Blood Pressure Measurement System and Method Based on Intelligent Simplification and Transmission of Oscillation Waves

Abstract

The present invention provides a blood pressure measurement system and method based on intelligent simplification and transmission of oscillation waves, relating to the field of wearable medical health monitoring technology. The system comprises a pressure sensing module, a data preprocessing module, a signal quality assessment module, a waveform simplification module, a usability assessment module, and an information processing module. A cuff pressure sensor is used to continuously collect cuff pressure data from the user's arm in real time, obtaining real-time cuff pressure and the real-time value of the cuff pressure oscillation wave. An air pressure sensor is provided on the trachea of the blood pressure cuff, and a voltage comparator is added to the air pressure sensor to monitor pressure changes within the cuff. A serial port chip converts data transmitted by the chip's SOC into serial port data. The system then transmits the output simplified oscillation waveform signal, cuff pressure signal, real-time location information, and the tester's personal information to a health cloud system via an integrated 4G / 5G antenna, GPS / GNSS antenna, and identity recognition module, thereby establishing a personal blood pressure monitoring database.

Description

Technical Field

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

[0002] With the development of digital healthcare, smart blood pressure monitors have become widely available, conveniently collecting and recording blood pressure data. However, this field currently faces significant technical challenges. Internet upload functionality is immature, data integration with health cloud services is difficult, and data analysis and diagnosis rely on specialized physicians. This burdens medical resources and reduces user experience. Furthermore, access to health cloud services requires the purchase of additional network-connected equipment, adding significant financial costs. While the traditional oscillatory wave method is widely used, its raw oscillatory waveform signal data is massive and computationally demanding. This places high demands on device storage capacity, increasing storage costs and complexity, while computationally consuming significant resources and time, resulting in low efficiency. Uploading to the cloud is slow and prone to interruptions, hindering real-time data sharing and the development of telemedicine.

[0003] Chinese patent CN112185499A collects user blood pressure data at a preset location, identifies personal information, and creates an electronic file. It also allows for remote monitoring of dynamic blood pressure updates, facilitating data management for patients with chronic hypertension. However, it cannot store oscillation waveform data, hindering the use of optimization algorithms to obtain accurate blood pressure values, and simply transmitting waveform data is difficult. Chinese patent CN118919075A utilizes multiple components to analyze the correlation between blood pressure fluctuations and noise-sensitive characteristics, enabling time-based blood pressure analysis and early warning prompts, improving monitoring effectiveness. However, the volume of raw oscillation waveform data processed is enormous, placing excessive demands on device performance and making it unsuitable for personal use. Chinese patent CN118749931A acquires patient blood pressure data in real time, preprocesses and calibrates it, and constructs a model to predict when blood pressure abnormalities occur, optimizing medical processes. However, the sheer volume of data generated by data processing places high demands on storage devices, making transmission prone to slowness and interruptions, and limiting the device's lightweight and portability.

[0004] In the existing technology, the storage and transmission of blood pressure data mostly uses the SBP and DBP values obtained by measurement directly, rather than transmitting oscillation wave signals. The results measured by different measurement equipment and methods vary greatly, which is not conducive to blood pressure management and analysis. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a blood pressure measurement system and method based on intelligent simplification and transmission of oscillation waves, which realizes simple, feasible and adaptable data simplification and blood pressure network monitoring.

[0006] In one aspect, a blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves includes a pressure sensing module, a data preprocessing module, a signal quality assessment module, a waveform simplification module, a usability assessment module, and an information processing module;

[0007] The data preprocessing module, signal quality assessment module, waveform simplification module, availability assessment module, and information processing module are integrated on a chip SOC;

[0008] The pressure sensing module is arranged on the cuff trachea, and receives the cuff pressure by sensing the pressure change in the cuff trachea, converting the pressure signal into an electrical signal, and collecting the cuff pressure and oscillation wave signal;

[0009] The data preprocessing module is used to filter the oscillation wave signal, process the waveform using lifting wavelet transform, and pre-extract key points of the waveform;

[0010] The signal quality assessment module receives the pre-processed oscillation wave signal, filters out signals with poor quality assessment and removes them;

[0011] The waveform simplification module simplifies the pre-processed complete single-cycle waveforms one by one based on the Douglas-Peucker algorithm;

[0012] The usability evaluation module determines whether the simplified waveform is usable by calculating the root mean square error and correlation coefficient between the simplified waveform and the original waveform and comparing the calculated values with a preset threshold.

[0013] The information processing module is responsible for outputting and storing simplified oscillation waveform signals, restoring the original oscillation waveform, collecting personal identity information and positioning functions, and uploading them to the health cloud system;

[0014] The blood pressure measurement system based on intelligent simplification and transmission of oscillation waves also includes a pressure control modification module; the pressure control modification module is installed separately on the blood pressure measurement system, providing 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 a sphygmomanometer, and receive the blood pressure value obtained by analysis by the health cloud system.

[0015] The pressure sensing module continuously collects the cuff pressure data on the user's arm in real time through the cuff pressure sensor. The cuff pressure AC isolation circuit and the filter 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 the filter circuit are responsible for isolating the DC signal of the cuff pressure, obtaining the AC signal of the cuff pressure, and collecting the real-time value of the cuff pressure oscillation wave; an air pressure sensor is set on the trachea of the blood pressure monitor cuff, and a voltage comparator is added to the air 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 into each other, and transmits the output simplified oscillation wave waveform signal, cuff pressure signal, real-time location 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;

[0016] The health cloud system stores user personal information, original oscillation wave data and simplified oscillation wave data; it is also used to accurately restore simplified waveforms, analyze blood pressure data, determine blood pressure conditions, and provide health advice and warnings; it also supports external interaction and feeds back the analyzed blood pressure values to the pressure control modification module.

[0017] On the other hand, a blood pressure measurement method based on intelligent simplification and transmission of oscillatory waves is implemented based on the aforementioned blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves, and includes the following steps:

[0018] Step 1: Acquisition of the original oscillation wave signal of the sample to be identified;

[0019] Step 1.1: The pressure sensor in the pressure sensing module can sense the pressure changes in the cuff trachea and convert them into electrical signals;

[0020] Step 1.2: Obtain the DC and AC signals of the cuff pressure;

[0021] Step 1.2.1: The cuff pressure AC isolation and filtering circuit converts the cuff pressure AC signal into a DC signal and collects the real-time value of the cuff pressure;

[0022] Step 1.2.2: The cuff pressure DC isolation and filtering circuit 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 cuff pressure oscillation wave;

[0023] Step 1.2.3: The voltage comparator continuously outputs a high level when the air pressure is greater than 20 mmHg. The high level triggers the SOC and wakes up the cuff pressure DC isolation circuit, cuff pressure AC isolation circuit, filter circuit, serial port chip, 4G / 5G antenna, GPS / GNSS antenna, and identification module.

[0024] Step 2: Preprocess the acquired oscillation wave signal, including low-pass filtering and high-pass filtering, to optimize the noise and baseline drift in the oscillation wave signal. A bandpass filter based on lifting wavelet transform is designed to recover the first harmonic of the two sidebands of the oscillation wave.

[0025] Step 2.1: Use a low-pass filter to remove high-frequency noise from the original oscillation wave signal;

[0026] Step 2.2: Use high-pass filtering to remove the influence of baseline drift on the oscillation wave;

[0027] Step 2.3: Construct a bandpass filter based on the lifting wavelet transform to process each periodic oscillation wave and eliminate artifacts;

[0028] Step 2.4: Pre-extract key points and detect peaks and troughs of each single-cycle waveform. First, use the sliding window method to identify peaks and troughs.

[0029] Step 3: Evaluate the quality of the pre-processed oscillation wave by calculating the signal-to-noise ratio, detecting periodicity, and checking the uniformity of the data point distribution;

[0030] Step 3.1: Calculate the signal-to-noise ratio to assess the noise level in the waveform.

[0031] When calculating the signal-to-noise ratio, the signal and noise should first be defined, and two adjacent troughs should be defined as the starting point and end point of the corresponding single-cycle waveform, and the single-cycle waveform should be extracted; the signal power and noise power of the single-cycle waveform should be calculated; the signal-to-noise ratio is obtained by calculating the ratio of the signal power to the noise power, and the signal-to-noise ratio threshold is obtained by using a dynamic noise baseline modeling method, comprehensively considering the baseline noise level and the current noise level;

[0032] Step 3.2: Detect periodicity and evaluate whether the waveform has obvious periodicity;

[0033] The detection of periodicity adopts the autocorrelation method, performs autocorrelation analysis on the pre-processed waveform signal, calculates the autocorrelation function under different time lags and standardizes it, finds the peak of the autocorrelation function and determines the period of the signal, and verifies its periodicity through consistency check;

[0034] Step 3.3: Check the uniformity of data point distribution and evaluate whether the waveform data points are evenly distributed;

[0035] The uniformity of the data point distribution is checked by calculating the standard deviation and the mean of the time intervals between the data points, and determining whether the time intervals conform to a uniform distribution by using the Kolmogorov-Smirnov test;

[0036] Step 4: Simplify the waveform based on the Douglas-Peucker algorithm: First, determine the start and end points of the single-cycle waveform; then calculate the vertical distance from each point in the waveform to the straight line formed by the start and end points using the vector cross product method; find the point with the largest vertical distance and compare it with the threshold. If the vertical distance is greater than the threshold, retain the point with the largest vertical distance as the key point; otherwise, consider the current line to represent the single-cycle waveform being simplified; for the key points found, divide the waveform into two segments, recursively process them separately, and repeatedly calculate the vertical distance until all vertical distances are less than the threshold; and by continuously adjusting the threshold, each complete single-cycle waveform retains 20% of the original data and at least two feature points;

[0037] Step 4.1: Select a complete single-cycle waveform for waveform simplification and determine two adjacent troughs as the start and end points of the complete single-cycle waveform;

[0038] Step 4.2: For each point in the waveform, use the vector cross product to calculate its perpendicular distance to the line formed by the start and end points.

[0039] Step 4.3: Find the point with the largest vertical distance. If this distance is less than a given threshold, it is considered that there is no key point that needs to be retained and the current line can represent the single-cycle waveform being simplified. Otherwise, the point with the largest vertical distance is considered to be a key point that needs to be retained.

[0040] Step 4.4: For the key points found, divide the waveform into two segments, recursively process the two segments separately, and repeat step 4.3 until all key points are processed;

[0041] Step 4.5: Adjust the threshold to ensure that each complete single-cycle waveform retains 20% of the feature points of the original data;

[0042] Step 4.6: Merge all the retained key points to form a simplified waveform;

[0043] 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 a pre-set threshold to determine whether the simplified waveform is usable;

[0044] Step 5.1: Calculate the root mean square error between the simplified waveform and the original waveform;

[0045] 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;

[0046] Step 5.3: By comparing with the pre-set threshold, if both similarity indicators meet the requirements, the waveform is retained. Otherwise, return to step 4 and gradually reduce the vertical distance threshold while retaining the feature value of the deleted point until it passes the usability evaluation module;

[0047] Step 6: The complete waveform signal and the simplified waveform signal are stored and sent, and uploaded to the health cloud system, where the simplified waveform is restored and stored;

[0048] Step 6.1: Output the simplified oscillation waveform signal to the serial port chip to achieve information transmission with the host computer;

[0049] Step 6.2: Transmit the patient's personal information and the simplified oscillation waveform signal to the health cloud system through the integrated 4G or 5G antenna and store them;

[0050] Step 6.3: In the health cloud system, the simplified oscillation waveform signal is restored to the complete original oscillation waveform signal and stored.

[0051] The waveform simplification and transmission function can also be used for the transmission of photoplethysmography (PPG) signals.

[0052] Step 7: If there is no blood pressure monitor to provide 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;

[0053] Step 7.1: The micro air pump controls the inflation and deflation of the cuff. The negative feedback control and adjustment signals generated by the chip SOC are transmitted to the micro air pump to ensure that the pressure of the cuff is as close as possible to the desired value.

[0054] 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;

[0055] The beneficial effects of adopting the above technical solution are:

[0056] This invention provides a blood pressure measurement system and method based on intelligent oscillatory wave simplification and transmission. This system offers strong hardware adaptability, adapting to most models of electronic blood pressure monitors from any manufacturer. Modification is simple, requiring only the cuff's trachea to be cut and connected to the two ends of the module, without affecting the normal use of the monitor. The system is low-cost, portable, low-power, and fast-charging. Software-wise, a waveform simplification method based on the Douglas-Peucker algorithm significantly reduces data transmission and computational complexity. Uploading oscillatory wave data to the cloud enables storage, enabling long-term blood pressure monitoring and comparison. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A structural block diagram of a blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves provided in an embodiment of the present invention;

[0058] Figure 2 A block diagram of the design of a blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves provided in an embodiment of the present invention;

[0059] Figure 3 A physical diagram of a blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves provided in an embodiment of the present invention;

[0060] In the figure, a-the oscillation wave acquisition, simplification and transmission system without the pressure control module;

[0061] Figure 4 A gas circuit block diagram of a blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves provided in an embodiment of the present invention;

[0062] Figure 5 A physical diagram of a blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves after adding a pressure control modification module according to an embodiment of the present invention;

[0063] In the figure, b is the oscillation wave acquisition, simplification and transmission system after the pressure control module is installed;

[0064] Figure 6 A block diagram of the gas circuit of a blood pressure measurement system based on intelligent simplification and transmission of oscillating waves after adding a pressure control modification module according to an embodiment of the present invention;

[0065] Figure 7 This is a block diagram of an implementation of a data preprocessing module provided in an embodiment of the present invention;

[0066] Figure 8 A block diagram of an implementation of a signal quality assessment module provided in an embodiment of the present invention;

[0067] Figure 9 A block diagram of an implementation of a waveform simplification module provided in an embodiment of the present invention;

[0068] Figure 10 This is an implementation block diagram of the usability evaluation module provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0070] On the one hand, a blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves, e.g. Figure 1As shown, it includes a pressure sensing module, a data preprocessing module, a signal quality assessment module, a waveform simplification module, a usability assessment module, and an information processing module; the data preprocessing module, the signal quality assessment module, the waveform simplification module, the usability assessment module, and the information processing module are integrated on a chip SOC;

[0071] The pressure sensing module is arranged on the cuff trachea, and receives the cuff pressure by sensing the pressure change in the cuff trachea, converting the pressure signal into an electrical signal, and collecting the cuff pressure and oscillation wave signal;

[0072] The data preprocessing module is used to filter the oscillation wave signal, process the waveform using lifting wavelet transform, and pre-extract key points of the waveform;

[0073] In this embodiment, the data preprocessing module implements the process as follows: Figure 7 As shown in the figure, a low-pass filter is first used to remove high-frequency noise from the original oscillation wave signal. High-pass filtering is then performed to remove the effects of baseline drift on the oscillation wave. Baseline drift can be caused by various environmental and physiological factors that offset the signal. High-pass filtering can effectively reduce its impact, making the oscillation wave morphology more accurate and reliable. A bandpass filter is constructed based on the lifting wavelet transform to process each cycle of the oscillation wave to eliminate artifacts caused by various factors. Finally, key point pre-extraction is performed, and peak / trough detection is performed on each single-cycle waveform. Peaks and troughs are first identified using the sliding window method to ensure that the Douglas-Peucker algorithm prioritizes these key points.

[0074] The signal quality assessment module receives the pre-processed oscillation wave signal, filters out signals with poor quality assessment and removes them;

[0075] The waveform simplification module simplifies the pre-processed complete single-cycle waveforms one by one based on the Douglas-Peucker algorithm;

[0076] The usability evaluation module determines whether the simplified waveform is usable by calculating the root mean square error and correlation coefficient between the simplified waveform and the original waveform and comparing the calculated values with a preset threshold.

[0077] The information processing module is responsible for outputting and storing simplified oscillation waveform signals, restoring the original oscillation waveform, collecting personal identity information and positioning functions, and uploading them to the health cloud system;

[0078] The information processing module integrates the output, storage, uploading of oscillation waveform signals to the health cloud system, simplified waveform restoration, tester identity identification and positioning functions, realizing simple, easy and adaptable online blood pressure monitoring.

[0079] The blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves also includes a pressure control modification module. This module, installed separately from the blood pressure measurement system, provides a micro-pump connected to the SOC chip. This module is used to inflate the cuff when no pressure is provided by a sphygmomanometer and receives blood pressure readings analyzed by the health cloud system. This modification module is removable and connects to the multifunctional blood pressure monitoring module, replacing the existing sphygmomanometer to provide stable pressurization and deflation.

[0080] In this embodiment, the implementation process is as follows Figure 2 As 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 filter 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 filter circuit are responsible for isolating the DC signal of the cuff pressure, obtaining the AC signal of the cuff pressure, and collecting the real-time value of the cuff pressure oscillation wave; an air pressure sensor is set on the trachea of the blood pressure cuff, and a voltage comparator is added to the air 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 oscillation wave waveform signal, cuff pressure signal, real-time location 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 an individual basis. A storage read-write module is added to store the collected original waveform signal in real time; the lithium battery management chip and the voltage conversion chip are responsible for managing the charging process of the lithium battery to prevent problems such as overcharging and heating;

[0081] The health cloud system is an information storage and interaction system with powerful data processing capabilities. It is used to store user personal information, original oscillation wave data, and simplified oscillation wave data. It is also used to accurately restore simplified waveforms, analyze blood pressure data, determine blood pressure status, and provide health advice and warnings. It also supports external interaction and feeds analyzed blood pressure values back to the pressure control modification module, realizing an intelligent blood pressure monitoring closed loop and providing strong support for remote medical care and health management.

[0082] The entire system is located in the middle of the cuff trachea, collecting information on pressure changes in the cuff trachea. The modification is simple and easy, and does not affect the normal use of the blood pressure monitor, but greatly enriches the function of the blood pressure monitor. The physical picture and gas circuit diagram are as follows Figure 3 、 Figure 4 As shown in Figure 1, a shows the oscillation wave acquisition, simplification, and transmission system without the pressure control module installed. It can be freely disassembled and installed on the cuff tube of any type of electronic blood pressure monitor.

[0083] The pressure control modification module can be used for blood pressure measurement when there is only a cuff but no sphygmomanometer. It realizes data intercommunication 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 adjustment. The negative feedback control and adjustment signal generated by the SOC is 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 receiving the analysis of the health cloud system is displayed on the screen of the modification module. The physical 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 blood pressure measurement data fed back by the health cloud and display it on the screen;

[0084] The signal quality evaluation module implements the following process: Figure 8 As shown. This includes calculating the signal-to-noise ratio, detecting periodicity, and checking the uniformity of data point distribution to determine whether the data is usable;

[0085] 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 and ending points 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 signal part data 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 the sum by the total number of data points, which is used to measure the strength of the signal.

[0086] Noise power can be estimated by measuring the difference between the signal and the original data. That is, subtracting the extracted ideal signal portion from the original data to obtain the pure noise portion. The mean square value of the noise is calculated 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.

[0087] The signal-to-noise ratio is calculated by calculating the ratio of signal power to noise power. The calculation formula is as follows: ;in That is the signal-to-noise ratio.

[0088] Because setting a fixed threshold may not accurately assess signal acquisition quality, a dynamic noise baseline modeling approach is employed. This approach establishes a dynamic baseline by monitoring ambient noise in real time, thus overcoming the limitations of a fixed threshold. First, a sliding window noise estimation is performed. Before each measurement or between blood pressure deflations, a short period (e.g., 0.5 seconds) of background noise without active pressurization is collected. The root mean square (RMS) of this signal is calculated as the noise baseline.

[0089] Propose an adaptive SNR formula: ;in is the signal-to-noise ratio threshold of the current measurement, is the historical average SNR, It 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).

[0090] Periodicity 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 then normalized so that its value is between -1 and 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.

[0091] Normalize the autocorrelation function: ; After finding the peak of the autocorrelation function, determine the period of the signal and verify its periodicity through consistency check.

[0092] To calculate the uniformity index of data point distribution, first obtain preprocessed waveform data, calculate the time intervals between all adjacent data points, and then calculate the mean and standard deviation of these time intervals. The Kolmogorov-Smirnov test is then used to determine whether the time intervals conform to a uniform distribution. A high significance level (p) indicates that the time intervals between the data points conform to the uniform distribution assumption.

[0093] The waveform simplification module implementation process in this embodiment is as follows: Figure 9As shown. First, define two adjacent troughs as the starting point and end point of the corresponding single-cycle waveform, and use the vector cross product method to calculate the vertical distance from each point in the corresponding single-cycle waveform to the straight line formed by the starting point and end point of the single-cycle waveform. Find the point with the largest vertical distance. If this distance is greater than the given threshold Epsilon, it is considered that there are no key points that need to be retained, and the current straight line can represent the single-cycle waveform being simplified. Otherwise, the point with the largest vertical distance is considered to be a key point that needs to be retained. In this process, the deleted points are recorded and classified. The same type of deleted points can be simplified to parameterized values P0, and the different types of deleted points can be simplified to parameterized values P1, P2, ... Pn. For the key points found, the waveform is divided into two segments, and the two segments are recursively processed separately. Repeat the above steps until all points are processed. By increasing or decreasing the threshold, each complete single-cycle waveform can retain about 20% of the feature points of the original data, and the feature values of the deleted points are recorded, so as to simplify the data volume of the overall waveform while retaining the main features of the waveform.

[0094] Usability evaluation module, implementation process such as Figure 10 This 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.

[0095] 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 point i. N is the total number of waveform data points: ;in, represents the root mean square error, is the value of the original waveform at point i, is the value of the simplified waveform at point i, and N is the total number of waveform data points.

[0096] 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: ;in, is the correlation coefficient and are the values of the original waveform and the simplified waveform at point i, respectively. and are the means of the original waveform and the simplified waveform respectively.

[0097] RMS analysis: A smaller RMS error value indicates that the simplified waveform is very close to the original waveform, indicating a good simplification effect.

[0098] Correlation coefficient analysis: The closer the correlation coefficient is to 1, the more similar the simplified waveform is to the original waveform. If the correlation coefficient is close to 0 or a negative value, it means that the simplified waveform is significantly different from the original waveform.

[0099] Set thresholds 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 fully retain as many original waveform features as possible. The waveform simplification module should be returned to further lower the vertical distance threshold until the simplified waveform can pass the usability assessment.

[0100] The information processing module in this embodiment integrates functions such as outputting, storing, and uploading oscillation waveform signals to the health cloud system, restoring simplified waveforms, and identifying testers, enabling simple, adaptable online blood pressure monitoring. First, the simplified waveform information is output to the serial port chip for transmission to a PC or host computer. In this step, the system outputs the simplified oscillation waveform signal to the serial port chip, enabling information transmission to a PC or host computer.

[0101] Next, the data is transmitted to the health cloud system via an integrated 4G / 5G antenna. During this step, the system uses the integrated 4G or 5G antenna to transmit the patient's personal information and a simplified oscillation waveform signal to the health cloud system for storage. This high-speed data transmission ensures real-time updates and efficient transmission of information. Furthermore, the use of the cloud system enhances the flexibility and scalability of data processing, supporting telemedicine and long-term health management.

[0102] In the health cloud system, the simplified oscillatory waveform signal is restored to its original form and stored. Because oscillatory waveform signals contain many straight line segments, and restoration of straight line waveform segments is not very meaningful, waveform restoration focuses on curved features. The radian of the waveform segments during the simplification process is identified. If the waveform segment is straight or approximately straight, the restoration step is skipped. If the waveform segment has a large radian or contains many characteristic points, the waveform restoration step continues. First, the identified key points are connected to construct the main contours of the waveform, laying the foundation for subsequent restoration. Second, the eigenvalues Pi of the deleted points in each group of key points are extracted. These eigenvalues reflect the position and characteristics of the deleted points in the original waveform. Next, the eigenvalues of the deleted points are mapped back to the original waveform using interpolation or extrapolation. Linear or spline interpolation can be used to ensure a smooth transition. After the deleted points are restored, the key points are connected to the restored deleted points to reconstruct the original waveform, ensuring that its structure is similar to that before simplification and preserving as much signal detail as possible. This step is critical for data integrity and accurate diagnosis. Advanced algorithms within the system are responsible for restoring simplified data into detailed waveforms, ensuring that doctors can rely on complete and detailed data when making diagnostic and treatment decisions. Because the oscillation wave signal contains many straight line segments, the amount of restored oscillation wave data is reduced, but its characteristic points or characteristic bands are not affected. Therefore, feature recognition and identity information recognition are not affected for the restored waveform. If the original oscillation wave waveform data collected by the cuff is required, it can be directly uploaded to the Health Cloud System through the information processing module.

[0103] The pressure control modification module can receive the blood pressure values analyzed by the health cloud system and display the blood pressure measurement values through the screen of the modification module. The modification module can be freely disassembled and connected to the multi-functional blood pressure monitoring module to replace the original blood pressure monitor to provide stable pressurization and deflation.

[0104] The above steps together constitute an efficient, reliable and secure data processing process, which aims to improve the quality and convenience of blood pressure monitoring through advanced technical means, while ensuring the security and privacy of patient data.

[0105] The above description is merely an illustration of the preferred embodiments of the present disclosure and the technical principles employed. Those skilled in the art should understand that the scope of the invention encompassed by the embodiments of the present disclosure is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned inventive concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with 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 pressure sensing module, data preprocessing module, signal quality assessment module, waveform simplification module, usability assessment module and information processing module; The pressure sensing module is arranged on the cuff trachea, and receives the cuff pressure by sensing the pressure change in the cuff trachea, converting the pressure signal into an electrical signal, and collecting the cuff pressure and oscillation wave signal; The data preprocessing module is used to filter the oscillation wave signal based on low-pass filtering and high-pass filtering to remove noise, and process the waveform using lifting wavelet transform to eliminate artifacts; at the same time, a sliding window method is used to identify key points of the waveform to ensure that the Douglas-Peucker algorithm can retain important feature points; The signal quality assessment module receives the pre-processed oscillation wave signal, determines whether the data is usable by calculating the signal-to-noise ratio, detecting periodicity, and checking the uniformity of data point distribution, and filters out signals with poor quality assessment and removes them; The waveform simplification module receives the filtered high-quality oscillation wave signal 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 characteristic points. The usability evaluation module calculates the simplified waveform, calculates the root mean square error and correlation coefficient between the simplified waveform and the original waveform, and compares the calculated value with a preset threshold value to determine whether the simplified waveform is usable. The information processing module is responsible for outputting and storing simplified oscillation wave waveform signals, restoring the original oscillation wave waveform, collecting personal identity information and positioning functions, and uploading them to the health cloud system; the health cloud system stores user 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, and provide health advice and warnings; it also supports external interaction and feeds back the analyzed blood pressure values to the pressure control modification module; finally, if accurate analysis of the oscillation wave is required, the original oscillation wave waveform data collected by the cuff is required, or the original oscillation wave waveform data is directly uploaded to the health cloud system through the information processing module.

2. A blood pressure measurement system based on oscillatory wave intelligent simplification and transmission according to claim 1, characterized in that: The data preprocessing module, signal quality evaluation module, waveform simplification module, availability evaluation module and information processing module are integrated on a chip SOC.

3. The blood pressure measurement system based on oscillatory wave intelligent simplification and transmission according to claim 2, characterized in that: The blood pressure measurement system based on intelligent simplification and transmission of oscillation waves also includes a pressure control modification module; the pressure control modification module is installed separately on the blood pressure measurement system, providing 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 a sphygmomanometer, and receive the blood pressure value obtained by analysis by the health cloud system.

4. The blood pressure measurement system based on oscillatory wave intelligent simplification and transmission 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 the cuff pressure sensor. The cuff pressure AC isolation circuit and the filter 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 the filter circuit are responsible for isolating the DC signal of the cuff pressure, obtaining the AC signal of the cuff pressure, and collecting the real-time value of the cuff pressure oscillation wave; an air pressure sensor is set on the trachea of the blood pressure monitor cuff, and a voltage comparator is added to the air pressure sensor to monitor the pressure changes in the cuff. The serial port chip converts the data transmitted by the chip SOC and the serial port data into each other, and transmits the output simplified oscillation wave waveform signal, cuff pressure signal, real-time location 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 the blood pressure measurement system based on intelligent simplification and transmission of oscillatory waves according to claim 1, characterized in that: The following steps are involved: Step 1: Acquisition of the original oscillation wave signal of the sample to be identified; Step 1.1: The pressure sensor in the pressure sensing module can sense the pressure changes in the cuff trachea and convert them into electrical signals; Step 1.2: Obtain the DC and AC signals of the cuff pressure; Step 1.2.1: The cuff pressure AC isolation and filtering circuit converts the cuff pressure AC signal into a DC signal and collects the real-time value of the cuff pressure; Step 1.2.2: The cuff pressure DC isolation and filtering circuit 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 cuff pressure oscillation wave; Step 1.2.3: The voltage comparator continuously outputs a high level when the air pressure is greater than 20 mmHg. The high level triggers the SOC and wakes up the cuff pressure DC isolation circuit, cuff pressure AC isolation circuit, filter circuit, serial port chip, 4G / 5G antenna, GPS / GNSS antenna, and identification module. Step 2: Preprocess the acquired oscillation wave signal, including low-pass filtering and high-pass filtering, to optimize the noise and baseline drift in the oscillation wave signal. A bandpass filter based on lifting wavelet transform is designed to recover the first harmonic of the two sidebands of the oscillation wave. Step 2.1: Use a low-pass filter to remove high-frequency noise from the original oscillation wave signal; Step 2.2: Use high-pass filtering to remove the influence of baseline drift on the oscillation wave; Step 2.3: Construct a bandpass filter based on the lifting wavelet transform to process each periodic oscillation wave and eliminate artifacts; Step 2.4: Pre-extract key points and detect peaks and troughs of each single-cycle waveform. First, use the sliding window method to identify peaks and troughs. Step 3: Evaluate the quality of the pre-processed oscillation wave by calculating the signal-to-noise ratio, detecting periodicity, and checking the uniformity of the data point distribution; Step 4: Simplify the waveform based on the Douglas-Peucker algorithm: First, determine the start and end points of the single-cycle waveform; then calculate the vertical distance from each point in the waveform to the straight line formed by the start and end points using the vector cross product method; find the point with the largest vertical distance and compare it with the threshold. If the vertical distance is greater than the threshold, retain the point with the largest vertical distance as the key point; otherwise, consider the current line to represent the single-cycle waveform being simplified; for the key points found, divide the waveform into two segments, recursively process them separately, and repeatedly calculate the vertical distance until all vertical distances are less than the threshold; and by continuously adjusting the threshold, each complete single-cycle waveform retains 20% of the original data and at least two feature points; 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 a 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, if both similarity indicators meet the requirements, the waveform is retained. Otherwise, return to step 4 and gradually reduce the vertical distance threshold while retaining the feature value of the deleted point until it passes the usability evaluation module; Step 6: The complete waveform signal and the simplified waveform signal are stored and sent, and uploaded to the health cloud system, where the simplified waveform is restored and stored; Step 7: When there is no sphygmomanometer to provide 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. The blood pressure measurement method based on oscillation wave intelligent simplification and transmission according to claim 5, characterized in that: Step 3 includes the following steps: Step 3.1: Calculate the signal-to-noise ratio to assess the noise level in the waveform. When calculating the signal-to-noise ratio, the signal and noise should first be defined, and two adjacent troughs should be defined as the starting point and end point of the corresponding single-cycle waveform, and the single-cycle waveform should be extracted; the signal power and noise power of the single-cycle waveform should be calculated; the signal-to-noise ratio is obtained by calculating the ratio of the signal power to the noise power, and the signal-to-noise ratio threshold is obtained by using a dynamic noise baseline modeling method, comprehensively considering 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, performs autocorrelation analysis on the pre-processed waveform signal, calculates the autocorrelation function under different time lags and standardizes it, finds the peak of the autocorrelation function and determines the period of the signal, and verifies its periodicity through consistency check; Step 3.3: Check the uniformity of data point distribution and evaluate whether the waveform data points are evenly distributed; The checking of the uniformity of the data point distribution requires calculating the standard deviation and the mean of the time intervals between the data points, and determining whether the time intervals conform to a uniform distribution by using a Kolmogorov-Smirnov test.

7. The blood pressure measurement method based on oscillatory wave intelligent simplification and transmission 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 troughs as the start and end 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 line formed by the start and end points. Step 4.3: Find the point with the largest vertical distance. If this distance is less than a given threshold, it is considered that there is no key point that needs to be retained and the current line can represent the single-cycle waveform being simplified. Otherwise, the point with the largest vertical distance is considered to be a key point that needs to be retained. Step 4.4: For the key points found, divide the waveform into two segments, recursively process the two segments separately, and repeat step 4.3 until all key points are processed; Step 4.5: Adjust the threshold to ensure that each complete single-cycle waveform retains 20% of the feature points of the original data; Step 4.6: Merge all remaining key points to form a simplified waveform.

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

9. The blood pressure measurement method based on oscillatory wave intelligent simplification and transmission 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 regulation signals generated by the chip SOC are transmitted to the micro air pump to ensure that the cuff pressure reaches the desired 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.

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