processing module; The data preprocessing module, the signal quality assessment module, the waveform simplification module, the availability assessment module, and the information processing module are integrated on a chip SOC; The pressure sensing module is disposed on the cuff trachea, receives the cuff pressure by sensing the pressure change in the cuff trachea, converts the pressure signal into an electrical signal, and acquires the cuff pressure and the oscillatory wave signal; The data preprocessing module is used to filter the oscillatory wave signal, process the waveform using lifting wavelet transform, and pre-extract key points of the waveform; The signal quality assessment module receives the preprocessed oscillatory wave signal, screens out signals with poor quality assessment and removes them; The waveform simplification module simplifies each complete single-cycle waveform after preprocessing based on the Douglas-Peucker algorithm; The availability assessment module determines whether the simplified waveform is usable by calculating the root mean square error and the correlation coefficient between the simplified waveform and the original waveform and comparing them with a preset threshold; The information processing module is responsible for outputting and storing the simplified oscillatory wave waveform signal, restoring the original oscillatory wave waveform, collecting personal identity information and positioning functions, and uploading them to the health cloud system; The blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves further includes a pressure control modification module; the pressure control modification module is separately installed on the blood pressure measurement system, provides a micro air pump connected to the chip SOC and a display screen, is used to inflate the cuff when there is no blood pressure monitor to provide pressure, and receives the blood pressure value analyzed by the health cloud system.

[0007] The pressure sensing module continuously collects the cuff pressure data on the user's arm in real time through a cuff pressure sensor. The AC isolation circuit and filtering circuit of the cuff pressure convert the AC signal of the cuff pressure into a DC signal to obtain the real-time cuff pressure; the DC isolation circuit and filtering circuit of the cuff pressure are responsible for isolating the DC signal of the cuff pressure to obtain the AC signal of the cuff pressure and collect the real-time value of the oscillatory wave of the cuff pressure. A barometric pressure sensor is provided on the trachea of the blood pressure monitor cuff, a voltage comparator is added to the barometric pressure sensor to monitor the pressure change in the cuff, and the serial port chip converts the data transmitted by the chip SOC and the serial port data with each other. Through the integrated 4G / 5G antenna, GPS / GNSS antenna, and identity recognition module, the output simplified oscillatory wave waveform signal, cuff pressure signal, real-time position information, and tester personal information are transmitted to the health cloud system to establish a personal blood pressure monitoring database; The health cloud system stores users' personal information, original oscillation wave data, and simplified oscillation wave data; it is also used to accurately restore the simplified waveform, analyze blood pressure data, judge blood pressure conditions, provide health advice and warnings; it also supports external interaction and feeds back the analyzed blood pressure values to the pressure control modification module.

[0008] On the other hand, a blood pressure measurement method based on intelligent simplification and transmission of oscillation waves is implemented based on the aforementioned blood pressure measurement system based on intelligent simplification and transmission of oscillation waves, and includes the following steps: Step 1: Obtaining the original oscillation wave signal of the sample to be recognized; Step 1.1: The pressure sensor in the pressure sensing module can sense the pressure change in the cuff trachea and convert it into an electrical signal; Step 1.2: Obtaining the DC and AC signals of the cuff pressure; Step 1.2.1: The AC isolation and filtering circuit of the cuff pressure converts the AC signal of the cuff pressure into a DC signal and collects the real-time value of the cuff pressure; Step 1.2.2: The DC isolation and filtering circuit of the cuff pressure is responsible for isolating the DC signal of the cuff pressure and obtaining the AC signal of the cuff pressure, which is used to collect the real-time value of the oscillation wave of the cuff pressure; Step 1.2.3: A voltage comparator continuously outputs a high level when the air pressure is greater than 20 mmHg, and the high level triggers to wake up the SOC, the DC isolation circuit of the cuff pressure, the AC isolation circuit of the cuff pressure, the filtering circuit, the serial port chip, the 4G / 5G antenna, the GPS / GNSS antenna, and the identity recognition module.

[0009] Step 2: Preprocessing the obtained oscillation wave signal, including low-pass filtering and high-pass filtering, optimizing the noise and baseline drift in the oscillation wave signal, and designing a band-pass filter based on the lifting wavelet transform to restore the first harmonics of the two sidebands of the oscillation wave; Step 2.1: Using a low-pass filter to remove the high-frequency noise in the original oscillation wave signal; Step 2.2: Using high-pass filtering to remove the influence of baseline drift on the oscillation wave; Step 2.3: Constructing a band-pass filter based on the lifting wavelet transform to process each cycle of the oscillation wave and eliminate artifacts; Step 2.4: Performing key point pre-extraction, detecting the peaks and valleys of each single-cycle waveform, and first identifying the peaks and valleys through the sliding window method; Step 3: Evaluating the quality of the preprocessed oscillation wave by calculating the signal-to-noise ratio, detecting periodicity, and checking the uniformity of the data point distribution; Step 3.1: Calculating the signal-to-noise ratio to evaluate the noise level in the waveform; When calculating the signal-to-noise ratio, the signal and noise should be defined first. Define two adjacent wave troughs as the starting and ending points of the corresponding single-cycle waveform, and extract the single-cycle waveform; calculate the signal power and noise power of the single-cycle waveform; calculate the ratio of the signal power to the noise power to obtain the signal-to-noise ratio. Adopt the method of dynamic noise baseline modeling to obtain the signal-to-noise ratio threshold by synthesizing the baseline noise level and the current noise level. Step 3.2: Detect periodicity and evaluate whether the waveform has obvious periodicity. The detection of periodicity adopts the autocorrelation method. Perform autocorrelation analysis on the preprocessed waveform signal, calculate the autocorrelation function at different time lags and standardize it, determine the period of the signal after finding the peak of the autocorrelation function, and verify its periodicity through consistency checks. Step 3.3: Check the uniformity of the data point distribution and evaluate whether the distribution of the waveform data points is uniform. To check the uniformity of the data point distribution, it is necessary to calculate the standard deviation of the time intervals between data points and the mean of the time intervals, and judge whether the time intervals conform to the uniform distribution through the Kolmogorov-Smirnov test. Step 4: Simplify the waveform based on the Douglas-Peucker algorithm: First, determine the starting and ending points of the single-cycle waveform; then calculate the perpendicular distance from each point in the waveform to the straight line formed by the starting and ending points through the method of vector cross product; find the point with the maximum perpendicular distance and compare it with the threshold. If the perpendicular distance is greater than the threshold, retain the point with the maximum perpendicular distance as the key point. Otherwise, it is considered that the current straight line can represent the single-cycle waveform being simplified; for the found key points, divide the waveform into two segments and process them recursively, repeating the calculation of the perpendicular distance until all perpendicular distances are less than the threshold; and by continuously adjusting the threshold, ensure that each complete single-cycle waveform retains 20% of the original data and at least 2 feature points. Step 4.1: Select a complete single-cycle waveform for waveform simplification, and determine two adjacent wave troughs as the starting and ending points of the complete single-cycle waveform. Step 4.2: For each point in the waveform, use the vector cross product to calculate its perpendicular distance to the straight line formed by the starting and ending points. Step 4.3: Find the point with the maximum perpendicular distance. If this distance is less than the given threshold, it is considered that there are no key points to be retained and the current straight line can represent the single-cycle waveform being simplified. Otherwise, it is considered that the point with the maximum perpendicular distance is a key point to be retained. Step 4.4: For the found key points, divide the waveform into two segments and process these two segments recursively, repeating Step 4.3 until all key points are processed. Step 4.5: Ensure that each complete single-cycle waveform retains 20% of the feature points of the original data by adjusting the threshold. Step 4.6: Combine all the remaining key points to form a simplified waveform. Step 5: Evaluate the usability of the simplified waveform signal. By calculating the root mean square error and correlation coefficient between the simplified waveform and the original waveform, and comparing them with the pre-set thresholds to determine whether the simplified waveform is usable. Step 5.1: Calculate the root mean square error between the simplified waveform and the original waveform. Step 5.2: Calculate the correlation coefficient between the simplified waveform and the original waveform to evaluate the correlation between the simplified waveform and the original waveform. Step 5.3: By comparing with the pre-set thresholds, waveforms that meet the requirements for both similarity metrics are retained. Otherwise, return to Step 4, gradually reduce the threshold of the vertical distance, and at the same time retain the eigenvalue of the deleted points until it passes the usability evaluation module. Step 6: Store and transmit the complete waveform signal and the simplified waveform signal, and upload them to the Health Cloud System. Restore and store the simplified waveform in the Health Cloud System. Step 6.1: Output the simplified oscillatory wave waveform signal to the serial port chip to achieve information transmission with the host computer. Step 6.2: Through the integrated 4G or 5G antenna, transmit the patient's personal information and the simplified oscillatory wave waveform signal to the Health Cloud System and store them. Step 6.3: Restore the simplified oscillatory wave waveform signal to the complete original oscillatory wave waveform signal and store it in the Health Cloud System.

[0010] The waveform simplification and transmission function can also be used for the transmission of photoplethysmography (PPG) signals.

[0011] Step 7: When there is no pressure provided by the sphygmomanometer, a separate pressure control modification module can be installed to inflate the cuff and receive the blood pressure value analyzed by the Health Cloud System. Step 7.1: The micro air pump controls the inflation and deflation of the cuff. The negative feedback control and adjustment signal generated by the chip SOC is transmitted to the micro air pump to ensure that the pressure of the cuff is as close as possible to the expected value. Step 7.2: Receive the blood pressure value analyzed by the Health Cloud System and display the blood pressure measurement value on the screen of the pressure control modification module. The beneficial effects of adopting the above technical solutions are as follows: The present invention provides a blood pressure measurement system and method based on intelligent simplification and transmission of oscillatory waves. From a hardware perspective, this system has strong adaptability and can be adapted to most models of electronic blood pressure monitors from any manufacturer; the transformation is simple, only need to cut open the cuff trachea and connect the two sections of this module, without affecting the normal use of the blood pressure monitor; it has low cost, portable volume, low power consumption and fast charging. From a software perspective, through a waveform simplification method based on the Douglas-Peucker algorithm, the data transmission volume and calculation volume are greatly reduced; the preservation of oscillatory wave data is realized by uploading to the cloud, and long-term blood pressure monitoring and comparison are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is a structural block diagram of the blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves provided by an embodiment of the present invention; Figure 2 is a design block diagram of the blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves provided by an embodiment of the present invention; Figure 3 is a physical diagram of the blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves provided by an embodiment of the present invention; In the figure, a is an oscillatory wave acquisition, simplification and transmission system without a pressure control module installed; Figure 4 is a gas circuit block diagram of the blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves provided by an embodiment of the present invention; Figure 5 is a physical diagram of the blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves after installing a pressure control modification module provided by an embodiment of the present invention; In the figure, b is an oscillatory wave acquisition, simplification and transmission system after installing a pressure control module; Figure 6 is a gas circuit block diagram of the blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves after installing a pressure control modification module provided by an embodiment of the present invention; Figure 7 is an implementation block diagram of the data preprocessing module provided by an embodiment of the present invention; Figure 8 is an implementation block diagram of the signal quality assessment module provided by an embodiment of the present invention; Figure 9 is an implementation block diagram of the waveform simplification module provided by an embodiment of the present invention; Figure 10 is an implementation block diagram of the usability assessment module provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] The specific embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0014] On the one hand, a blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves, as Figure 1 shown, includes a pressure sensing module, a data preprocessing module, a signal quality evaluation module, a waveform simplification module, an availability evaluation module, and an information processing module; the data preprocessing module, the signal quality evaluation module, the waveform simplification module, the availability evaluation module, and the information processing module are integrated on a chip SOC; The pressure sensing module is arranged on the cuff trachea. By sensing the pressure change in the cuff trachea, it receives the cuff pressure, converts the pressure signal into an electrical signal, and collects the cuff pressure and the oscillatory wave signal; The data preprocessing module is used to filter the oscillatory wave signal, process the waveform using lifting wavelet transform, and pre-extract key points of the waveform; The implementation process of the data preprocessing module in this embodiment is as Figure 7 shown. First, a low-pass filter is used to remove high-frequency noise in the original oscillatory wave signal; then high-pass filtering is performed to remove the influence of baseline drift on the oscillatory wave. Baseline drift may be a signal offset caused by various environmental and physiological factors. Through high-pass filtering, its influence can be effectively reduced, making the shape of the oscillatory wave more accurate and reliable. A band-pass filter is constructed based on the lifting wavelet transform to process each period of the oscillatory wave to eliminate artifacts caused by various factors. Finally, key point pre-extraction is performed, and peak / valley detection is performed on each single-period waveform. First, the peak and valley are identified by the sliding window method to ensure that the Douglas-Peucker algorithm preferentially retains these key points.

[0015] The signal quality evaluation module receives the preprocessed oscillatory wave signal, screens out signals with poor quality evaluation and removes them; The waveform simplification module simplifies each preprocessed complete single-period waveform one by one based on the Douglas-Peucker algorithm; The availability evaluation module judges whether the simplified waveform is available by calculating the root mean square error and correlation coefficient between the simplified waveform and the original waveform and comparing them with a preset threshold; The information processing module is responsible for outputting and storing the simplified oscillatory wave waveform signal, restoring the original oscillatory wave waveform, collecting personal identity information, and positioning function, and uploading them to the health cloud system; The information processing module integrates the output, storage, upload to the health cloud system of the oscillatory wave waveform signal, restoration of the simplified waveform, identification of the tester's identity, and positioning function, realizing simple and easy-to-implement and highly adaptable online blood pressure monitoring.

[0016] The described blood pressure measurement system based on intelligent simplification and transmission of oscillating waves further includes a pressure control modification module; the pressure control modification module is separately installed on the blood pressure measurement system, providing a micro air pump connected to the chip SOC, which is used to inflate the cuff when there is no pressure provided by the sphygmomanometer, and receives the blood pressure value analyzed by the health cloud system. This modification module can be freely disassembled and connected to the multi-functional blood pressure monitoring module to replace the original sphygmomanometer to provide stable pressurization and deflation.

[0017] The implementation process in this embodiment is as Figure 2 shown; the pressure sensing module continuously collects the cuff pressure data on the user's arm in real time through the cuff pressure sensor, and the cuff pressure AC isolation circuit and filtering circuit convert the AC signal of the cuff pressure into a DC signal to obtain the real-time cuff pressure; the cuff pressure DC isolation circuit and filtering circuit are responsible for isolating the DC signal of the cuff pressure to obtain the AC signal of the cuff pressure and collect the real-time value of the cuff pressure oscillating wave; a barometric pressure sensor is set on the trachea of the blood pressure cuff, and a voltage comparator is added to the barometric pressure sensor to monitor the pressure change in the cuff. The serial port chip converts the data transmitted by the chip SOC and the serial port data recognized by the host computer to realize data transmission; through the integrated 4G / 5G antenna, GPS / GNSS antenna and identity recognition module, the output simplified oscillating wave waveform signal, cuff pressure signal, real-time position information and tester's personal information are transmitted to the health cloud system to establish a personal blood pressure monitoring database and realize online blood pressure monitoring on a personal basis. A storage read / write module is added to store the collected original waveform signal in real time; the lithium battery management chip and voltage conversion chip are responsible for managing the charging process of the lithium battery to prevent problems such as overcharging and overheating; The health cloud system is an information storage and interaction system with powerful data processing capabilities. It is used to store the user's personal information, original oscillating wave data and simplified oscillating wave data; it is also used to accurately restore the simplified waveform, analyze blood pressure data, judge blood pressure conditions, provide health suggestions and warnings; it also supports external interaction, feedback the analyzed blood pressure value to the pressure control modification module to realize an intelligent blood pressure monitoring closed loop and provide strong support for remote medical treatment and health management.

[0018] The whole system is located in the middle of the cuff trachea, collecting the pressure change information in the cuff trachea. The transformation is simple and easy to implement, and does not affect the normal use of the sphygmomanometer, but greatly enriches the functions of the sphygmomanometer. Its physical diagram and gas circuit block diagram are as Figure 3 、 Figure 4 shown. a is an oscillating wave acquisition, simplification and transmission system without a pressure control module installed, which can be freely disassembled and installed on the cuff trachea of any model of electronic sphygmomanometer; The pressure control modification module can be used for blood pressure measurement when there is no sphygmomanometer but only a cuff. It realizes data exchange with the multi-function blood pressure monitoring module through the built-in Bluetooth connection. The cuff pressure sensor of the original module continuously collects the cuff pressure data on the user's arm in real time, and obtains the DC and AC signals of the cuff pressure. The cuff pressure AC isolation and filtering circuit converts the AC signal of the cuff pressure into a DC signal, which is provided to the SOC for real-time negative feedback control and regulation. The negative feedback control and regulation signals generated by the SOC are transmitted to the micro air pump in the modification module, and the micro air pump controls the inflation and deflation of the cuff to ensure that the pressure of the cuff is as close to the expected value as possible. The blood pressure value obtained by the analysis of the health cloud system is received, and the blood pressure measurement value is displayed on the screen of the modification module. The actual picture and gas circuit block diagram are as follows: Figure 5 , Figure 6 Figure b shows the oscillation wave acquisition, simplification and transmission system after the pressure control module is installed, which can receive the blood pressure measurement data fed back by the health cloud and display it on the display screen; The signal quality evaluation module implements the following process: Figure 8 As shown. It includes calculating the signal-to-noise ratio, detecting periodicity, checking the uniformity of data point distribution to determine whether the data is usable; When calculating the signal-to-noise ratio, the signal part and the noise part should be defined in the preprocessed oscillation waveform data. The signal part is the ideal waveform of the oscillation wave, and the noise part is the random interference or unexpected fluctuation superimposed on the signal. Define two adjacent troughs as the starting point and end point of the corresponding single-cycle waveform, and extract the single-cycle waveform signal data for subsequent calculations; calculate the signal power and noise power of the single-cycle waveform, first extract the data of the signal part and calculate the mean square value, the signal power calculation formula is as follows: ; in, represents the signal power, N represents the total number of single-cycle waveform data points, Indicates the first The signal power is obtained by summing the squares of the signal values ​​of all data points in a single cycle waveform and dividing by the total number of data points, which is used to measure the strength of the signal.

[0019] The noise power can be estimated by measuring the difference between the signal and the original data, that is, subtracting the extracted ideal signal part from the original data to obtain the pure noise part, and calculating the mean square value of the noise in the same way as the signal power. The noise power calculation formula is as follows: ; in, represents the noise power, The pure noise portion is obtained by subtracting the extracted ideal signal portion from the original data. The value of the data point.

[0020] The signal-to-noise ratio is obtained by calculating the ratio of the signal power to the noise power, and the calculation formula is as follows: ; where i.e., the signal-to-noise ratio.

[0021] Since setting a fixed threshold may not accurately evaluate the signal acquisition quality, a method of dynamic noise baseline modeling is adopted. A dynamic baseline is established by real-time monitoring of the environmental noise to avoid the limitations of the fixed threshold. First, sliding window noise estimation is performed. Before each measurement or during the blood pressure deflation interval, background noise signals without active pressurization for a short time (such as 0.5 seconds) are collected, and the root mean square (RMS) of them is calculated as the noise reference.

[0022] An adaptive SNR formula is proposed: ; where is the signal-to-noise ratio threshold for the current measurement, is the historical average SNR, is the root mean square of the short-time background noise signal collected before the current measurement or during the blood pressure deflation interval obtained by sliding window noise estimation, and α and β are weight coefficients (which can be set empirically).

[0023] Periodic detection uses the autocorrelation method. The preprocessed waveform signal is subjected to autocorrelation analysis, and the autocorrelation function at different time lags is calculated. The autocorrelation function is normalized so that the value of the autocorrelation function is between [-1, 1]. The calculation method is as follows: Rx(τ)=E[x(t)• x(t+τ)] (5); where, Rx(τ) is the autocorrelation function, x(t) is the value of the preprocessed waveform signal at time t, x(t+τ) is the value of the waveform signal at time t+τ, and τ represents the time lag.

[0024] Normalize the autocorrelation function: ; After finding the peak of the autocorrelation function, determine the period of the signal and verify its periodicity through consistency checking.

[0025] To calculate the uniformity index of the data point distribution, first obtain the preprocessed waveform data, calculate the time intervals between all adjacent data points, and calculate the mean and standard deviation of the time intervals. Use the Kolmogorov-Smirnov test to determine whether the time intervals conform to a uniform distribution. If the significance level (p) value is large, it indicates that the time intervals of the data points conform to the uniform distribution hypothesis.

[0026] In this embodiment, the implementation process of the waveform simplification module is as shown in Figure 9As shown below. First, define two adjacent wave troughs as the starting and ending points of the corresponding single-cycle waveform, and use the vector cross product method to calculate the perpendicular distance from each point in the corresponding single-cycle waveform to the straight line formed by the starting and ending points of the single-cycle waveform. Find the point with the maximum perpendicular distance. If this distance is greater than the given threshold Epsilon, it is considered that there are no key points to be retained, and the current straight line can represent the single-cycle waveform being simplified. Otherwise, the point with the maximum perpendicular distance is considered a key point to be retained. During this process, record the deleted points and classify them. The same type of deleted points can be simplified to the parameterized value P0, and different types of deleted points can be simplified to the parameterized values P1, P2, …… Pn. For the found key points, divide the waveform into two segments and recursively process these two segments. Repeat the above steps until all points are processed. By increasing or decreasing the threshold, about 20% of the characteristic points of the original data can be retained for each complete single-cycle waveform, and record the characteristic values of the deleted points to reduce the data volume of the overall waveform while retaining the main characteristics of the waveform.

[0027] The availability evaluation module, the implementation process is as Figure 10 shown. It includes calculating the root mean square error and correlation coefficient between each simplified complete single-cycle waveform and the original waveform to obtain the waveform similarity index.

[0028] Calculate the root mean square error between the simplified waveform and the original waveform, where is the value of the original waveform at the i-th point. is the value of the simplified waveform at the i-th point. N is the total number of waveform data points: ; Among them, represents the root mean square error, is the value of the original waveform at the i-th point, is the value of the simplified waveform at the i-th point, and N is the total number of waveform data points.

[0029] Calculate the correlation coefficient between the simplified waveform and the original waveform, where and are the means of the original waveform and the simplified waveform respectively: ; Among them, is the correlation coefficient and are the values of the original waveform and the simplified waveform at the i-th point respectively, and are the means of the original waveform and the simplified waveform respectively.

[0030] Root mean square analysis: A smaller root mean square error value indicates that the simplified waveform is very close to the original waveform, indicating good simplification effect.

[0031] Correlation coefficient analysis: The closer the correlation coefficient is to 1, the higher the shape similarity between the simplified waveform and the original waveform. If the correlation coefficient is close to 0 or negative, it indicates that there are significant differences between the simplified waveform and the original waveform.

[0032] Thresholds are set for the two calculation methods respectively. If the root mean square error value is less than the threshold and the correlation coefficient is greater than the threshold, the simplified waveform signal is available and should be retained. If the root mean square error value is greater than the threshold or the correlation coefficient is less than the threshold, the simplified waveform fails to retain as many original waveform features as possible, and the waveform simplification module should be returned to further reduce the threshold of the vertical distance until the simplified waveform can pass the usability assessment.

[0033] In this embodiment, the information processing module integrates functions such as output, storage, upload to the health cloud system, restoration of the simplified waveform, and identification of the tester's identity, realizing simple and easy-to-implement online blood pressure monitoring with strong adaptability. First, the simplified waveform information is output to the serial port chip for PC / host computer transmission. In this step, the system outputs the simplified oscillatory wave waveform signal to the serial port chip to achieve information transmission with the PC or host computer.

[0034] Next, data is transmitted to the health cloud system through the integrated 4G / 5G antenna. In this step, the system uses the integrated 4G or 5G antenna to transmit the patient's personal information and the simplified oscillatory wave waveform signal to the health cloud system for storage. During this process, the high-speed data transmission ability ensures the real-time update and efficient transmission of information. At the same time, the application of the cloud system enhances the flexibility and scalability of data processing, supporting remote medical treatment and long-term health management.

[0035] In the health cloud system, the simplified oscillatory wave form signal is restored to the complete original oscillatory wave form signal and stored. Since the oscillatory wave signal contains many straight line segments, and the restoration of the straight line waveform segments is of little significance, the waveform restoration mainly focuses on the curve features. By identifying the radian of the waveform segments during the simplification process, if the waveform segment is a straight line or approximately a straight line, the restoration step is skipped. If the waveform segment has a large radian or contains many feature points, the waveform restoration step continues: First, connect the identified key points to construct the main contour of the waveform, laying the foundation for subsequent restoration. Second, extract the eigenvalue Pi of the deleted points in each group of key points, and these eigenvalues reflect the position and characteristics of the deleted points in the original waveform. Next, use interpolation or extrapolation methods to map the eigenvalue of the deleted points back to the original waveform, and linear interpolation or spline interpolation can be selected to ensure smooth transition. After completing the restoration of the deleted points, connect the key points with the restored deleted points to reconstruct the original waveform and ensure that its structure is similar to that before simplification, and try to retain the signal details. This step is crucial for data integrity and accurate diagnosis. The advanced algorithms inside the system are responsible for restoring the simplified data to a detailed waveform, ensuring that doctors can rely on complete and detailed data when making diagnostic and treatment decisions. Since the oscillatory wave signal contains many straight line segments, although the amount of data of the restored oscillatory wave decreases, it does not affect its feature points or characteristic wave bands. Therefore, for the restored waveform, feature recognition or identity information recognition is not affected. If the original oscillatory wave form data collected by the cuff is needed, it can also be directly uploaded to the health cloud system through the information processing module.

[0036] The pressure control modification module can receive the blood pressure value analyzed by the health cloud system and display the blood pressure measurement value through the screen of the modification module itself; this modification module can be freely disassembled, connected to the multi-functional blood pressure monitoring module, and replace the original sphygmomanometer to provide stable pressurization and deflation.

[0037] The above steps together constitute an efficient, reliable and secure data processing process, aiming to improve the quality and convenience of blood pressure monitoring through advanced technical means, while ensuring the safety and privacy of patient data.

[0038] The above description is only the preferred embodiment of the present disclosure and the explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves, characterized in that, It includes a pressure sensing module, a data preprocessing module, a signal quality assessment module, a waveform simplification module, an availability assessment module, and an information processing module; The pressure sensing module is arranged on the cuff trachea. By sensing the pressure change in the cuff trachea, it receives the cuff pressure, converts the pressure signal into an electrical signal, and acquires the cuff pressure and the oscillatory wave signal; The data preprocessing module is used to filter the oscillatory wave signal based on low-pass filtering and high-pass filtering to remove noise, and use the lifting wavelet transform to process the waveform to eliminate artifacts; at the same time, the sliding window method is used to identify the key points of the waveform to ensure that the Douglas-Peucker algorithm can retain the important feature points; The signal quality assessment module receives the preprocessed oscillatory wave signal, and judges whether the data is available by calculating the signal-to-noise ratio, detecting periodicity, and checking the uniformity of the data point distribution, and filters out the signals with poor quality assessment and removes them; The waveform simplification module receives the oscillatory wave signal with good quality after screening, and uses the Douglas-Peucker algorithm to simplify the waveform. Each complete single-cycle waveform retains 20% of the original data and at least 2 feature points; The availability assessment module calculates the simplified waveform, calculates the root mean square error and the correlation coefficient between the simplified waveform and the original waveform, and compares with the preset threshold to judge whether the simplified waveform is available; The information processing module is responsible for outputting and storing the simplified oscillatory wave waveform signal, restoring the original oscillatory wave waveform, collecting personal identity information and positioning functions, and uploading them to the health cloud system; the health cloud system stores the user's personal information, the original oscillatory wave data and the simplified oscillatory wave data; it is also used to accurately restore the simplified waveform, analyze the blood pressure data, judge the blood pressure condition, provide health advice and warnings; it also supports interaction with the outside, and feeds back the analyzed blood pressure value to the pressure control modification module; finally, if precise analysis of the oscillatory wave is required, the original oscillatory wave waveform data collected by the cuff is needed, or directly upload the original oscillatory wave waveform data to the health cloud system through the information processing module.

2. The blood pressure measurement system based on intelligent simplification and transmission of oscillation waves according to claim 1, characterized in that, The data preprocessing module, the signal quality assessment module, the waveform simplification module, the availability assessment module, and the information processing module are integrated on the chip SOC.

3. The blood pressure measurement system based on oscillation wave intelligent simplification and transmission according to claim 2, characterized in that, The blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves also includes a pressure control modification module; the pressure control modification module is separately installed on the blood pressure measurement system, and provides a micro air pump connected to the chip SOC and a display screen, which is used to inflate the cuff when there is no pressure provided by the sphygmomanometer, and receives the blood pressure value analyzed by the health cloud system.

4. A blood pressure measurement system based on intelligent simplification and transmission of oscillation waves according to claim 2, characterized in that, The pressure sensing module continuously collects the cuff pressure data on the user's arm in real time through a cuff pressure sensor. The AC isolation circuit and filtering circuit of the cuff pressure convert the AC signal of the cuff pressure into a DC signal to obtain the real-time cuff pressure. The DC isolation circuit and filtering circuit of the cuff pressure are responsible for isolating the DC signal of the cuff pressure to obtain the AC signal of the cuff pressure and collect the real-time value of the cuff pressure oscillation wave. A barometric pressure sensor is set on the air tube of the blood pressure monitor cuff, and a voltage comparator is added to the barometric pressure sensor to monitor the pressure change in the cuff. The serial port chip converts the data transmitted by the chip SOC and the serial port data with each other, and transmits the output simplified oscillation wave signal, cuff pressure signal, real-time position information and tester's personal information to the health cloud system through the integrated 4G / 5G antenna, GPS / GNSS antenna and identity recognition module to establish a personal blood pressure monitoring database.

5. A blood pressure measurement method based on intelligent simplification and transmission of oscillatory waves, implemented by a blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves as described in claim 1, characterized in that, It includes the following steps: Step 1: Obtaining the original oscillation wave signal of the sample to be recognized; Step 1.1: The pressure sensor in the pressure sensing module can sense the pressure change in the cuff air tube and convert it into an electrical signal; Step 1.2: Obtaining the DC and AC signals of the cuff pressure; Step 1.2.1: The AC isolation and filtering circuit of the cuff pressure converts the AC signal of the cuff pressure into a DC signal and collects the real-time value of the cuff pressure; Step 1.2.2: The DC isolation and filtering circuit of the cuff pressure is responsible for isolating the DC signal of the cuff pressure to obtain the AC signal of the cuff pressure, which is used to collect the real-time value of the cuff pressure oscillation wave; Step 1.2.3: Voltage comparator, when the air pressure is greater than 20 mmHg, continuously outputs a high level, and the high level triggers to wake up the SOC, the DC isolation circuit of the cuff pressure, the AC isolation circuit of the cuff pressure, the filtering circuit, the serial port chip, the 4G / 5G antenna, the GPS / GNSS antenna and the identity recognition module; Step 2: Preprocessing the obtained oscillation wave signal, including low-pass filtering and high-pass filtering, optimizing the noise and baseline drift in the oscillation wave signal, and designing a band-pass filter based on the lifting wavelet transform to restore the first harmonic of the two sidebands of the oscillation wave; Step 2.1: Using a low-pass filter to remove the high-frequency noise in the original oscillation wave signal; Step 2.2: Using high-pass filtering to remove the influence of baseline drift on the oscillation wave; Step 2.3: Constructing a band-pass filter based on the lifting wavelet transform to process each cycle of the oscillation wave to eliminate artifacts; Step 2.4: Performing key point pre-extraction, detecting the peaks and valleys of each single-cycle waveform, and first identifying the peaks and valleys through the sliding window method; Step 3: Evaluating the quality of the preprocessed oscillation wave by calculating the signal-to-noise ratio, detecting periodicity, and checking the uniformity of the data point distribution; Step 4: Waveform simplification based on the Douglas-Peucker algorithm: First, determine the starting point and the ending point of a single-cycle waveform; then, calculate the perpendicular distance from each point in the waveform to the straight line formed by the starting point and the ending point through the method of vector cross product; find the point with the maximum perpendicular distance and compare it with the threshold. If the perpendicular distance is greater than the threshold, retain the point with the maximum perpendicular distance as a key point. Otherwise, it is considered that the current straight line can represent the single-cycle waveform being simplified; for the found key points, divide the waveform into two segments and process them recursively, repeating the calculation of the perpendicular distance until all perpendicular distances are less than the threshold; and by continuously adjusting the threshold, ensure that each complete single-cycle waveform retains 20% of the original data and at least 2 characteristic points. Step 5: Evaluate the usability of the simplified waveform signal. By calculating the root mean square error and the correlation coefficient between the simplified waveform and the original waveform, and comparing them with the pre-set threshold to determine whether the simplified waveform is usable. Step 5.1: Calculate the root mean square error between the simplified waveform and the original waveform. Step 5.2: Calculate the correlation coefficient between the simplified waveform and the original waveform to evaluate the correlation between the simplified waveform and the original waveform. Step 5.3: By comparing with the pre-set threshold, retain the waveforms that meet the requirements for both similarity metrics. Otherwise, return to Step 4, gradually decrease the threshold of the perpendicular distance, and at the same time retain the characteristic values of the deleted points until passing through the usability evaluation module. Step 6: Store and transmit the complete waveform signal and the simplified waveform signal, and upload them to the health cloud system. Restore and store the simplified waveform in the health cloud system. Step 7: When there is no blood pressure monitor providing pressure, a separate pressure control modification module can be installed to inflate the cuff and receive the blood pressure value analyzed by the health cloud system.

6. A blood pressure measurement method based on intelligent simplification and transmission of oscillation waves according to claim 5, characterized in that Step 3 includes the following steps: Step 3.1: Calculate the signal-to-noise ratio to evaluate the noise level in the waveform. When calculating the signal-to-noise ratio, first define the signal and the noise. Define two adjacent wave valleys as the starting point and the ending point of the corresponding single-cycle waveform, and extract the single-cycle waveform; calculate the signal power and the noise power of the single-cycle waveform; calculate the ratio of the signal power to the noise power to obtain the signal-to-noise ratio. Adopt the method of dynamic noise baseline modeling to obtain the signal-to-noise ratio threshold by synthesizing the baseline noise level and the current noise level. Step 3.2: Detect periodicity to evaluate whether the waveform has obvious periodicity. The detection of periodicity uses the autocorrelation method. Perform autocorrelation analysis on the preprocessed waveform signal, calculate the autocorrelation function at different time lags and standardize it, determine the period of the signal after finding the peak of the autocorrelation function, and verify its periodicity through consistency checks. Step 3.3: Check the uniformity of the data point distribution to evaluate whether the distribution of the waveform data points is uniform. To check the uniformity of the data point distribution, it is necessary to calculate the standard deviation of the time intervals between data points and the mean of the time intervals, and judge whether the time intervals conform to a uniform distribution through the Kolmogorov-Smirnov test.

7. A blood pressure measurement method based on intelligent simplification and transmission of oscillation waves according to claim 5, characterized in that, Step 4 includes the following steps: Step 4.1: Select a complete single - cycle waveform for waveform simplification, and determine two adjacent wave troughs as the starting point and the ending point of the complete single - cycle waveform; Step 4.2: For each point in the waveform, use the vector cross - product to calculate its perpendicular distance to the straight line formed by the starting point and the ending point; Step 4.3: Find the point with the maximum perpendicular distance. If this distance is less than the given threshold, it is considered that there are no key points to be retained, and the current straight line can represent the single - cycle waveform being simplified. Otherwise, the point with the maximum perpendicular distance is considered a key point to be retained; Step 4.4: For the found key points, divide the waveform into two segments, recursively process these two segments respectively, and repeat Step 4.3 until all key points are processed; Step 4.5: By adjusting the threshold, ensure that each complete single - cycle waveform retains 20% of the characteristic points of the original data; Step 4.6: Combine all the retained key points to form a simplified waveform.

8. A blood pressure measurement method based on intelligent simplification and transmission of oscillation waves according to claim 5, characterized in that, Step 6 includes the following steps: Step 6.1: Output the simplified oscillatory - wave waveform signal to the serial - port chip to achieve information transmission with the host computer; Step 6.2: Through the integrated 4G or 5G antenna, transmit the patient's personal information and the simplified oscillatory - wave waveform signal to the health cloud system and store them; Step 6.3: Restore the simplified oscillatory - wave waveform signal to the complete original oscillatory - wave waveform signal and store it in the health cloud system; The waveform simplification and transmission function is also used for the transmission of photoplethysmography (PPG) signals.

9. A blood pressure measurement method based on intelligent simplification and transmission of oscillation waves according to claim 5, characterized in that, Step 7 includes the following steps: Step 7.1: The micro - air pump controls the inflation and deflation of the cuff. The negative - feedback control and adjustment signal generated by the chip SOC is transmitted to the micro - air pump to ensure that the pressure of the cuff reaches the expected value; Step 7.2: Receive the blood - pressure value analyzed by the health cloud system and display the blood - pressure measurement value on the screen of the pressure - control modification module.

Images