Three-dimensional Pulse Signal Quantification Analysis Method and System

Abstract

The present invention provides a three-dimensional pulse signal quantization analysis method and system, belonging to the technical field of signal processing. Through a binocular vision pulse image acquisition system, continuous contact film images at the radial artery of a traditional Chinese medicine pulse training instrument are collected, and a three-dimensional pulse signal is reconstructed. The ROI region is selected to obtain a central region containing the effective components of the three-dimensional pulse signal. Using dynamic waveform slicing, the three-dimensional pulse signal is segmented into multiple two-dimensional slices to obtain a dynamic slice sequence. Using a composite sine cascade modeling model to fit the two-dimensional signals with sine similarity characteristics in the dynamic slice sequence to determine the pulse length, pulse width, and pulse height of the pulse signal. Using dynamic waveform slicing, the three-dimensional pulse signal is reduced to two-dimensional slices, reducing the data volume. Through composite sine cascade modeling, the pulse signal characteristics are accurately fitted, effectively improving the pulse analysis effect compared with the one-dimensional time series method.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and in particular, to a three-dimensional pulse signal quantization analysis method and system. Background Art

[0002] The pulse signal is an important physiological signal that reflects the activity of the cardiovascular system. The main source of the pulse signal is the pulsation of the heart, that is, the process of the heart pushing oxygen and blood to the whole body every time it beats. These pulsations are propagated by the arteries and finally generate a perceptible pulse signal at specific positions in the body. Research shows that the pulse signal carries rich physiological and pathological information, and the physiological information extracted from the pulse signal can be used to evaluate the heart health status. Although the existing pulse signal analysis methods have made certain progress, there are still some deficiencies. The traditional pulse signal processing methods mainly focus on one-dimensional time series.

[0003] However, the pulse signal processing method for one-dimensional time series cannot fully reflect the complexity and multi-dimensional characteristics of the pulse signal, resulting in poor pulse analysis effect. Summary of the Invention

[0004] The present invention provides a three-dimensional pulse signal quantization analysis method and system to solve the defect that the existing technology has poor effect in processing pulse signals relying on one-dimensional time series.

[0005] In a first aspect, the present invention provides a three-dimensional pulse signal quantization analysis method, including:

[0006] Collecting continuous contact film images at the radial artery of a traditional Chinese medicine pulse training instrument through a binocular vision pulse image acquisition system, and reconstructing a three-dimensional pulse signal;

[0007] Selecting an ROI region for the reconstructed three-dimensional pulse signal to obtain a central region containing the effective components of the three-dimensional pulse signal;

[0008] Using dynamic waveform slicing to divide the three-dimensional pulse signal in the central region into multiple two-dimensional slices to obtain a dynamic slice sequence;

[0009] Fitting the two-dimensional signals with sinusoidal similarity characteristics in the dynamic slice sequence using a composite sine cascade modeling model;

[0010] Determining the pulse length, pulse width, and pulse height of the pulse signal according to the frequency, phase, and amplitude of the sine waveform after the fitting is completed.

[0011] According to the three-dimensional pulse signal quantization analysis method provided by the present invention, the selecting an ROI region for the reconstructed three-dimensional pulse signal to obtain a central region containing the effective components of the three-dimensional pulse signal includes:

[0012] Represent the three-dimensional pulse signal using the pulse length grid length, pulse width grid length, and pulse height amplitude to obtain a three-dimensional pulse signal image;

[0013] Based on the indicator function, screen the ROI region in the three-dimensional pulse signal image that contains the effective components of the three-dimensional pulse signal as the central region.

[0014] According to a three-dimensional pulse signal quantization analysis method provided by the present invention, the using dynamic waveform slicing to divide the three-dimensional pulse signal in the central region into multiple two-dimensional slices to obtain a dynamic slice sequence includes:

[0015] Select the dimension of the pulse width as the slicing direction according to the characteristics of the three-dimensional pulse signal in the central region;

[0016] According to the dynamic change of the three-dimensional pulse signal in the central region in terms of pulse width and combined with the grid distribution during the reconstruction of the three-dimensional pulse signal, adjust the position and range of the slices;

[0017] Based on the position and range of the slices, extract two-dimensional slices from the three-dimensional pulse signal at each slice point;

[0018] Arrange all the extracted two-dimensional slices in the order of the slicing direction to form a dynamic slice sequence.

[0019] According to a three-dimensional pulse signal quantization analysis method provided by the present invention, the using a composite sine cascade modeling model to fit the two-dimensional signals with sine similarity characteristics in the dynamic slice sequence includes:

[0020] Construct a composite sine cascade modeling model based on the slice characteristics in the dynamic slice sequence;

[0021] Combine parameter boundary constraint conditions to solve the parameters of the composite sine cascade modeling model;

[0022] Use an optimization algorithm to determine the optimal characteristic parameters of each sine function among the solved parameters, minimize the fitting error, and obtain the best model parameters.

[0023] According to a three-dimensional pulse signal quantization analysis method provided by the present invention, the constructing a composite sine cascade modeling model based on the slice characteristics in the dynamic slice sequence includes:

[0024] Find all the wave valleys of the dynamic slice sequence, perform threshold processing on the dynamic slice sequence, and extract the rising edge and falling edge;

[0025] Extract a two-dimensional slice array from any current valley position to the next valley position from the dynamic slice sequence based on the rising edge and the falling edge;

[0026] Based on the curve characteristics of all the two-dimensional slice arrays, respectively use a sine function model for fitting to construct a composite sine cascade modeling model.

[0027] According to a three-dimensional pulse signal quantization analysis method provided by the present invention, before solving the parameters of the composite sine cascade modeling model in combination with parameter boundary constraint conditions, it further includes:

[0028] Initialize each parameter to obtain the corresponding initial value;

[0029] Based on the initial value, set boundary constraint conditions for each parameter.

[0030] According to a three-dimensional pulse signal quantization analysis method provided by the present invention, using an optimization algorithm to determine the optimal characteristic parameters of each sine function among the solved parameters, minimizing the fitting error to obtain the best model parameters, includes:

[0031] Determine the objective function to be minimized during the optimization solution process;

[0032] Select the YS-PSO optimization algorithm to optimize the solved parameters, minimize the fitting error to obtain the optimal model parameters, and organize all the optimal model parameters into a feature vector set.

[0033] According to a three-dimensional pulse signal quantization analysis method provided by the present invention, the composite sine cascade modeling model is:

[0034] ;

[0035] Wherein, is the number of sine function models; is the composite sine cascade modeling model; is the total length of each two-dimensional slice pulse length divided by the number of grids; is the current sine function model, represents the th sine function model amplitude; represents the th sine function model frequency; represents the th sine function model phase shift; represents the th sine function model vertical offset.

[0036] According to a three-dimensional pulse signal quantization analysis method provided by the present invention, reconstructing the three-dimensional pulse signal includes:

[0037] Filter, binarize, and extract feature points from the collected image pulse signals, and reconstruct three-dimensional pulse signals in combination with the binocular vision imaging principle.

[0038] In a second aspect, the present invention also provides a three-dimensional pulse signal quantization analysis system, including:

[0039] A reconstruction module for collecting continuous contact film images at the radial artery of a traditional Chinese medicine pulse training instrument through a binocular vision pulse image acquisition system and reconstructing three-dimensional pulse signals;

[0040] A selection module for selecting an ROI region from the reconstructed three-dimensional pulse signals to obtain a central region containing the effective components of the three-dimensional pulse signals;

[0041] A slicing module for using dynamic waveform slicing to divide the three-dimensional pulse signals in the central region into multiple two-dimensional slices to obtain a dynamic slice sequence;

[0042] A fitting module for fitting two-dimensional signals with sinusoidal similarity characteristics in the dynamic slice sequence using a composite sine cascade modeling model;

[0043] A determination module for determining the pulse length, pulse width, and pulse height of the pulse signal according to the frequency, phase, and amplitude of the sine waveform after the fitting is completed.

[0044] In a third aspect, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the three-dimensional pulse signal quantization analysis method as described in any one of the above.

[0045] In a fourth aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the three-dimensional pulse signal quantization analysis method as described in any one of the above.

[0046] In a fifth aspect, the present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the three-dimensional pulse signal quantization analysis method as described in any one of the above.

[0047] A three-dimensional pulse signal quantization analysis method and system provided by the present invention collect continuous contact film images at the radial artery of a traditional Chinese medicine pulse training instrument through a binocular vision pulse image acquisition system, reconstruct a three-dimensional pulse signal; select an ROI region for the reconstructed three-dimensional pulse signal to obtain a central region containing the effective components of the three-dimensional pulse signal; use dynamic waveform slicing to segment the three-dimensional pulse signal in the central region into multiple two-dimensional slices to obtain a dynamic slice sequence; use a composite sine cascade modeling model to fit the two-dimensional signals with sine similarity characteristics in the dynamic slice sequence; determine the pulse length, pulse width, and pulse height of the pulse signal according to the frequency, phase, and amplitude of the sine waveform after fitting. Using dynamic waveform slicing, the three-dimensional pulse signal is reduced to two-dimensional slices, reducing the data volume, and through composite sine cascade modeling, the characteristics of the pulse signal are accurately fitted. Compared with the one-dimensional time series, the pulse analysis effect is effectively improved through the pulse length, pulse width, and pulse height. Description of the Drawings

[0048] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0049] Figure 1 It is a schematic flowchart of the three-dimensional pulse signal quantization analysis method provided in this embodiment;

[0050] Figure 2 It is a schematic diagram of the reconstructed three-dimensional pulse signal;

[0051] Figure 3 It is a schematic diagram of the three-dimensional pulse signal distribution provided in this embodiment;

[0052] Figure 4 It is a schematic diagram of the curve characteristics of the two-dimensional slice array provided in this embodiment;

[0053] Figure 5 It is a dynamic slice sequence diagram provided in this embodiment;

[0054] Figure 6 It is a fitting effect diagram of a certain slice provided in this embodiment;

[0055] Figure 7 It is a fitting effect diagram of the entire dynamic slice sequence provided in this embodiment;

[0056] Figure 8 It is a partial enlarged schematic diagram of the fitting effect diagram of the entire dynamic slice sequence provided in this embodiment;

[0057] Figure 9 is the fitting residual plot of the entire dynamic slice sequence provided by this embodiment;

[0058] Figure 10 is the average model diagram of a certain pulse condition provided by this embodiment;

[0059] Figure 11 is the schematic structural diagram of the three-dimensional pulse signal quantization analysis system provided by this embodiment;

[0060] Figure 12 is the schematic structural diagram of the electronic device provided by this embodiment. Detailed implementation manners

[0061] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without making creative efforts based on the embodiments in the present invention belong to the scope of protection of the present invention.

[0062] Figure 1 is the schematic flowchart of the three-dimensional pulse signal quantization analysis method provided by this embodiment, Figure 2 is the schematic diagram of the reconstructed three-dimensional pulse signal, Figure 3 is the schematic diagram of the three-dimensional pulse signal distribution provided by this embodiment.

[0063] As Figure 1 shown, the three-dimensional pulse signal quantization analysis method provided by the embodiments of the present invention mainly includes the following steps:

[0064] 101. Through a binocular vision pulse image acquisition system, continuously acquire contact film images at the radial artery of a traditional Chinese medicine pulse training instrument, and reconstruct a three-dimensional pulse signal.

[0065] In a specific implementation process, the binocular vision pulse image acquisition system is an innovative system that combines binocular vision technology and biomedical detection, and is mainly used to accurately extract three-dimensional dynamic information of the human pulse. Through the binocular vision imaging principle, calculate the spatial deformation information of the pulse film, perform feature point matching on the left and right images using the SIFT feature matching algorithm, reconstruct the three-dimensional dynamic model of the pulse, and use a laser displacement sensor to verify the measurement accuracy of the binocular system. The error analysis results show that the system has high reliability. During reconstruction, specifically, preprocess the acquired image pulse signal, such as filtering, binarization, and feature point extraction, and combine the binocular vision imaging principle, that is, obtain depth information through images from two different perspectives, and reconstruct a three-dimensional pulse signal. As Figure 2 shown, it is the schematic diagram of the reconstructed three-dimensional pulse signal.

[0066] The core is divided into two parts. One is camera calibration and synchronous acquisition, and the other is signal processing and accuracy optimization. Among them, camera calibration and synchronous acquisition include: using Zhang Zhengyou calibration method for binocular camera calibration to simplify the operation process and reduce the implementation difficulty, realizing CMOS sensor initialization and data synchronous acquisition based on FPGA and I2C interface, optimizing data processing efficiency through AXI HP bus and DDR3 cache, and combining hardware external trigger and software control to ensure frame-level synchronization of binocular cameras (error < 1ms). While signal processing and accuracy optimization include: designing a Butterworth low-pass filter to separate the actual pulse waveform from the laser signal and reduce noise interference; configuring enhancement coefficients for overlapping regions and non-overlapping regions respectively to improve the image detail processing ability.

[0067] 102. Select the ROI region from the reconstructed three-dimensional pulse signal to obtain the central region containing the effective components of the three-dimensional pulse signal.

[0068] Using the pulse length grid length, pulse width grid length, and pulse height amplitude, represent the three-dimensional pulse signal to obtain the three-dimensional pulse signal image, as Figure 3 shown. Use T ( x , y , z ) to represent the three-dimensional pulse signal. x represents the pulse length grid length, y represents the pulse width grid length, z represents the pulse height amplitude.

[0069] Observe the three-dimensional pulse signal as Figure 3 and find that the effective components of the signal are mainly concentrated in the central region. Define this region as the ROI region. Based on the indicator function, screen the ROI region in the three-dimensional pulse signal image that contains the effective components of the three-dimensional pulse signal as the central region, as shown in formula (1):

[0070] (1)

[0071] In the formula, T ROI represents the three-dimensional pulse signal after retaining the ROI region, I ( z > θ ) represents the indicator function. When z > θ , retain its value, otherwise it is 0. θ is the set threshold.

[0072] Thus, the ROI region containing the effective components is effectively extracted.

[0073] 103. Using dynamic waveform slicing, the three-dimensional pulse signal in the central region is segmented into multiple two-dimensional slices to obtain a dynamic slice sequence.

[0074] Dynamic slicing is a technique for signal segmentation and analysis based on the dynamic characteristics of signals, aiming to simplify the signal processing process through dimensionality reduction while retaining key feature information and improving the efficiency and accuracy of signal analysis.

[0075] Specifically, according to the characteristics of the three-dimensional pulse signal in the central region, a suitable pulse width dimension is selected as the slicing direction, and the slice position and range are set according to the dynamic changes of the three-dimensional pulse signal in the pulse width dimension in combination with the reconstructed grid distribution. By extracting the two-dimensional slices of each slice point, a dynamic slice sequence is formed, which fully presents the dynamic changes of the signal, as Figure 4 is a schematic diagram of the curve characteristics of the two-dimensional slice array, as Figure 5 shown is the dynamic slice sequence diagram.

[0076] 104. Use the composite sine cascade modeling model to fit the two-dimensional signals with sine similarity characteristics in the dynamic slice sequence.

[0077] The composite sine cascade modeling model is a tool for fitting data with complex characteristics. By cascading multiple sine functions together, a composite model is formed, which can accurately fit complex signals with different frequencies, phases, and amplitudes. The core idea of this module is to combine multiple sine waveforms to achieve higher-precision fitting of complex waveforms.

[0078] Specifically, each two-dimensional signal in the dynamic slice sequence is fitted by the composite sine cascade modeling model, and the waveform that best fits the two-dimensional signal is selected from among numerous sine waveforms.

[0079] As Figure 6 shown, is the fitting effect diagram of a certain slice, Figure 7 is the fitting effect diagram of the entire dynamic slice sequence, Figure 8 is a partial enlarged schematic diagram of the fitting effect diagram of the entire dynamic slice sequence, Figure 9 is the fitting residual diagram of the entire dynamic slice sequence.

[0080] Through Figures 6 - 9 the fitting effect diagram, the corresponding indicators are shown in Table 1 as follows:

[0081] Table 1

[0082]

[0083] Among them, MSE is the mean square error, RMSE is the root mean square error, and MAE is the mean absolute error.

[0084] 105. Determine the pulse length, pulse width, and pulse height of the pulse signal according to the frequency, phase, and amplitude of the sine waveform after fitting is completed.

[0085] After obtaining the waveform that best fits the two-dimensional slice through fitting, the frequency, phase, and amplitude of the fitted sine waveform can be determined. Taking the frequency as the pulse length, the phase as the pulse width, and the amplitude as the pulse height, the three-dimensional characteristics of the pulse signal are successfully obtained.

[0086] Since the present invention adopts the dynamic waveform slicing technology, it effectively solves the problem of the complex and changeable three-dimensional pulse signal. By reducing the three-dimensional pulse signal to a two-dimensional signal, the amount of data is significantly reduced, and the calculation efficiency is improved. This technology has high flexibility and can flexibly select the time point and range of slicing according to the dynamic changes of the signal to adapt to the analysis requirements in different scenarios.

[0087] Combined with the composite sine cascade modeling method, it can effectively fit the two-dimensional slice pulse signal. By accurately modeling the changes of each signal, the detailed characteristics of the pulse signal can be captured, thereby improving the accuracy of signal quantization analysis. In addition, this model has good scalability and can increase or decrease the number of sine functions according to needs to adapt to pulse signals of different complexities.

[0088] Compared with the analysis of the one-dimensional time series pulse signal, it can analyze the complex pulse shape from multiple angles, effectively improving the pulse analysis effect and accuracy.

[0089] Furthermore, on the basis of the above embodiments, in this embodiment, the three-dimensional pulse signal in the central region is segmented into multiple two-dimensional slices by using dynamic waveform slicing to obtain a dynamic slice sequence, including:

[0090] Select the dimension of the pulse width as the slicing direction according to the characteristics of the three-dimensional pulse signal in the central region.

[0091] According to the dynamic change of the three-dimensional pulse signal in the pulse width and combined with the grid distribution during the reconstruction of the three-dimensional pulse signal, adjust the position and range of the slice. Specifically, according to the dynamic change of the three-dimensional pulse signal in the pulse width and combined with the grid distribution during reconstruction, set the position and range of the slice to the size of the grid, that is, assume the original grid distribution is a × b , where a represents the pulse length grid length and b represents the pulse width grid length. According to the pulse width grid length b, the signal is segmented into b slices, and the range of each slice is 1 / b.

[0092] Then the slice position is expressed as (2):

[0093] (2)

[0094] Among them, k is the index of the slice, is the k th slice's y position in the

[0095] Based on the position and range of the slice, at each slice point a two-dimensional slice is extracted from the three-dimensional signal,

[0096] (3)

[0097] which is composed of the values of the three-dimensional signal at that position. The specific formula is shown as (3):

[0098] (4)

[0099] Among them, b is the number of slices.

[0100] Furthermore, on the basis of the above embodiments, in this embodiment, a composite sine cascade modeling model is used to fit the two-dimensional signals with sine similarity characteristics in the dynamic slice sequence, including: constructing a composite sine cascade modeling model based on the slice characteristics in the dynamic slice sequence; solving the parameters of the composite sine cascade modeling model in combination with parameter boundary constraints; using an optimization algorithm to determine the optimal characteristic parameters of each sine function among the solved parameters to minimize the fitting error and obtain the optimal model parameters.

[0101] Specifically, constructing a composite sine cascade modeling model based on the slice characteristics in the dynamic slice sequence includes: finding all the troughs of the dynamic slice sequence, performing threshold processing on the dynamic slice sequence, and extracting the rising edge and the falling edge; for the convenience of understanding, hereinafter data ( k ) is used to replace the dynamic slice sequence, as shown in formula (5):

[0102] (5)

[0103] And the rising edge and the falling edge are extracted, and the formulas are as (6)(7):

[0104] (6)

[0105] (7)

[0106] Among them, Data is data(k) the entire dynamic slice sequence array, BG is the position storing the rising edge and the falling edge of the square wave, that is, the position of the trough;j The index variable traverses each element of the dynamic slice sequence of the array data ( k ); line is data ( k )'s length.

[0107] Based on the rising edge and falling edge, extract the two-dimensional slice array s from the dynamic slice sequence data ( k ) from any current valley position BG ( n ) to the next valley position BG (n + 1), as shown in formulas (8) and (9):

[0108] (8)

[0109] (9)

[0110] Among them, BG ( n ) and BG ( n + 1) respectively represent BG the n th and the n + 1th elements in the array; i is BG the specific value in the array, Data ( i ) is the entire dynamic slice sequence from position BG ( n ) to position BG ( n + 1) of the array, s is the two-dimensional slice array of the dynamic slice sequence, Length is the length of this two-dimensional array.

[0111] Based on the curve characteristics of all two-dimensional slice arrays, respectively use the sine function model for fitting to construct a composite sine cascade modeling model. The curve characteristics of the two-dimensional slice array are as Figure 4 shown, so use the sine function model for fitting, and the specific formula is as (10):

[0112] (10)

[0113] Among them, s ( τ ) is the sine function model, A is the amplitude of the model; B is the model frequency; C is the model phase shift, D is the model vertical offset,τ It is the ratio of the length of the sub - array to the number of grid points of the pulse length.

[0114] The entire dynamic slice sequence is fitted with a composite sine cascade model function, and the specific formula is as in (11):

[0115] (11)

[0116] Among them, is the number of sine function models; is the composite sine cascade modeling model; is the total length of each two - dimensional slice pulse length divided by the number of grid points; is the current sine function model, represents the th amplitude of the sine function model, that is, the maximum deviation value of the sine wave; represents the th frequency of the sine function model, which controls the period of the sine wave, and the period is , and the larger the frequency, the faster the waveform repeats; represents the th phase shift of the sine function model, which represents the translation of the waveform along the x - axis. A positive value represents a left translation, and a negative value represents a right translation; represents the th vertical offset of the sine function model, which represents the translation of the waveform along the y - axis and changes the mid - line position of the waveform.

[0117] Specifically, before solving the parameters of the composite sine cascade modeling model in combination with the parameter boundary constraint conditions, it also includes: initializing each parameter. Before optimization, a reasonable initial value needs to be selected for each parameter , , , .

[0118] For , the formula for the initialized value is as in (12):

[0119] (12)

[0120] Among them, max( s m ) is the maximum value of the m th sub - array s , min( s m ) is the minimum value of the m th sub - array s , and the sub - array is the two - dimensional slice array.

[0121] For The formula for the initial value is as in (13):

[0122] (13)

[0123] where, is the grid interval, i.e., the ratio of the total length of the grid to the total number of grids.

[0124] For the formula for the initial value is as in (14):

[0125] (14)

[0126] For the formula for the initial value is as in (15):

[0127] (15)

[0128] where, represents the th two-dimensional slice array.

[0129] To ensure the rationality and physical meaning of the parameters, boundary constraint conditions need to be set for each parameter in the model based on the initial values, as follows:

[0130] Amplitude constraint: A m ∈ [0, 2 ⋅ Am ;

[0131] Frequency constraint: B m ∈ B min , B max , where B min = , B max = ;

[0132] Phase constraint: C m ∈ [0, 2π] (uniqueness within the period);

[0133] Offset constraint: D m ∈ min(s m ) , max( s m )].[[]]END]]

[0134] Specifically, determine the optimal characteristic parameters of each sine function among the parameters to be solved, minimize the fitting error, and obtain the optimal model parameters, that is, use an optimization algorithm to find the optimal characteristic parameters, and solve the parameters of each sine function in the composite sine cascade modeling model through the optimization algorithm. , , , , so that the model minimizes the fitting error, thereby obtaining the optimal model parameters, including:

[0135] Define the objective function. The objective function is the function to be minimized during the solution process, usually the mean square error (MSE), that is, the average of the sum of the squares of the differences between the model output and the actual data. The formula is as in (16):

[0136] (16)

[0137] Where l is the index variable that traverses the dynamic slice elements of the array.

[0138] Select the YS-PSO optimization algorithm to optimize the parameters to be solved, minimize the fitting error, obtain the optimal model parameters, and organize all the optimal model parameters into a feature vector set.

[0139] For example, the pulse length grid length and the pulse width grid length are 115 and 114 respectively. That is, the original three-dimensional pulse signal is a two-dimensional array with a data structure of 115×114 elements, and the memory occupancy is about 98KB. After using the three-dimensional pulse signal quantization analysis method based on dynamic waveform slicing and composite sine cascade modeling, the data is optimized to a two-dimensional array of 115×4 elements, and the memory occupancy is reduced to 5KB. According to the calculation formula of the data compression ratio as in (17):

[0140] (17)

[0141] Calculate that the data compression ratio reaches 24.5, which means that the size of the compressed data is 1 / 24.5 of the original data size. Or rather, the data is compressed by about 94.89%. This dimensionality reduction process not only simplifies the data structure but also retains the core features of the pulse signal, providing a more refined data basis for pulse waveform analysis and pulse condition recognition.

[0142] Using the parameters estimated by the three-dimensional pulse signal quantization analysis method based on dynamic waveform slicing and composite sine cascade modeling of the present invention, obtain the average model and feature vector of a certain pulse condition. The average model is as Figure 10 shown.

[0143] Further, in this embodiment, after obtaining the pulse signal, it further includes converting the processed pulse signal into a classifiable pulse condition label. The core process includes feature extraction, feature selection, model training, and classification result output, specifically as follows:

[0144] After passing the two-sample Kolmogorov-Smirnov (KS) test, the optimal model parameters obtained by fitting the composite sine cascade modeling model are sorted into a feature vector set. In this example, the size of the feature vector is 24840×4.

[0145] For the tested feature vectors, 80% of the feature vectors can be used as the training set, and 20% of the feature vectors can be used as the test set.

[0146] Select different training classification models: Probabilistic Neural Network (PNN), Decision Tree (DT), Random Forest (RF) classifier, and Convolutional Neural Network-Bidirectional Long Short-Term Memory Network with Attention mechanism (CNN-BiLSTM-Attention) model.

[0147] Using the new variables formed by different combinations of feature parameters as inputs, train the classifier to perform pulse condition recognition on the collected data pulses and select the accuracy rate ( Ac ), unweighted average precision rate ( Pr ), unweighted average recall rate ( Re ), unweighted average F1 score ( F1 ), and kappa coefficient ( Kap ) as the evaluation indicators for the classification performance of the classifier, as shown in Table 2:

[0148] Table 2

[0149]

[0150] Then, based on the results in Table 2, the optimal model can be determined for the conversion of the pulse signal, ensuring the effect and accuracy of the signal conversion.

[0151] Based on the same general inventive concept, the present invention also protects a three-dimensional pulse signal quantization analysis system. The three-dimensional pulse signal quantization analysis system described below can be correspondingly referred to the three-dimensional pulse signal quantization analysis method described above.

[0152] Figure 11 It is a schematic structural diagram of the three-dimensional pulse signal quantization analysis system provided in this embodiment.

[0153] As Figure 11As shown in the figure, a three-dimensional pulse signal quantization analysis system provided in this embodiment includes:

[0154] A reconstruction module 1101, configured to collect continuous contact film images of a traditional Chinese medicine pulse training instrument at the radial artery through a binocular vision pulse image acquisition system, and reconstruct a three-dimensional pulse signal;

[0155] A selection module 1102, configured to perform ROI region selection on the reconstructed three-dimensional pulse signal to obtain a central region containing the effective components of the three-dimensional pulse signal;

[0156] A slicing module 1103, configured to use dynamic waveform slicing to divide the three-dimensional pulse signal in the central region into multiple two-dimensional slices to obtain a dynamic slice sequence;

[0157] A fitting module 1104, configured to fit two-dimensional signals with sinusoidal similarity characteristics in the dynamic slice sequence using a composite sine cascade modeling model;

[0158] A determination module 1105, configured to determine the pulse length, pulse width, and pulse height of the pulse signal according to the frequency, phase, and amplitude of the sine waveform after fitting.

[0159] Figure 12 It is a schematic structural diagram of an electronic device provided in this embodiment.

[0160] As Figure 12 shown, the electronic device may include: a processor 1210, a communication interface 1220, a memory 1230, and a communication bus 1240. Among them, the processor 1210, the communication interface 1220, and the memory 1230 communicate with each other through the communication bus 1240. The processor 1210 can call logic instructions in the memory 1230 to execute a three-dimensional pulse signal quantization analysis method, which includes: collecting continuous contact film images of a traditional Chinese medicine pulse training instrument at the radial artery through a binocular vision pulse image acquisition system, and reconstructing a three-dimensional pulse signal; performing ROI region selection on the reconstructed three-dimensional pulse signal to obtain a central region containing the effective components of the three-dimensional pulse signal; using dynamic waveform slicing to divide the three-dimensional pulse signal in the central region into multiple two-dimensional slices to obtain a dynamic slice sequence; using a composite sine cascade modeling model to fit two-dimensional signals with sinusoidal similarity characteristics in the dynamic slice sequence; determining the pulse length, pulse width, and pulse height of the pulse signal according to the frequency, phase, and amplitude of the sine waveform after the fitting is completed.

[0161] In addition, when the logical instructions in the above-mentioned memory 1230 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, external hard drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0162] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the three-dimensional pulse signal quantization analysis method provided by the above-mentioned various methods. The method includes: collecting continuous contact film images at the radial artery of a traditional Chinese medicine pulse training instrument through a binocular vision pulse image acquisition system, and reconstructing a three-dimensional pulse signal; performing ROI region selection on the reconstructed three-dimensional pulse signal to obtain a central region containing the effective components of the three-dimensional pulse signal; using dynamic waveform slicing to divide the three-dimensional pulse signal in the central region into multiple two-dimensional slices to obtain a dynamic slice sequence; using a composite sine cascade modeling model to fit the two-dimensional signals with sine similarity characteristics in the dynamic slice sequence; and determining the pulse length, pulse width, and pulse height of the pulse signal according to the frequency, phase, and amplitude of the sine waveform after the fitting is completed.

[0163] On another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is implemented to execute the three-dimensional pulse signal quantization analysis method provided by the above-mentioned various methods. The method includes: collecting continuous contact film images at the radial artery of a traditional Chinese medicine pulse training instrument through a binocular vision pulse image acquisition system, and reconstructing a three-dimensional pulse signal; performing ROI region selection on the reconstructed three-dimensional pulse signal to obtain a central region containing the effective components of the three-dimensional pulse signal; using dynamic waveform slicing to divide the three-dimensional pulse signal in the central region into multiple two-dimensional slices to obtain a dynamic slice sequence; using a composite sine cascade modeling model to fit the two-dimensional signals with sine similarity characteristics in the dynamic slice sequence; and determining the pulse length, pulse width, and pulse height of the pulse signal according to the frequency, phase, and amplitude of the sine waveform after the fitting is completed.

[0164] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative work.

[0165] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0166] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A three-dimensional pulse signal quantization analysis method, characterized in that Including: Through a binocular vision pulse image acquisition system, continuously acquire contact film images at the radial artery of the traditional Chinese medicine pulse training instrument, and reconstruct three-dimensional pulse signals; Select the ROI region for the reconstructed three-dimensional pulse signal to obtain a central region containing the effective components of the three-dimensional pulse signal; Using dynamic waveform slicing, segment the three-dimensional pulse signal in the central region into multiple two-dimensional slices to obtain a dynamic slice sequence, including: selecting the dimension of the pulse width as the slicing direction according to the characteristics of the three-dimensional pulse signal in the central region; adjusting the position and range of the slices according to the dynamic changes of the three-dimensional pulse signal in the pulse width and combining with the grid distribution during the reconstruction of the three-dimensional pulse signal; based on the position and range of the slices, extract two-dimensional slices from the three-dimensional pulse signal at each slice point; arrange all the extracted two-dimensional slices in the order of the slicing direction to form a dynamic slice sequence; Use a composite sine cascade modeling model to fit the two-dimensional signals with sine similarity characteristics in the dynamic slice sequence, including: constructing a composite sine cascade modeling model based on the slice characteristics in the dynamic slice sequence; solving the parameters of the composite sine cascade modeling model in combination with parameter boundary constraint conditions; using an optimization algorithm to determine the optimal characteristic parameters of each sine function among the solved parameters, minimizing the fitting error, and obtaining the best model parameters; Determine the pulse length, pulse width, and pulse height of the pulse signal according to the frequency, phase, and amplitude of the sine waveform after the fitting is completed; The composite sine cascade modeling model is: ； Among them, is the number of sine function models; is the composite sine cascade modeling model; is the total length of each two-dimensional slice pulse length divided by the number of grids; is the current sine function model, represents the amplitude of the th sine function model; represents the frequency of the th sine function model; represents the phase shift of the th sine function model; represents the vertical offset of the th sine function model.

2. The three-dimensional pulse signal quantization analysis method according to claim 1, wherein The selecting the ROI region for the reconstructed three-dimensional pulse signal to obtain a central region containing the effective components of the three-dimensional pulse signal includes: Represent the three-dimensional pulse signal using the pulse length grid length, pulse width grid length, and pulse height amplitude to obtain a three-dimensional pulse signal image; Based on the indicator function, screen the ROI region in the three-dimensional pulse signal image that contains the effective components of the three-dimensional pulse signal as the central region.

3. The three-dimensional pulse signal quantization analysis method according to claim 1, wherein The constructing a composite sine cascade modeling model based on the slice characteristics in the dynamic slice sequence includes: Find all the wave valleys of the dynamic slice sequence, perform threshold processing on the dynamic slice sequence, and extract the rising edge and falling edge; Based on the rising edge and falling edge, extract a two-dimensional slice array from the dynamic slice sequence from any current wave valley position to the next wave valley position; Based on the curve characteristics of all the two-dimensional slice arrays, respectively use a sine function model to fit and construct a composite sine cascade modeling model.

4. The three-dimensional pulse signal quantization analysis method according to claim 1, wherein Before the solving the parameters of the composite sine cascade modeling model in combination with parameter boundary constraint conditions, it further includes: Initialize each parameter to obtain the corresponding initial value; Based on the initial value, set boundary constraint conditions for each parameter.

5. The three-dimensional pulse signal quantization analysis method according to claim 1, characterized in that The using an optimization algorithm to determine the optimal characteristic parameters of each sine function among the solved parameters, minimizing the fitting error, and obtaining the best model parameters includes: Determine the objective function to be minimized during the optimization solution process; Select the YS-PSO optimization algorithm to optimize the parameters to be solved, minimize the fitting error, obtain the optimal model parameters, and organize all the optimal model parameters into a feature vector set.

6. The three-dimensional pulse signal quantization analysis method according to any one of claims 1-5, characterized in that The reconstruction of the three-dimensional pulse signal includes: Filter, binarize, and extract feature points from the collected image pulse signal, and combine the binocular vision imaging principle to reconstruct the three-dimensional pulse signal.

7. A three-dimensional pulse signal quantization analysis system, characterized in that It includes: A reconstruction module for collecting continuous contact film images at the radial artery of the traditional Chinese medicine pulse trainer through a binocular vision pulse image acquisition system and reconstructing the three-dimensional pulse signal; A selection module for selecting the ROI region of the reconstructed three-dimensional pulse signal to obtain the central region containing the effective components of the three-dimensional pulse signal; A slicing module for using dynamic waveform slicing to divide the three-dimensional pulse signal in the central region into multiple two-dimensional slices to obtain a dynamic slice sequence, including: selecting the dimension of the pulse width as the slicing direction according to the characteristics of the three-dimensional pulse signal in the central region; adjusting the position and range of the slice according to the dynamic change of the three-dimensional pulse signal in the central region in the pulse width and combining the grid distribution during the reconstruction of the three-dimensional pulse signal; based on the position and range of the slice, extracting two-dimensional slices from the three-dimensional pulse signal at each slice point; arranging all the extracted two-dimensional slices in the order of the slicing direction to form a dynamic slice sequence; A fitting module for fitting the two-dimensional signals with sinusoidal similarity characteristics in the dynamic slice sequence using a composite sine cascade modeling model, including: constructing a composite sine cascade modeling model based on the slice characteristics in the dynamic slice sequence; solving the parameters of the composite sine cascade modeling model in combination with parameter boundary constraint conditions; using an optimization algorithm to determine the optimal characteristic parameters of each sine function in the solved parameters, minimizing the fitting error, and obtaining the best model parameters; A determination module for determining the pulse length, pulse width, and pulse height of the pulse signal according to the frequency, phase, and amplitude of the sine waveform after the fitting is completed; The composite sine cascade modeling model is: ； Among them, is the number of sine function models; is the composite sine cascade modeling model; is the total length of each two-dimensional slice pulse length divided by the number of grids; is the current sine function model, represents the th amplitude of the sine function model; represents the th frequency of the sine function model; represents the th phase shift of the sine function model; represents the th vertical offset of the sine function model.

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