Finger vascular systolic pressure measuring device and measuring method

Abstract

According to the finger blood vessel systolic pressure measuring device and method, the liquid pressure of a liquid conveying module is stably received through a finger lining, finger blood vessels are accurately pressed, a strain gauge at the far end sensitively captures blood flow recovery pulsation data, a reliable basis is provided for systolic pressure calculation, the proper temperature of liquid is kept through a temperature control module, and the systolic pressure is accurately measured. The influence of the temperature on the measurement result is reduced, the measurement accuracy and stability are improved, the device keeps consistent performance at different environment temperatures, the applicability and reliability are enhanced, the water pressure is automatically adjusted through the liquid conveying module, blood vessel compression and relaxation are accurately controlled, manual operation is simplified, and the measurement efficiency and accuracy are improved. The data processing module is used for automatically collecting and processing data, rapidly calculating the vasosystolic pressure, reducing personal errors and improving the measuring speed.

Description

Technical Field

[0001] The present invention relates to the technical field of finger vascular systolic pressure measurement, and in particular to a finger vascular systolic pressure measurement device and a measurement method. Background Art

[0002] In the field of medical diagnosis and health monitoring, blood pressure measurement has always occupied a pivotal position. Although traditional blood pressure measurement methods, such as using mercury sphygmomanometers or electronic sphygmomanometers to measure on the upper arm, have been widely used, they still have many limitations. For example, they may not be suitable for all individuals, especially those with abnormal arm blood vessels or certain diseases. In addition, traditional measurement methods may not be convenient enough in some occasions and cannot meet the needs of real-time monitoring.

[0003] In order to overcome these limitations, finger vascular systolic blood pressure measurement devices came into being. This device measures systolic blood pressure by compressing the finger blood vessels and monitoring the blood flow recovery process. Compared with traditional methods, finger vascular systolic blood pressure measurement has the advantages of being non-invasive, convenient, and easy to operate. It is particularly suitable for home health monitoring, clinical preliminary screening, and blood pressure monitoring of special patient groups.

[0004] However, although the finger vascular systolic pressure measurement device has many advantages, the existing technology still has some obvious defects. First, in terms of measurement accuracy, the existing device is often seriously disturbed by environmental noise and instrument noise, resulting in large errors in the measurement results. Secondly, in terms of measurement stability, since the finger blood vessels are thin and easily affected by external factors, the existing device often has large fluctuations in measurement values ​​during long-term measurement or continuous monitoring. In addition, the existing device is also insufficient in adapting to the finger blood vessel characteristics of different individuals, making it difficult to achieve accurate measurement of all individuals. Summary of the invention

[0005] In view of this, the present invention proposes a finger vascular systolic pressure measurement device and a measurement method, which can effectively solve the defects of the prior art such as large error, poor stability and difficulty in achieving accurate measurement of all individuals.

[0006] The technical solution of the present invention is achieved in this way:

[0007] A finger vascular systolic pressure measuring device, comprising:

[0008] The finger sleeve is placed on the finger to be tested and is connected to the liquid delivery module to compress the blood vessels of the finger through the pressure of the liquid;

[0009] A finger strain gauge is provided at the distal end of the finger sleeve and is used to detect pulsation data when blood flow is restored;

[0010] A liquid delivery module is connected to the finger sleeve through a water pipe and is used to provide liquid with a specific water pressure to the finger sleeve to compress the blood vessels of the finger;

[0011] A temperature control module, used for providing a temperature control function to adjust the temperature of the liquid flowing through the finger bushing;

[0012] The data processing module is used to collect and process data from the finger strain gauge, the liquid delivery module and the temperature control module to calculate the vasosystolic pressure of the finger.

[0013] As a further optional solution of the finger vasosystolic pressure measuring device, the data processing module collects and processes data from the finger strain gauge, the liquid delivery module and the temperature control module to calculate the finger vasosystolic pressure, specifically including:

[0014] Through the signal acquisition host, ensure that the finger strain gauge, liquid delivery module and temperature control module synchronously collect the pulse wave signal of the finger blood vessels, the blood vessel pressure signal, the temperature and pressure of the liquid;

[0015] Adaptive filtering technology is used to denoise the collected data;

[0016] The denoised pulse wave signal, blood vessel pressure signal, liquid temperature and pressure are converted into a time series;

[0017] Construct complex networks based on the similarity of time series and calculate the topological characteristics of complex networks;

[0018] The calculated complex network topological features are input into the deep learning model, and the deep learning model predicts the systolic blood pressure of the finger blood vessels based on the predicted input topological features.

[0019] As a further optional solution of the finger vascular systolic pressure measuring device, the adaptive filtering technology is used to perform denoising on the collected data, specifically including:

[0020] Analyze the noise type and signal characteristics of the collected data;

[0021] Select the corresponding adaptive filter type according to the noise type and signal characteristics;

[0022] Set the filtering parameters of the adaptive filter;

[0023] The collected data is filtered and denoised based on the adaptive filter with set filtering parameters.

[0024] As a further optional solution of the finger vascular systolic pressure measuring device, the denoised pulse wave signal, the vascular pressure signal, the temperature and pressure of the liquid are converted into a time series, specifically including:

[0025] Determine the sampling frequency according to the characteristics and analysis requirements of the pulse wave signal after denoising, the blood vessel pressure signal, the temperature and pressure of the liquid;

[0026] Generate a corresponding timestamp for each sampling point;

[0027] Perform data alignment for each sampling point;

[0028] The pulse wave signal, blood vessel pressure signal, liquid temperature and pressure after data alignment are arranged in the order of timestamps to form their own time series.

[0029] As a further optional solution of the finger vascular systolic pressure measuring device, the construction of a complex network based on the similarity of time series and the calculation of the topological features of the complex network specifically include:

[0030] Based on the Pearson correlation coefficient, the similarity value between each pair of time series is calculated;

[0031] According to the result of similarity calculation, a threshold is set, wherein the threshold is used to determine the similarity strength between time series;

[0032] According to the set threshold, an adjacency matrix is ​​constructed, and each element in the adjacency matrix indicates whether there is a connection between two time series;

[0033] Convert the adjacency matrix into a complex network, where nodes represent time series and edges represent similarity connections between time series;

[0034] The topological eigenvalues ​​of the complex network are calculated, and the topological characteristics of the complex network are obtained based on the topological eigenvalues ​​of the complex network.

[0035] As a further optional solution of the finger vascular systolic pressure measuring device, the temperature control module adjusts the temperature of the liquid flowing through the finger sleeve, specifically including:

[0036] The temperature sensor in the temperature control module monitors the temperature of the liquid flowing through the finger bushing in real time;

[0037] The temperature data is transmitted to the controller of the temperature control module;

[0038] The controller compares the real-time monitored temperature with the preset temperature range and makes a decision to adjust the temperature;

[0039] Depending on the controller's decision, the temperature control module will start the heating or cooling equipment;

[0040] When the heating or cooling device starts operating, it gradually adjusts the temperature of the liquid flowing through the finger sleeve.

[0041] A method for measuring finger vascular systolic pressure, the method using any one of the above-mentioned finger vascular systolic pressure measuring devices, specifically comprising:

[0042] The finger sleeve is placed on the finger to be tested, and the finger sleeve is connected to the liquid delivery module through a water pipe;

[0043] The liquid delivery module provides liquid with a specific water pressure to the finger cuff to compress the blood vessels of the finger to a blood flow blocking state;

[0044] Gradually reduce the water pressure provided by the liquid delivery module until the pulsation data when the blood flow is restored is observed by the strain gauge on the finger;

[0045] During the blood flow restoration process, the pulsation data is continuously collected through the strain gauge on the finger, and these data and the relevant data of the liquid delivery module and the temperature control module are sent to the data processing module;

[0046] The data processing module receives and processes data from the finger strain gauge, the liquid delivery module and the temperature control module, and calculates the systolic blood pressure of the finger according to a preset algorithm;

[0047] Output the calculated finger vasosystolic pressure value.

[0048] A computing device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned finger vascular systolic pressure measurement method when executing the computer program.

[0049] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for measuring finger vascular systolic pressure.

[0050] The beneficial effects of the present invention are as follows: the setting of the finger sleeve enables the measured finger to stably receive the liquid pressure from the liquid delivery module, thereby ensuring accurate compression of the finger blood vessels; the finger strain gauge is located at the distal end of the finger sleeve, and can sensitively capture the pulsation data when the blood flow is restored, providing a reliable basis for the subsequent systolic pressure calculation; the existence of the temperature control module ensures that the temperature of the liquid flowing through the finger sleeve is always maintained within an appropriate range, which helps to reduce the influence of temperature on the measurement results and improve the accuracy and stability of the measurement; the temperature control also enables the device to maintain consistent measurement performance at different ambient temperatures, thereby improving its applicability and reliability; the liquid delivery module can automatically adjust the water pressure of the liquid as needed to achieve accurate compression and relaxation of the finger blood vessels, which reduces the complexity of manual operation and improves the efficiency and accuracy of measurement; the data processing module can automatically collect and process data from each module to calculate the systolic pressure of the finger blood vessels; this automated processing not only improves the measurement speed, but also reduces the possibility of human error. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 It is a schematic diagram of the composition of a finger vascular systolic pressure measuring device of the present invention;

[0053] Figure 2 It is a schematic flow chart of a method for measuring finger vascular systolic pressure of the present invention;

[0054] Figure 3 The figure is a schematic diagram of the composition of a computing device according to the present invention. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] refer to Figures 1 to 3 , a finger vascular systolic pressure measuring device, comprising:

[0057] The finger sleeve is placed on the finger to be tested and is connected to the liquid delivery module to compress the blood vessels of the finger through the pressure of the liquid;

[0058] A finger strain gauge is provided at the distal end of the finger sleeve and is used to detect pulsation data when blood flow is restored;

[0059] A liquid delivery module is connected to the finger sleeve through a water pipe and is used to provide liquid with a specific water pressure to the finger sleeve to compress the blood vessels of the finger;

[0060] A temperature control module, used for providing a temperature control function to adjust the temperature of the liquid flowing through the finger bushing;

[0061] The data processing module is used to collect and process data from the finger strain gauge, the liquid delivery module and the temperature control module to calculate the vasosystolic pressure of the finger.

[0062] In this embodiment, the setting of the finger sleeve enables the measured finger to stably receive the liquid pressure from the liquid delivery module, thereby ensuring accurate compression of the finger blood vessels. The finger strain gauge is located at the distal end of the finger sleeve and can sensitively capture the pulsation data when the blood flow is restored, providing a reliable basis for the subsequent systolic pressure calculation. The existence of the temperature control module ensures that the temperature of the liquid flowing through the finger sleeve is always maintained within an appropriate range, which helps to reduce the impact of temperature on the measurement results and improve the accuracy and stability of the measurement. The temperature control also enables the device to maintain consistent measurement performance at different ambient temperatures, thereby improving its applicability and reliability. The liquid delivery module can automatically adjust the water pressure of the liquid as needed to achieve accurate compression and relaxation of the finger blood vessels, which reduces the complexity of manual operation and improves the efficiency and accuracy of measurement. The data processing module can automatically collect and process data from each module to calculate the systolic pressure of the finger blood vessels. This automated processing not only improves the measurement speed, but also reduces the possibility of human error.

[0063] Preferably, the data processing module collects and processes data from the finger strain gauge, the liquid delivery module and the temperature control module to calculate the systolic blood pressure of the finger, specifically including:

[0064] Through the signal acquisition host, ensure that the finger strain gauge, liquid delivery module and temperature control module synchronously collect the pulse wave signal of the finger blood vessels, the blood vessel pressure signal, the temperature and pressure of the liquid;

[0065] Adaptive filtering technology is used to denoise the collected data;

[0066] The denoised pulse wave signal, blood vessel pressure signal, liquid temperature and pressure are converted into a time series;

[0067] Construct complex networks based on the similarity of time series and calculate the topological characteristics of complex networks;

[0068] The calculated complex network topological features are input into the deep learning model, and the deep learning model predicts the systolic blood pressure of the finger blood vessels based on the predicted input topological features.

[0069] In this embodiment, the signal acquisition host ensures that the finger strain gauge, liquid delivery module and temperature control module can synchronously collect data. This synchronization is crucial for subsequent data processing and analysis because it can ensure the temporal consistency of all relevant data, thereby improving the accuracy and reliability of data analysis; the adaptive filtering technology is used to denoise the collected data, which can effectively remove noise and interference in the data and improve the purity and quality of the data, which is crucial for subsequent data analysis and systolic blood pressure calculation because noise and interference may affect the accuracy of the results; converting the denoised data into a time series helps to better understand and analyze the dynamic changes of the data. Time series analysis is the process of processing and analyzing time series data. An effective method that can reveal hidden patterns and trends in the data and provide strong support for subsequent analysis and prediction; constructing a complex network based on the similarity of time series and calculating the topological features of the complex network. This is an innovative data processing method. By converting time series data into a complex network and calculating its topological features, it can reveal the complex relationships and interactions between the data and provide richer information for subsequent deep learning models; the calculated complex network topological features are input into the deep learning model. The deep learning model can predict the systolic blood pressure of the finger blood vessels based on these features. The deep learning model has strong learning and prediction capabilities, can handle complex data relationships and nonlinear relationships, and can therefore more accurately predict the systolic blood pressure of the finger blood vessels.

[0070] Preferably, the denoising process of the collected data using the adaptive filtering technology specifically includes:

[0071] Analyze the noise type and signal characteristics of the collected data;

[0072] Select the corresponding adaptive filter type according to the noise type and signal characteristics;

[0073] Set the filtering parameters of the adaptive filter;

[0074] The collected data is filtered and denoised based on the adaptive filter with set filtering parameters.

[0075] In this embodiment, the noise type and signal characteristics of the collected data are analyzed, which is the basis of denoising, because only by accurately understanding the type of noise and the characteristics of the signal can the most appropriate filtering method and parameters be selected. This accurate analysis helps to improve the pertinence and effectiveness of the denoising; according to the noise type and signal characteristics, the corresponding adaptive filter type is selected. The adaptive filter can automatically adjust its filtering parameters according to the changes in the input signal, thereby effectively suppressing different types of noise. This selective filtering method can ensure that while removing the noise, the useful information of the original signal is retained as much as possible; the setting of the filtering parameters directly affects the performance and effect of the filter. By accurately setting the filtering parameters, it can be ensured that the adaptive filter will not cause excessive distortion or loss to the original signal while removing the noise; filtering and denoising the collected data according to the adaptive filter with set filtering parameters, this step is the actual execution of the denoising process. Since the adaptive filter has the ability to automatically adjust the parameters, it can continuously adapt to the changes in the input signal during the processing process, thereby achieving efficient and accurate denoising.

[0076] It should be noted that analyzing the noise type in the original data, such as Gaussian noise, impulse noise (salt and pepper noise), etc., in order to select a suitable adaptive filtering algorithm, and understanding the characteristics of the signal to be processed, including the frequency range and amplitude change of the signal, is helpful to design more suitable filter parameters. According to the noise type and signal characteristics, a suitable adaptive filter type is selected, such as Wiener adaptive filter, LMS adaptive filter, etc., and filter parameters are set, including the order of the filter, step factor (for LMS algorithm, etc.), regularization factor (for NLMS algorithm, etc.), etc. These parameters will directly affect the performance and convergence speed of the filter. Through iteration, the parameters of the filter are continuously adjusted according to the input signal and the expected signal (or error signal) to minimize the error between the output signal and the expected signal. By observing the output signal after filtering, the denoising effect of the filter is evaluated. According to the evaluation results, it may be necessary to adjust the parameters of the filter to obtain a better denoising effect. The optimized adaptive filter is applied to the actual environment to test its denoising performance and stability.

[0077] Preferably, the step of converting the denoised pulse wave signal, the blood vessel pressure signal, and the temperature and pressure of the liquid into a time series specifically includes:

[0078] Determine the sampling frequency according to the characteristics and analysis requirements of the pulse wave signal after denoising, the blood vessel pressure signal, the temperature and pressure of the liquid;

[0079] Generate a corresponding timestamp for each sampling point;

[0080] Perform data alignment for each sampling point;

[0081] The pulse wave signal, blood vessel pressure signal, liquid temperature and pressure after data alignment are arranged in the order of timestamps to form their own time series.

[0082] In this embodiment, the sampling frequency is accurately determined according to the characteristics and analysis requirements of the pulse wave signal after denoising, the blood vessel pressure signal, the temperature and pressure of the liquid. The selection of the sampling frequency directly affects the resolution and accuracy of the time series. By selecting a suitable sampling frequency, it can be ensured that the time series can accurately reflect the dynamic changes of the original signal; a corresponding timestamp is generated for each sampling point, and data alignment is performed on each sampling point. The generation of the timestamp enables each sampling point to have a clear time mark, thereby facilitating subsequent time series analysis and processing. Data alignment ensures the time consistency between different signals, that is, different signal values ​​at the same time point can correspond to each other; the pulse wave signal after data alignment, The blood vessel pressure signal, liquid temperature and pressure are arranged in the order of timestamps to form their own time series. The formation of time series makes the data present a clear time dimension, which is convenient for observing and analyzing the changing trends and characteristics of the data at different time points. Through a series of steps such as accurately determining the sampling frequency, generating timestamps, aligning data and forming time series, the technical solution successfully converts the denoised data into a time series. This conversion not only improves the readability and analyzability of the data, but also provides strong support for subsequent data processing and analysis. Time series analysis can reveal hidden patterns and trends in the data, and provide a more accurate and reliable basis for the calculation and prediction of finger vascular systolic pressure.

[0083] It should be noted that for pulse wave signals and vascular pressure signals, since they usually contain high-frequency components (such as heart rate variability), a higher sampling frequency is required to capture these changes. For the temperature and pressure of the liquid, if their changes are relatively slow, a lower sampling frequency can be selected. The generation of timestamps is usually based on a stable clock source (such as a system clock or a high-precision timer). The timestamp of each sampling point should be continuous and consistent with the sampling frequency. Data alignment can be achieved through interpolation, resampling or time synchronization. A time series is a collection of data arranged in chronological order. It reflects the changes of the signal over time. Each time series contains two parts of information: a timestamp and a data value.

[0084] Preferably, the method of constructing a complex network based on the similarity of time series and calculating the topological features of the complex network specifically includes:

[0085] Based on the Pearson correlation coefficient, the similarity value between each pair of time series is calculated;

[0086] According to the result of similarity calculation, a threshold is set, wherein the threshold is used to determine the similarity strength between time series;

[0087] According to the set threshold, an adjacency matrix is ​​constructed, and each element in the adjacency matrix indicates whether there is a connection between two time series;

[0088] Convert the adjacency matrix into a complex network, where nodes represent time series and edges represent similarity connections between time series;

[0089] The topological eigenvalues ​​of the complex network are calculated, and the topological characteristics of the complex network are obtained based on the topological eigenvalues ​​of the complex network.

[0090] In this embodiment, by using the Pearson correlation coefficient to calculate the similarity value between each pair of time series, the technical solution can accurately quantify the linear correlation between time series. The Pearson correlation coefficient is a commonly used statistic that can measure the degree of linear correlation between two variables, so it has good applicability in time series analysis; by setting a threshold to determine the similarity strength between time series, the technical solution can flexibly control which time series are considered similar and form a connection. This flexibility enables the technical solution to adapt to different application scenarios and data analysis needs; by constructing an adjacency matrix and converting it into a complex network, the technical solution can intuitively express the connection relationship between time series. The nodes in the complex network represent time series, and the edges represent the similarity connections between time series. This representation facilitates visualization and further analysis. By calculating the topological eigenvalues ​​of complex networks, this technical solution can reveal the structural characteristics of complex systems. Topological eigenvalues ​​such as degree distribution, clustering coefficient, average path length, etc. can reflect the overall structure and local characteristics of complex networks. It extends time series analysis to the field of complex networks, providing new perspectives and methods for time series analysis. By converting the similarities between time series into connection relationships in complex networks, complex network theory and methods can be used to analyze the potential structure and dynamic behavior of time series data. Complex networks are constructed based on time series data, thereby supporting the dynamic analysis of complex systems. By comparing the topological characteristics of complex networks at different time points or under different conditions, the dynamic changes and evolution laws of complex systems can be revealed.

[0091] It should be noted that, based on the results of similarity calculation, a threshold is set to determine which time series should be connected. Based on the set threshold, an adjacency matrix is ​​constructed, in which each element represents whether there is a connection between two time series. The adjacency matrix is ​​converted into a complex network, in which nodes represent time series and edges represent similarity connections between time series. According to the research purpose and the characteristics of the complex network, appropriate topological feature parameters are selected, such as degree, clustering coefficient, average path length, network diameter, maximum connected subgraph size, number of cores and betweenness, etc., and the specific values ​​of the selected topological feature parameters are calculated using relevant algorithms and tools. The calculated topological feature values ​​are analyzed to reveal the intrinsic structure and characteristics of the complex network.

[0092] Preferably, the temperature control module adjusts the temperature of the liquid flowing through the finger bushing, specifically comprising:

[0093] The temperature sensor in the temperature control module monitors the temperature of the liquid flowing through the finger bushing in real time;

[0094] The temperature data is transmitted to the controller of the temperature control module;

[0095] The controller compares the real-time monitored temperature with the preset temperature range and makes a decision to adjust the temperature;

[0096] Depending on the controller's decision, the temperature control module will start the heating or cooling equipment;

[0097] When the heating or cooling device starts operating, it gradually adjusts the temperature of the liquid flowing through the finger sleeve.

[0098] In this embodiment, the temperature sensor in the temperature control module can monitor the temperature of the liquid flowing through the finger sleeve in real time. This real-time monitoring ensures that the system can respond quickly to temperature changes. The controller compares the real-time monitored temperature with the preset temperature range to make an accurate decision to adjust the temperature. This precise control helps to maintain the temperature stability of the liquid in the finger sleeve and avoid the influence of temperature fluctuations on applications such as finger vascular systolic pressure measurement. According to the decision of the controller, the temperature control module will automatically start the heating or cooling device. This automatic adjustment not only improves the response speed, but also reduces the need for manual intervention, making it more efficient and energy-saving. By accurately controlling the start and stop of the heating or cooling device, it can meet the temperature requirements while minimizing Energy consumption; after the heating or cooling equipment starts working, it will gradually adjust the temperature of the liquid flowing through the finger sleeve until it reaches the preset temperature range. This gradual adjustment method helps to avoid discomfort to the fingers caused by sudden temperature changes, while ensuring the temperature stability of the liquid in the finger sleeve. A stable temperature environment is essential for the measurement of physiological parameters such as finger vascular systolic pressure, which helps to improve the accuracy and reliability of the measurement; by adjusting the preset temperature range, it can adapt to different application scenarios and user needs. For example, in a cold environment, a higher temperature range can be set to keep the fingers warm; in a hot environment, a lower temperature range can be set to avoid overheating of the fingers. This flexibility and adaptability make this technical solution have broad application prospects.

[0099] A method for measuring finger vascular systolic pressure, the method using any one of the above-mentioned finger vascular systolic pressure measuring devices, specifically comprising:

[0100] The finger sleeve is placed on the finger to be tested, and the finger sleeve is connected to the liquid delivery module through a water pipe;

[0101] The liquid delivery module provides liquid with a specific water pressure to the finger cuff to compress the blood vessels of the finger to a blood flow blocking state;

[0102] Gradually reduce the water pressure provided by the liquid delivery module until the pulsation data when the blood flow is restored is observed by the strain gauge on the finger;

[0103] During the blood flow restoration process, the pulsation data is continuously collected through the strain gauge on the finger, and these data and the relevant data of the liquid delivery module and the temperature control module are sent to the data processing module;

[0104] The data processing module receives and processes data from the finger strain gauge, the liquid delivery module and the temperature control module, and calculates the systolic blood pressure of the finger according to a preset algorithm;

[0105] Output the calculated finger vasosystolic pressure value.

[0106] A computing device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned finger vascular systolic pressure measurement method when executing the computer program.

[0107] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for measuring finger vascular systolic pressure.

[0108] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A finger vascular systolic pressure measuring device, characterized in that: include: The finger sleeve is placed on the finger to be tested and is connected to the liquid delivery module to compress the blood vessels of the finger through the pressure of the liquid; A finger strain gauge is provided at the distal end of the finger sleeve and is used to detect pulsation data when blood flow is restored; A liquid delivery module is connected to the finger sleeve through a water pipe and is used to provide liquid with a specific water pressure to the finger sleeve to compress the blood vessels of the finger; A temperature control module, used to provide a temperature control function to adjust the temperature of the liquid flowing through the finger bushing; The data processing module is used to collect and process data from the finger strain gauge, the liquid delivery module and the temperature control module to calculate the vasosystolic pressure of the finger.

2. A finger vascular systolic pressure measuring device according to claim 1, characterized in that: The data processing module collects and processes data from the finger strain gauge, the liquid delivery module and the temperature control module to calculate the systolic blood pressure of the finger, specifically including: Through the signal acquisition host, ensure that the finger strain gauge, liquid delivery module and temperature control module synchronously collect the pulse wave signal of the finger blood vessels, the blood vessel pressure signal, the temperature and pressure of the liquid; Adaptive filtering technology is used to denoise the collected data; The denoised pulse wave signal, blood vessel pressure signal, liquid temperature and pressure are converted into a time series; Construct complex networks based on the similarity of time series and calculate the topological characteristics of complex networks; The calculated complex network topological features are input into the deep learning model, and the deep learning model predicts the systolic blood pressure of the finger blood vessels based on the predicted input topological features.

3. A finger vascular systolic pressure measuring device according to claim 2, characterized in that: The denoising process of the collected data using the adaptive filtering technology specifically includes: Analyze the noise type and signal characteristics of the collected data; Select the corresponding adaptive filter type according to the noise type and signal characteristics; Set the filtering parameters of the adaptive filter; The collected data is filtered and denoised based on the adaptive filter with set filtering parameters.

4. A finger vascular systolic pressure measuring device according to claim 3, characterized in that: The step of converting the denoised pulse wave signal, the blood vessel pressure signal, and the temperature and pressure of the liquid into a time series specifically includes: Determine the sampling frequency according to the characteristics and analysis requirements of the pulse wave signal after denoising, the blood vessel pressure signal, the temperature and pressure of the liquid; Generate a corresponding timestamp for each sampling point; Perform data alignment for each sampling point; The pulse wave signal, blood vessel pressure signal, liquid temperature and pressure after data alignment are arranged in the order of timestamps to form their own time series.

5. A finger vascular systolic pressure measuring device according to claim 4, characterized in that: The method of constructing a complex network based on the similarity of time series and calculating the topological features of the complex network specifically includes: Based on the Pearson correlation coefficient, the similarity value between each pair of time series is calculated; According to the result of similarity calculation, a threshold is set, wherein the threshold is used to determine the similarity strength between time series; According to the set threshold, an adjacency matrix is ​​constructed, and each element in the adjacency matrix indicates whether there is a connection between two time series; Convert the adjacency matrix into a complex network, where nodes represent time series and edges represent similarity connections between time series; The topological eigenvalues ​​of the complex network are calculated, and the topological characteristics of the complex network are obtained based on the topological eigenvalues ​​of the complex network.

6. A finger vascular systolic pressure measuring device according to claim 5, characterized in that: The temperature control module adjusts the temperature of the liquid flowing through the finger bushing, specifically comprising: The temperature sensor in the temperature control module monitors the temperature of the liquid flowing through the finger bushing in real time; The temperature data is transmitted to the controller of the temperature control module; The controller compares the real-time monitored temperature with the preset temperature range and makes a decision to adjust the temperature; Depending on the controller's decision, the temperature control module will start the heating or cooling equipment; When the heating or cooling device starts operating, it gradually adjusts the temperature of the liquid flowing through the finger sleeve.

7. A method for measuring finger vascular systolic pressure, characterized in that: The method uses any one of the finger vascular systolic pressure measuring devices of claims 1-6, and specifically comprises: The finger sleeve is placed on the finger to be tested, and the finger sleeve is connected to the liquid delivery module through a water pipe; The liquid delivery module provides liquid with a specific water pressure to the finger cuff to compress the blood vessels of the finger to a blood flow blocking state; Gradually reduce the water pressure provided by the liquid delivery module until the pulsation data when the blood flow is restored is observed by the strain gauge on the finger; During the blood flow restoration process, the pulsation data is continuously collected through the strain gauge on the finger, and these data and the relevant data of the liquid delivery module and the temperature control module are sent to the data processing module; The data processing module receives and processes data from the finger strain gauge, the liquid delivery module and the temperature control module, and calculates the systolic blood pressure of the finger according to a preset algorithm; Output the calculated finger vasosystolic pressure value.

8. A computing device, characterized in that The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for measuring the systolic blood pressure of a finger blood vessel as claimed in claim 7 when executing the computer program.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the method for measuring finger vascular systolic pressure according to claim 7 are implemented.

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