WO2021164349A1 - 一种基于光体积变化描记图法信号的血压预测方法和装置 - Google Patents

一种基于光体积变化描记图法信号的血压预测方法和装置 Download PDF

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WO2021164349A1
WO2021164349A1 PCT/CN2020/129636 CN2020129636W WO2021164349A1 WO 2021164349 A1 WO2021164349 A1 WO 2021164349A1 CN 2020129636 W CN2020129636 W CN 2020129636W WO 2021164349 A1 WO2021164349 A1 WO 2021164349A1
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ppg
matrix
generate
wavelet
factor
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PCT/CN2020/129636
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French (fr)
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王思翰
曹君
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乐普（北京）医疗器械股份有限公司
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/021—Measuring pressure in heart or blood vessels
    • A61B5/02108—Measuring pressure in heart or blood vessels from analysis of pulse wave characteristics
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/726—Details of waveform analysis characterised by using transforms using Wavelet transforms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor

Definitions

  • the present invention relates to the technical field of electrophysiological signal processing, in particular to a blood pressure prediction method and device based on a photoplethysmography signal.
  • the heart is the center of human blood circulation.
  • the heart produces blood pressure through regular pulsation, and then supplies blood to the whole body to complete the body's metabolism.
  • Blood pressure is one of the very important physiological signals of the human body.
  • Human blood pressure contains two important values: systolic blood pressure and diastolic blood pressure. Medically, these two quantities are used to judge whether human blood pressure is normal or not. Long-term continuous observation of these two parameters of blood pressure can help people have a clearer understanding of their own heart health.
  • most of the current traditional blood pressure measurement methods use external force upward pressure detection methods such as pressure gauges, which are not only cumbersome to operate, but also easily cause discomfort to the subject, so they cannot be used multiple times to achieve the purpose of continuous monitoring. .
  • the purpose of the present invention is to provide a blood pressure prediction method and device based on the photoplethysmography signal based on the defects of the prior art, and use the photoplethysmography (PPG) equipment to perform non-invasive data on the tester
  • the acquisition solves the problem that the monitor cannot be continuously observed in conventional monitoring; in order to fully obtain effective signal data from the PPG signal, the embodiment of the present invention uses continuous wavelet transform to decompose the PPG signal; in order to realize automatic learning and prediction capabilities
  • the embodiment of the present invention uses a convolutional neural network model with classification and regression function to predict the decomposed signal to obtain the tester’s blood pressure data (diastolic blood pressure, systolic blood pressure); through the embodiment of the present invention, the cumbersome and cumbersome methods of conventional testing methods are avoided.
  • the sense of discomfort has produced an automatic and intelligent data analysis method, so that the application side can conveniently monitor the measured object multiple times.
  • the first aspect of the embodiments of the present invention provides a blood pressure prediction method based on a photovolography signal, the method including:
  • the scaling factor array includes M scaling factors
  • the movement factor array includes N movement factors
  • the M and the N are both integers
  • the PPG signal segment is subjected to signal decomposition processing using continuous wavelet transform to generate a PPG wavelet coefficient matrix [M,N ];
  • the PPG time-frequency three-dimensional tensor [M,N,3] is reconstructed to generate a PPG convolutional three-dimensional tensor [Y,Y] using the bicubic interpolation algorithm ,3];
  • the Y is the input width threshold of the convolutional network;
  • the classification regression model of the convolutional neural network is used to perform classification regression calculation on the PPG convolution three-dimensional tensor [Y, Y, 3] to generate a PPG prediction blood pressure data pair.
  • the matrix elements of the PPG wavelet coefficient matrix [M, N] are complex wavelet coefficients
  • the value range of the matrix elements of the PPG normalization matrix [M, N] is from 0 to 1;
  • the convolutional neural network classification regression model includes: a two-dimensional convolution layer, a maximum pooling layer, a batch normalization layer, an activation layer, an addition layer, a global average pooling layer, a random discarding layer, and a fully connected layer;
  • the PPG predicted blood pressure data pair includes diastolic blood pressure data and systolic blood pressure data.
  • the acquiring the PPG signal of the photoplethysmography method, and segmenting the PPG signal to generate the PPG signal segment specifically includes:
  • the tester uses a PPG signal acquisition device to perform signal acquisition at a preset sampling frequency to generate the PPG signal; segment the PPG signal according to a preset segment duration threshold to generate multiple PPG signal segments.
  • the PPG signal segment is subjected to signal decomposition processing using a continuous wavelet transform method on the PPG signal segment according to the expansion factor of the expansion factor array, the movement factor of the movement factor array, and the wavelet base type to generate PPG wavelet coefficients Matrix [M,N], including:
  • Step 41 Construct a matrix according to the number of rows as the M and the number of columns as the N, generate a temporary PPG wavelet coefficient matrix [M, N], and initialize all matrix elements of the temporary PPG wavelet coefficient matrix [M, N] Is empty;
  • Step 42 Initialize the value of the first index to 1;
  • Step 43 Initialize the value of the second index to 1;
  • Step 44 extracting the scaling factor generating factor a corresponding to the first index from the scaling factor array, and extracting the moving factor generating factor b corresponding to the second index from the moving factor array;
  • Step 45 using the factor a and the factor b as transformation parameters, and using the continuous wavelet transformation formula corresponding to the wavelet base type to perform continuous wavelet transformation calculation on the PPG signal segment to generate wavelet coefficients WT f (a ,b);
  • the wavelet coefficients WT f (a,b) are complex numbers;
  • Step 46 Add the wavelet coefficients WT(a, b) to the temporary PPG wavelet coefficient matrix [M, N] to add data items;
  • Step 47 Add 1 to the second index
  • Step 48 Determine whether the second index is greater than the N, if the second index is greater than the N, go to step 49, and if the second index is less than or equal to the N, go to step 44;
  • Step 49 Add 1 to the first index
  • Step 50 Determine whether the first index is greater than the M, if the first index is greater than the M, go to step 51, and if the first index is less than or equal to the M, go to step 43;
  • Step 51 Set the PPG wavelet coefficient matrix [M, N] as the temporary PPG wavelet coefficient matrix [M, N].
  • the factor a and the factor b are used as transformation parameters, and a continuous wavelet transformation formula corresponding to the wavelet base type is used to perform continuous wavelet transformation calculation on the PPG signal segment to generate wavelet coefficients WT f (a,b), specifically including:
  • the selected wavelet basis expansion and translation function is Wherein, said a is said factor a; said b is said factor b; said Is a standard constant; said e is Euler's number; said H(t) is a unit step function; said t is a time variable;
  • the wavelet-based expansion and contraction translation function ⁇ a,b (t) use the formula for the PPG signal segment Perform continuous wavelet transform calculation to generate the wavelet coefficients WT f (a, b); wherein, the R is a real number; and the f(t) is the PPG signal segment.
  • the PPG wavelet coefficient matrix [M, N] is converted into a real number matrix by modulo the matrix elements, and the converted matrix is subjected to matrix element value normalization processing to generate a PPG normalized matrix [M,N], including:
  • the PPG real number matrix [M, N] performs data item addition operation; the calculation result of the wavelet coefficient modulus is a real number;
  • the RGB color disk matrix is acquired; the PPG time-frequency tensor conversion is performed on the PPG normalization matrix [M, N] according to the RGB color disk matrix to generate a PPG time-frequency three-dimensional tensor [M, N, 3] ], specifically including:
  • RGB color wheel matrix is specifically [X, 3]; the RGB color wheel matrix includes the X color vectors [3]; the X is an integer;
  • the X As the quantization level, divide the range from 0 to 1 into the X data segments; the data segment includes the data segment index and the data segment threshold range; the data segment index ranges from 1 to The X;
  • the color vector [3] is added to the PPG time-frequency three-dimensional tensor [M, N, 3].
  • the method further includes:
  • the first aspect of the embodiments of the present invention provides a blood pressure prediction method based on a photovolographic signal, using a PPG acquisition device to perform non-invasive data collection on a tester, which solves the problem that the monitor cannot be continuously observed in routine monitoring;
  • the embodiment of the present invention uses continuous wavelet transform to decompose the PPG signal; in order to realize the automatic learning and prediction capabilities, the embodiment of the present invention uses a convolutional neural network model with classification and regression function. Decompose the signal for prediction to obtain the tester's blood pressure data (diastolic blood pressure, systolic blood pressure).
  • a second aspect of the embodiments of the present invention provides a device, the device including a memory and a processor, the memory is used to store a program, and the processor is used to execute the first aspect and the methods in each implementation manner of the first aspect.
  • a third aspect of the embodiments of the present invention provides a computer program product containing instructions, which when the computer program product runs on a computer, causes the computer to execute the first aspect and the methods in the implementation manners of the first aspect.
  • a fourth aspect of the embodiments of the present invention provides a computer-readable storage medium on which a computer program is stored.
  • the computer program implements the first aspect and the methods in the first aspect when executed by a processor. .
  • FIG. 1 is a schematic diagram of a blood pressure prediction method based on photovolography signals according to Embodiment 1 of the present invention
  • FIG. 2 is a schematic diagram of a method for generating a wavelet transform time-frequency diagram of a photoplethysmography signal according to the second embodiment of the present invention
  • FIG. 3 is a schematic diagram of the equipment structure of a blood pressure prediction apparatus based on photovolography signals according to Embodiment 3 of the present invention.
  • the PPG signal is a set of signals that uses the light sensor to identify and record the change in light intensity of a specific light source.
  • the blood flow per unit area in the blood vessel changes periodically, and the corresponding blood volume also changes accordingly, resulting in a periodic change trend in the PPG signal reflecting the amount of light absorbed by the blood.
  • a cardiac cycle consists of two time periods: systolic and diastolic; during systole, the heart does work on the blood throughout the body, causing continuous and periodic changes in intravascular pressure and blood flow volume. The absorption of light is the most; when the heart is in diastole, the pressure on the blood vessels is relatively small.
  • the PPG signal waveform reflecting the light energy absorbed by the blood in the blood vessel is composed of two signals: the systolic period signal and the diastolic period signal; the common PPG signal has two peaks, the first one belongs to the systolic period, and the diastolic period signal. The latter belongs to the diastolic period.
  • a feature calculation and regression model that has been trained by batch PPG signals and corresponding measured blood pressure data can be used to obtain the blood pressure prediction value of the current PPG signal.
  • the obtained characteristic data is obtained by using the blood pressure regression calculation method to obtain the regression data as the prediction result.
  • the extraction of valid data on the one hand, we can extract the signal amplitude from the signal time domain as the characteristic value, on the other hand, we can also use the time-frequency conversion of the signal to extract the changing frequency from the signal frequency domain as the characteristic value. In the latter case, it is necessary to perform time-frequency conversion on the signal first, and then extract the eigenvalues according to the conversion result to form a characteristic matrix.
  • the conventional time-frequency conversion method for signals is through Fourier transform. But Fourier transform, because its time-frequency analysis window is a fixed size, it is easy to lose characteristic data for non-stationary signals.
  • the electrophysiological signals such as PPG signals mentioned in this article are all non-stationary signals that are susceptible to interference.
  • Wavelet transform is a kind of time-frequency analysis method, which inherits the idea of Fourier transform, and can also highlight the local characteristics of the signal from its principle.
  • the embodiment of the present invention uses one of the wavelet transforms: continuous wavelet transform to decompose the PPG signal.
  • Continuous wavelet transform provides an important means for signal locality analysis. Compared with short-time Fourier transform, continuous wavelet transform has a window adjustable property, so it has higher analysis ability for non-stationary signals;
  • the wavelet expansion and translation operation performs multi-scale refinement of the signal, which can achieve a higher time resolution in the high-frequency component of the signal, and a higher frequency resolution in the low-frequency component.
  • Continuous wavelet transform has three core parameters: wavelet base, expansion factor and movement factor. Among them, the wavelet base is a wavelet function specifically used for wavelet transformation, the expansion factor is a scale parameter that will transform itself during the wavelet transformation process, and the movement factor is a movement time parameter that will transform itself during the wavelet transformation process.
  • Fig. 1 is a schematic diagram of a blood pressure prediction method based on a photovolography signal according to Embodiment 1 of the present invention. The method mainly includes the following steps:
  • Step 1 Obtain the PPG signal of photoplethysmography, and divide it into segments to generate PPG signal segments;
  • the tester uses a PPG signal acquisition device to perform signal acquisition at a preset sampling frequency to generate the PPG signal; segment the PPG signal according to a preset segment duration threshold to generate multiple PPG signal segments.
  • Step 2 Obtain wavelet base type, scaling factor array and movement factor array
  • the stretch factor array includes M stretch factors; the movement factor array includes N movement factors; M and N are both integers.
  • Step 3 According to the expansion factor of the expansion factor array, the movement factor of the movement factor array and the wavelet base type, the PPG signal segment is processed by continuous wavelet transform to perform signal decomposition processing to generate the PPG wavelet coefficient matrix [M,N];
  • the matrix elements of the PPG wavelet coefficient matrix [M,N] are wavelet coefficients in complex form
  • Step 31 construct a matrix according to the number of rows as the M and the number of columns as the N, generate a temporary PPG wavelet coefficient matrix [M, N], and initialize the temporary PPG wavelet coefficient matrix [M, N] All matrix elements are empty;
  • Step 32 Initialize the value of the first index to 1;
  • Step 33 Initialize the value of the second index to 1;
  • Step 34 extracting the scaling factor generating factor a corresponding to the first index from the scaling factor array, and extracting the moving factor generating factor b corresponding to the second index from the moving factor array;
  • Step 35 using the factor a and the factor b as transformation parameters, and using the continuous wavelet transformation formula corresponding to the wavelet base type to perform continuous wavelet transformation calculation on the PPG signal segment to generate wavelet coefficients WT f (a ,b);
  • wavelet coefficients WT f (a, b) are complex numbers
  • step 351 when the wavelet basis type is generalized Morse wavelet, select the wavelet basis expansion and translation function as
  • Step 352 according to the wavelet base expansion and contraction translation function ⁇ a, b (t), use the formula Performing continuous wavelet transform calculation to generate the wavelet coefficient WT f (a, b);
  • the R is a real number
  • the f(t) is the PPG signal segment
  • Step 36 adding the wavelet coefficients WT(a, b) to the temporary PPG wavelet coefficient matrix [M, N];
  • Step 37 Add 1 to the second index
  • Step 38 Determine whether the second index is greater than the N, if the second index is greater than the N, go to step 39, and if the second index is less than or equal to the N, go to step 34;
  • Step 39 Add 1 to the first index
  • Step 40 Determine whether the first index is greater than the M, if the first index is greater than the M, go to step 41, and if the first index is less than or equal to the M, go to step 33;
  • Step 41 Set the PPG wavelet coefficient matrix [M, N] as the temporary PPG wavelet coefficient matrix [M, N].
  • Step 4 Perform real number matrix conversion on the PPG wavelet coefficient matrix [M, N] by taking the modulus of the matrix elements, and normalize the matrix element values of the converted matrix to generate the PPG normalization matrix [M, N ];
  • the value range of the matrix elements of the PPG normalization matrix [M,N] is from 0 to 1;
  • step 42 constructing a matrix according to the number of rows as the M and the number of columns as the N, generating a PPG real number matrix [M, N], and initializing all the matrix elements of the PPG real number matrix [M, N] as null;
  • Step 43 sequentially extract the matrix elements of the PPG wavelet coefficient matrix [M, N] to generate temporary wavelet coefficients, perform complex modulus calculation on the temporary wavelet coefficients to generate a wavelet coefficient modulus calculation result, and calculate the wavelet coefficient modulus As a result, add data items to the PPG real number matrix [M, N];
  • the calculation result of the wavelet coefficient modulus is a real number
  • Step 44 Perform normalization processing on the values of all matrix elements of the PPG real number matrix [M, N] to generate the PPG normalization matrix [M, N].
  • Step 5 Obtain the RGB color wheel matrix, and perform PPG time-frequency tensor conversion on the PPG normalized matrix [M, N] according to the RGB color wheel matrix to generate the PPG time-frequency three-dimensional tensor [M, N, 3];
  • Step 51 Obtain the RGB color wheel matrix
  • the RGB color wheel matrix is specifically [X, 3]; the RGB color wheel matrix includes the X color vectors [3]; and the X is an integer;
  • the RGB color model is a color standard in the industry. It obtains a variety of colors by changing the three color channels of red (R), green (G), and blue (B) and superimposing them with each other. Yes, this standard includes almost all colors that human vision can perceive, and is one of the most widely used color systems; assuming that the RGB color wheel matrix includes 256 color vectors, the length of each color vector is 3, including three The numerical value of each primary color; assuming that X is equal to 256, the RGB color wheel matrix includes 256 colors;
  • Step 52 Construct a matrix according to the number of rows as the M and the number of columns as the N, generate a temporary level matrix [M, N], and initialize all matrix elements of the temporary level matrix [M, N] to be empty;
  • Step 53 Using the X as the quantization level, divide 0 to 1 equally into the X data segments; the data segment includes the data segment index and the data segment threshold range; the value of the data segment index From 1 to the X;
  • Step 54 Extract the matrix elements of the PPG normalization matrix [M, N] in turn to generate the first current element, and use the value of the first current element to poll and compare the data segment threshold ranges of all data segments. When the value of the first current element is within the compared data segment threshold range, extract the currently compared data segment index to the temporary level matrix [M, N] to perform a data item addition operation;
  • X is 256
  • 256 it is divided into 256 data segments evenly from 0 to 1
  • 0-1/256 is the first segment
  • 1/256 to 2/256 is the second segment
  • 255/256 to 1 is the 256th segment
  • the value of an element is 1/257, then it belongs to the first segment, then this element
  • the level of should be 1, that is, the value of the element corresponding to this element in the temporary level matrix [M,N] is 1;
  • Step 55 Initialize all matrix elements of the PPG time-frequency three-dimensional tensor [M, N, 3] to be empty;
  • Step 56 Extract the matrix elements of the temporary level matrix [M, N] in sequence to generate a second current element, and extract the corresponding color vector from the RGB color wheel matrix using the value of the second current element as an index [3 ] Generate the current color vector [3], and add the current color vector [3] to the PPG time-frequency three-dimensional tensor [M, N, 3].
  • all element values in the temporary level matrix [M,N] are integers from 1-256. According to the value, a corresponding color point can be extracted from the RGB color wheel matrix, and then the color point can be used as a dimension supplement
  • the one-dimensional vector of the temporary level matrix [M,N] is subjected to matrix upscaling processing to generate the PPG time-frequency three-dimensional tensor [M,N,3], and the actual PPG time-frequency three-dimensional tensor [M,N,3] is derived from A three-dimensional tensor composed of M*N color points.
  • the original signal is decomposed into a two-dimensional complex matrix containing wavelet coefficients, where each row corresponds to a single expansion factor (scale factor), that is, the frequency band obtained by dividing the specified octave; Subsequently, the wavelet coefficients are quantized.
  • the specific process is to perform a modulo operation on each element of the complex matrix, and normalize the real matrix obtained by taking the modulus, and finally obtain a matrix with the value range of the element; then the matrix
  • the elements are mapped to a two-dimensional plane, mapped to a three-dimensional RGB color value through a prescribed color space, and the size of the picture is adjusted to adapt to the input of the convolutional neural network.
  • Step 6 According to the preset input width threshold of the convolutional network, use the bicubic interpolation algorithm to perform tensor shape reconstruction operation on the PPG time-frequency 3D tensor [M,N,3] to generate the PPG convolution 3D tensor [Y, Y,3];
  • Y is the input width threshold of the convolutional network.
  • the size of the PPG time-frequency three-dimensional tensor [M,N,3] is deviated from the input size requirements of the convolutional neural network classification regression model.
  • the PPG time-frequency three-dimensional tensor [M,N,3] If the size of is too small, use the bicubic interpolation algorithm to increase the intermediate value points to achieve the effect of changing the shape of the three-dimensional tensor, and finally generate a PPG convolution three-dimensional tensor [Y,Y,3] that meets the requirements.
  • Step 7 use the convolutional neural network classification regression model to perform classification regression calculation on the PPG convolution three-dimensional tensor [Y, Y, 3] to generate a PPG prediction blood pressure data pair;
  • the PPG predicted blood pressure data pair includes diastolic blood pressure data and systolic blood pressure data.
  • the convolutional network used is a customized convolutional network structure.
  • the convolutional neural network classification regression model includes: two-dimensional convolutional layer, maximum pooling layer, batch normalization layer, activation layer, and addition layer , Global average pooling layer, random discarding layer, and fully connected layer. Through the modification of the network structure, a regression model that can output diastolic and systolic blood pressure at the same time can finally be realized.
  • Fig. 2 is a schematic diagram of a wavelet transform time-frequency diagram generation method of a photovolography signal provided by the second embodiment of the present invention. The method mainly includes the following steps:
  • Step 101 the tester uses the photoplethysmography PPG signal acquisition device to perform signal acquisition at a preset sampling frequency to generate a PPG signal; segment the PPG signal according to a preset segment duration threshold to generate multiple PPG signal segments.
  • Step 102 Obtain the wavelet base type, the stretch factor array, and the movement factor array;
  • the stretch factor array includes M stretch factors; the movement factor array includes N movement factors; M and N are both integers.
  • Step 103 according to the expansion factor of the expansion factor array, the movement factor of the movement factor array, and the wavelet base type, perform signal decomposition processing on the PPG signal segment using continuous wavelet transform to generate a PPG wavelet coefficient matrix [M, N];
  • the matrix elements of the PPG wavelet coefficient matrix [M,N] are wavelet coefficients in complex form
  • Step 1031 construct a matrix according to the number of rows as M and the number of columns as N, generate a temporary PPG wavelet coefficient matrix [M, N], and initialize all matrix elements of the temporary PPG wavelet coefficient matrix [M, N] to be empty;
  • Step 1032 Initialize the value of the first index to 1;
  • Step 1033 Initialize the value of the second index to 1;
  • Step 1034 Extract the scaling factor generation factor a corresponding to the first index from the scaling factor array, and extract the movement factor generation factor b corresponding to the second index from the movement factor array;
  • Step 1035 Using factor a and factor b as transformation parameters, using the continuous wavelet transformation formula corresponding to the wavelet base type, perform continuous wavelet transformation calculation on the PPG signal segment to generate wavelet coefficients WT f (a, b); wavelet coefficients WT f (a,b) are plural;
  • the wavelet basis type is the generalized Morse wavelet
  • select the wavelet basis expansion and translation function as Among them, a is factor a; b is factor b; Is the standard constant; e is Euler's number; H(t) is the unit step function; t is the time variable;
  • wavelet base expansion and contraction function ⁇ a,b (t) use the formula for the PPG signal segment Perform continuous wavelet transform calculation to generate wavelet coefficients WT f (a, b); where R is a real number; f(t) is a PPG signal segment;
  • Step 1036 Add the wavelet coefficients WT (a, b) to the temporary PPG wavelet coefficient matrix [M, N] to add data items;
  • Step 1037 add 1 to the second index
  • Step 1038 determine whether the second index is greater than N, if the second index is greater than N, go to step 1039, if the second index is less than or equal to N, go to step 1034;
  • Step 1039 add 1 to the first index
  • Step 1040 Determine whether the first index is greater than M, if the first index is greater than M, go to step 1041, if the first index is less than or equal to M, go to step 1033;
  • Step 1041 Set the PPG wavelet coefficient matrix [M, N] as a temporary PPG wavelet coefficient matrix [M, N].
  • Step 104 Perform real number matrix conversion on the PPG wavelet coefficient matrix [M, N] by modulo the matrix elements, and perform numerical normalization processing on the converted matrix to generate a PPG normalization matrix [M, N];
  • the value range of the matrix elements of the PPG normalization matrix [M, N] is from 0 to 1.
  • Step 105 Obtain the RGB color wheel matrix, and perform PPG time-frequency tensor conversion on the PPG normalized matrix [M, N] according to the RGB color wheel matrix to generate the PPG time-frequency three-dimensional tensor [M, N, 3].
  • the RGB color model is a color standard in the industry. It obtains a variety of colors by changing the three color channels of red (R), green (G), and blue (B) and superimposing them with each other. Yes, this standard includes almost all colors that human vision can perceive, and is one of the most widely used color systems; assuming that the RGB color wheel matrix includes 256 color vectors, the length of each color vector is 3, including three The numerical value of each primary color; assuming that X is equal to 256, the RGB color wheel matrix includes 256 colors;
  • All element values in the temporary level matrix [M, N] are integers from 1-256. According to the value, a corresponding color point can be extracted from the RGB color wheel matrix, and then the color point can be used as a dimension supplementary one-dimensional
  • the vector performs matrix upgrade processing on the temporary level matrix [M,N] to generate the PPG time-frequency three-dimensional tensor [M,N,3], and the actual PPG time-frequency three-dimensional tensor [M,N,3] is M*N A three-dimensional tensor composed of four color points.
  • the original signal is decomposed into a two-dimensional complex matrix containing wavelet coefficients, where each row corresponds to a single expansion factor (scale factor), that is, the frequency band obtained by dividing the specified octave; Subsequently, the wavelet coefficients are quantized.
  • the specific process is to perform a modulo operation on each element of the complex matrix, and normalize the real matrix obtained by taking the modulus, and finally obtain a matrix with the value range of the element; then the matrix
  • the elements are mapped to a two-dimensional plane, mapped to a three-dimensional RGB color value through a prescribed color space, and the size of the picture is adjusted to adapt to the input of the convolutional neural network.
  • Step 106 Perform image conversion on the PPG time-frequency three-dimensional tensor [M, N, 3] to generate PPG time-frequency map data.
  • the bicubic interpolation algorithm can be used to add pixels between points to achieve the effect of magnifying the image.
  • the bicubic interpolation method is to expand the surrounding 4*4 pixels based on a certain original pixel.
  • the bicubic interpolation algorithm can also be used for abbreviation.
  • the PPG time-frequency three-dimensional tensor [M,N,3] is [224,128,3], indicating that the original image is a bitmap with a size of 224*128.
  • FIG. 3 is a schematic diagram of a device structure of a blood pressure prediction apparatus based on a photovolography signal according to Embodiment 3 of the present invention.
  • the device includes a processor and a memory.
  • the memory can be connected to the processor through a bus.
  • the memory may be a non-volatile memory, such as a hard disk drive and a flash memory, and a software program and a device driver program are stored in the memory.
  • the software program can execute various functions of the foregoing method provided by the embodiments of the present invention; the device driver may be a network and interface driver.
  • the processor is used to execute a software program, and when the software program is executed, the method provided in the embodiment of the present invention can be implemented.
  • the embodiment of the present invention also provides a computer-readable storage medium.
  • a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method provided in the embodiment of the present invention can be implemented.
  • the embodiment of the present invention also provides a computer program product containing instructions.
  • the processor is caused to execute the above method.
  • the embodiment of the present invention provides a blood pressure prediction method and device based on a photovolography signal, using a PPG acquisition device to perform non-invasive data collection on a tester, which solves the problem that the monitor cannot be continuously observed in routine monitoring; Fully obtain effective signal data from the PPG signal.
  • the embodiment of the present invention uses continuous wavelet transform to decompose the PPG signal; in order to achieve automatic learning and prediction capabilities, the embodiment of the present invention uses a convolutional neural network model with classification and regression function to decompose the PPG signal.
  • the signal is predicted to obtain the tester’s blood pressure data (diastolic blood pressure, systolic blood pressure); the embodiment of the present invention not only avoids the tediousness and discomfort of conventional testing methods, but also produces an automatic and intelligent data analysis method, so that The application side can conveniently carry out multiple continuous monitoring of the measured object.
  • the steps of the method or algorithm described in combination with the embodiments disclosed herein can be implemented by hardware, a software module executed by a processor, or a combination of the two.
  • the software module can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or all areas in the technical field. Any other known storage media.

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Abstract

一种基于光体积变化描记图法信号的血压预测方法和装置，包括：生成PPG信号片段（1）；根据获取的小波基类型、伸缩因子和移动因子对PPG信号片段使用连续小波变换方式进行信号分解生成PPG小波系数矩阵（3）；通过对矩阵元素取模对PPG小波系数矩阵进行转换并将转后的矩阵进行归一化处理生成PPG归一矩阵（4）；获取RGB色盘矩阵，并且根据RGB色盘矩阵对PPG归一矩阵进行张量转换生成PPG时频三维张量（5）；根据卷积网络输入宽度阈值使用双三次插值算法对PPG时频三维张量进行张量形状重构生成PPG卷积三维张量（6）；使用卷积神经网络分类回归模型对PPG卷积三维张量进行分类回归计算生成PPG预测血压数据对（7）。

Description

一种基于光体积变化描记图法信号的血压预测方法和装置
本申请要求于2020年2月21日提交中国专利局、申请号为202010110279.4、发明名称为“一种基于光体积变化描记图法信号的血压预测方法和装置”的中国专利申请的优先权。
技术领域
本发明涉及电生理信号处理技术领域，特别涉及一种基于光体积变化描记图法信号的血压预测方法和装置。
背景技术
心脏是人体血液循环的中心，心脏通过有规律的搏动产生血压，进而向全身供血完成人体的新陈代谢，血压是人体非常重要的生理信号之一。人体血压含有两个重要的数值：收缩压和舒张压，医学上通过这两个量来判断人体血压正常与否。长期持续观测血压这两项参数，可以帮助人们对自身心脏健康状态有较为清晰的认识。但是，当下大多数传统的血压测量方式均采用压力计之类的外力上压检测方式，不仅操作繁琐，且容易引起被测者的不适，因此也就不能多次地使用以达到连续监测的目的。
发明内容
本发明的目的，就是针对现有技术的缺陷，提供一种基于光体积变化描记图法信号的血压预测方法和装置,使用光体积变化描记图法(Photoplethysmography,PPG)设备对测试者进行无创数据采集解决了常规监测中不能对监测者进行持续观察的问题；为充分从PPG信号中获得有效信号数据本发明实施例采用连续小波变换的方式对PPG信号进行信号分解；为实 现自动学习与预测能力本发明实施例使用具备分类回归功能的卷积神经网络模型对分解信号进行预测得出测试者的血压数据(舒张压、收缩压)；通过本发明实施例，既避免了常规测试手段的繁琐和不适感，又产生了一种自动智能的数据分析方法，从而使得应用方可以方便地对被测对象进行多次连续监测。
为实现上述目的，本发明实施例第一方面提供了一种基于光体积变化描记图法信号的血压预测方法，所述方法包括：
获取光体积变化描记图法PPG信号，并对其进行片段划分生成PPG信号片段；
获取小波基类型、伸缩因子数组和移动因子数组；所述伸缩因子数组包括M个伸缩因子；所述移动因子数组包括N个移动因子；所述M与所述N均为整数；
根据所述伸缩因子数组的伸缩因子、所述移动因子数组的移动因子和所述小波基类型，对所述PPG信号片段使用连续小波变换方式进行信号分解处理，生成PPG小波系数矩阵[M,N]；
通过对矩阵元素取模的方式对所述PPG小波系数矩阵[M,N]进行实数矩阵转换，并将转换后的矩阵进行矩阵元素值归一化处理，生成PPG归一矩阵[M,N]；
获取RGB色盘矩阵，并且根据所述RGB色盘矩阵对所述PPG归一矩阵[M,N]进行PPG时频张量转换生成PPG时频三维张量[M,N,3]；
根据预置的卷积网络输入宽度阈值，使用双三次插值算法对所述PPG时频三维张量[M,N,3]进行张量形状重构操作生成PPG卷积三维张量[Y,Y,3]；所述Y为所述卷积网络输入宽度阈值；
使用卷积神经网络分类回归模型对所述PPG卷积三维张量[Y,Y,3]进行分类回归计算，生成PPG预测血压数据对。
优选的，
所述PPG小波系数矩阵[M,N]的矩阵元素为复数形式的小波系数；
所述PPG归一矩阵[M,N]的矩阵元素的取值范围为从0到1；
所述卷积神经网络分类回归模型包括：二维卷积层、最大池化层、批量均一化层、激活层、相加层、全局平均池化层、随机丢弃层和全连接层；
所述PPG预测血压数据对包括舒张压数据和收缩压数据。
优选的，所述获取光体积变化描记图法PPG信号，并对其进行片段划分生成PPG信号片段，具体包括：
对测试者使用PPG信号采集设备按预置的采样频率进行信号采集生成所述PPG信号；对所述PPG信号按预置的片段时长阈值进行片段划分生成多个所述PPG信号片段。
优选的，所述根据所述伸缩因子数组的伸缩因子、所述移动因子数组的移动因子和所述小波基类型，对所述PPG信号片段使用连续小波变换方式进行信号分解处理，生成PPG小波系数矩阵[M,N]，具体包括：
步骤41，按行数为所述M、列数为所述N构建矩阵，生成临时PPG小波系数矩阵[M,N]，并初始化所述临时PPG小波系数矩阵[M,N]的所有矩阵元素为空；
步骤42，初始化第一索引的值为1；
步骤43，初始化第二索引的值为1；
步骤44，从所述伸缩因子数组中提取与所述第一索引对应的伸缩因子生成因子a，从所述移动因子数组中提取与所述第二索引对应的移动因子生成因子b；
步骤45，以所述因子a和所述因子b为变换参数，使用与所述小波基类型对应的连续小波变换公式，对所述PPG信号片段进行连续小波变换计算，生成小波系数WT f(a,b)；所述小波系数WT f(a,b)为复数；
步骤46，将所述小波系数WT(a,b)向所述临时PPG小波系数矩阵[M,N]进行数据项添加操作；
步骤47，将所述第二索引加1；
步骤48，判断所述第二索引是否大于所述N，如果所述第二索引大于所 述N则转至步骤49，如果所述第二索引小于或等于所述N则转至步骤44；
步骤49，将所述第一索引加1；
步骤50，判断所述第一索引是否大于所述M，如果所述第一索引大于所述M则转至步骤51，如果所述第一索引小于或等于所述M则转至步骤43；
步骤51，设置所述PPG小波系数矩阵[M,N]为所述临时PPG小波系数矩阵[M,N]。
进一步的，所述以所述因子a和所述因子b为变换参数，使用与所述小波基类型对应的连续小波变换公式，对所述PPG信号片段进行连续小波变换计算，生成小波系数WT f(a,b)，具体包括：
当所述小波基类型为广义Morse小波时，选择小波基伸缩平移函数为
Figure PCTCN2020129636-appb-000001
其中，所述a为所述因子a；所述b为所述因子b；所述
Figure PCTCN2020129636-appb-000002
为标准常数；所述e为欧拉数；所述H(t)为单位阶跃函数；所述t为时间变量；
根据所述小波基伸缩平移函数ψ a,b(t)，对所述PPG信号片段使用公式
Figure PCTCN2020129636-appb-000003
进行连续小波变换计算生成所述小波系数WT f(a,b)；其中，所述R为实数；所述f(t)为所述PPG信号片段。
优选的，所述通过对矩阵元素取模的方式对所述PPG小波系数矩阵[M,N]进行实数矩阵转换，并将转换后的矩阵进行矩阵元素值归一化处理，生成PPG归一矩阵[M,N]，具体包括：
按行数为所述M、列数为所述N构建矩阵，生成PPG实数矩阵[M,N]，并初始化所述PPG实数矩阵[M,N]的所有矩阵元素为空；
依次提取所述PPG小波系数矩阵[M,N]的矩阵元素生成临时小波系数，对所述临时小波系数进行复数取模计算生成小波系数模计算结果，并将所述小波系数模计算结果向所述PPG实数矩阵[M,N]进行数据项添加操作；所述小波系数模计算结果为实数；
对所述PPG实数矩阵[M,N]的所有矩阵元素的数值做归一化处理，生成所述PPG归一矩阵[M,N]。
优选的，所述获取RGB色盘矩阵；根据所述RGB色盘矩阵对所述PPG归一矩阵[M,N]进行PPG时频张量转换生成PPG时频三维张量[M,N,3]，具体包括：
获取RGB色盘矩阵；所述RGB色盘矩阵具体为[X,3]；所述RGB色盘矩阵包括所述X个颜色向量[3]；所述X为整数；
按行数为所述M、列数为所述N构建矩阵，生成临时级别矩阵[M,N]，并初始化所述临时级别矩阵[M,N]的所有矩阵元素为空；
以所述X为量化级数，将0到1之间等分为所述X个数据段；所述数据段包括数据段索引和数据段阈值范围；所述数据段索引的取值从1到所述X；
依次提取所述PPG归一矩阵[M,N]矩阵元素生成第一当前元素，使用所述第一当前元素的值对所有数据段的数据段阈值范围进行轮询比对，当所述第一当前元素的值处于比对的数据段阈值范围之内时，提取当前比对的数据段索引向所述临时级别矩阵[M,N]进行数据项添加操作；
初始化所述PPG时频三维张量[M,N,3]的所有矩阵元素为空；
依次提取所述临时级别矩阵[M,N]的矩阵元素生成第二当前元素，以所述第二当前元素的值为索引从所述RGB色盘矩阵中提取对应的颜色向量[3]生成当前颜色向量[3]，并将所述当前颜色向量[3]向所述PPG时频三维张量[M,N,3]进行数据项添加操作。
优选的，所述方法还包括：
在生成所述PPG时频三维张量[M,N,3]之后，对所述PPG时频三维张量[M,N,3]进行图像转换生成PPG时频图数据。
本发明实施例第一方面提供的一种基于光体积变化描记图法信号的血压预测方法，使用PPG采集设备对测试者进行无创数据采集解决了常规监测中不能对监测者进行持续观察的问题；为充分从PPG信号中获得有效信号数据 本发明实施例采用连续小波变换的方式对PPG信号进行信号分解；为实现自动学习与预测能力本发明实施例使用具备分类回归功能的卷积神经网络模型对分解信号进行预测得出测试者的血压数据(舒张压、收缩压)。
本发明实施例第二方面提供了一种设备，该设备包括存储器和处理器，存储器用于存储程序，处理器用于执行第一方面及第一方面的各实现方式中的方法。
本发明实施例第三方面提供了一种包含指令的计算机程序产品，当计算机程序产品在计算机上运行时，使得计算机执行第一方面及第一方面的各实现方式中的方法。
本发明实施例第四方面提供了一种计算机可读存储介质，计算机可读存储介质上存储有计算机程序，计算机程序被处理器执行时实现第一方面及第一方面的各实现方式中的方法。
附图说明
图1为本发明实施例一提供的一种基于光体积变化描记图法信号的血压预测方法示意图；
图2为本发明实施例二提供的一种光体积变化描记图法信号的小波变换时频图生成方法示意图；
图3为本发明实施例三提供的一种基于光体积变化描记图法信号的血压预测的装置的设备结构示意图。
具体实施方式
为了使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明作进一步地详细描述，显然，所描述的实施例仅仅是本发明一部份实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其它实施例，都属于本发明保护的范围。
在通过实施例对本发明做进一步详细阐述之前，先就文中提及的一些技术手段做下简要说明。
PPG信号是利用光感传感器对特定光源的光强识别记录光强变化的一组信号。在心脏搏动时，对血管内单位面积的血流量形成周期性变化，与之对应的血液体积也相应发生变化，从而导致反映血液吸收光量的PPG信号也呈现周期性变化趋势。一个心动周期包括两个时间期：心脏收缩期和心脏舒张期；当心脏收缩期时，心脏对血液去全身做功，造成血管内压力与血流体积产生连续周期性变化，此时血管内血液对光线的吸收最多；当心脏舒张期时，对血管的压力相对性较小，此时上一次心脏收缩向全身推出的血液经过循环撞击心脏瓣膜从而对光线产生一定的反射与折射效应，造成舒张周期时血管内血液对光线能量的吸收降低。因此，反映血管内血液吸收光能的PPG信号波形就由两部分信号叠加而成：心脏收缩时期信号和心脏舒张时期信号；常见的PPG信号中有大小两个峰值，前一个属于心脏收缩期、后一个属于心脏舒张期。
在获取到PPG信号之后，利用一个已经由批量PPG信号与对应的实测血压数据训练完成的特征计算与回归模型，就能获得对当前PPG信号的血压预测数值。在进行血压预测时，需要先行对脉搏波数据进行有效数据提取，也就是所谓的特征提取或者特征计算。然后将获得的特征数据通过使用血压回归计算方式得到回归数据作为预测结果。在血压回归计算中的回归数据为两个：舒张压数据与收缩压数据，其中收缩压数据大于舒张压数据。
这里，关于有效数据的提取，一方面我们可以从信号时域中提取信号幅值作为特征值，另一方面也可以将信号进行时频转换从信号频域中提取变化的频率作为特征值。在后者的情况下，就需要先行对信号进行时频转换，然后根据转换结果提取特征值形成特征矩阵。常规的，对信号常规的时频转换方式是通过傅里叶变换。但傅里叶变换，因为它的时频分析窗口为固定大小，所以对于非平稳信号而言，容易丢失特征数据。本文提及的类似PPG信号之类的电生理信号，都属于易受干扰的非平稳信号。
小波变换是时频分析方法的一种，继承了傅里叶变换的思想，从其原理上也可以同时突出信号的局部特征。本发明实施例就是采用小波变换中的一种：连续小波变换方式对PPG信号进行分解。连续小波变换提供了一种对信号进行局部性分析的重要手段，与短时傅里叶变换相比，由于连续小波变换具有窗口可调性质，其对非平稳信号有较高的分析能力；通过小波的伸缩平移运算对信号进行多尺度细化，可以在信号的高频分量达到较高的时间分辨率，在低频分量则具有较高的频率分辨率的特性。连续小波变换有三个核心参数：小波基、伸缩因子和移动因子。其中，小波基是具体用于小波变换的小波函数，伸缩因子是小波变换过程中会自行变换的尺度参数，移动因子就是小波变换过程中会自行变换的移动时间参数。
如图1为本发明实施例一提供的一种基于光体积变化描记图法信号的血压预测方法示意图所示，本方法主要包括如下步骤：
步骤1，获取光体积变化描记图法PPG信号，并对其进行片段划分生成PPG信号片段；
具体包括：对测试者使用PPG信号采集设备按预置的采样频率进行信号采集生成所述PPG信号；对所述PPG信号按预置的片段时长阈值进行片段划分生成多个所述PPG信号片段。
步骤2，获取小波基类型、伸缩因子数组和移动因子数组；
其中，伸缩因子数组包括M个伸缩因子；移动因子数组包括N个移动因子；M与N均为整数。
步骤3，根据伸缩因子数组的伸缩因子、移动因子数组的移动因子和小波基类型，对PPG信号片段使用连续小波变换方式进行信号分解处理，生成PPG小波系数矩阵[M,N]；
其中，PPG小波系数矩阵[M,N]的矩阵元素为复数形式的小波系数；
具体包括：步骤31，按行数为所述M、列数为所述N构建矩阵，生成临时PPG小波系数矩阵[M,N]，并初始化所述临时PPG小波系数矩阵[M,N]的所有矩 阵元素为空；
步骤32，初始化第一索引的值为1；
步骤33，初始化第二索引的值为1；
步骤34，从所述伸缩因子数组中提取与所述第一索引对应的伸缩因子生成因子a，从所述移动因子数组中提取与所述第二索引对应的移动因子生成因子b；
步骤35，以所述因子a和所述因子b为变换参数，使用与所述小波基类型对应的连续小波变换公式，对所述PPG信号片段进行连续小波变换计算，生成小波系数WT f(a,b)；
其中，所述小波系数WT f(a,b)为复数；
具体包括：步骤351，当所述小波基类型为广义Morse小波时，选择小波基伸缩平移函数为
Figure PCTCN2020129636-appb-000004
其中，所述a为所述因子a；所述b为所述因子b；所述
Figure PCTCN2020129636-appb-000005
为标准常数；所述e为欧拉数；所述H(t)为单位阶跃函数；所述t为时间变量；
步骤352，根据所述小波基伸缩平移函数ψ a,b(t)，对所述PPG信号片段使用公式
Figure PCTCN2020129636-appb-000006
进行连续小波变换计算生成所述小波系数WT f(a,b)；
其中，所述R为实数；所述f(t)为所述PPG信号片段；
步骤36，将所述小波系数WT(a,b)向所述临时PPG小波系数矩阵[M,N]进行数据项添加操作；
步骤37，将所述第二索引加1；
步骤38，判断所述第二索引是否大于所述N，如果所述第二索引大于所述N则转至步骤39，如果所述第二索引小于或等于所述N则转至步骤34；
步骤39，将所述第一索引加1；
步骤40，判断所述第一索引是否大于所述M，如果所述第一索引大于所述M则转至步骤41，如果所述第一索引小于或等于所述M则转至步骤33；
步骤41，设置所述PPG小波系数矩阵[M,N]为所述临时PPG小波系数矩阵[M,N]。
步骤4，通过对矩阵元素取模的方式对PPG小波系数矩阵[M,N]进行实数矩阵转换，并将转后的矩阵进行矩阵元素值归一化处理，生成PPG归一矩阵[M,N]；
其中，PPG归一矩阵[M,N]的矩阵元素的取值范围为从0到1；
具体包括：步骤42，按行数为所述M、列数为所述N构建矩阵，生成PPG实数矩阵[M,N]，并初始化所述PPG实数矩阵[M,N]的所有矩阵元素为空；
步骤43，依次提取所述PPG小波系数矩阵[M,N]的矩阵元素生成临时小波系数，对所述临时小波系数进行复数取模计算生成小波系数模计算结果，并将所述小波系数模计算结果向所述PPG实数矩阵[M,N]进行数据项添加操作；
其中，所述小波系数模计算结果为实数；
步骤44，对所述PPG实数矩阵[M,N]的所有矩阵元素的数值做归一化处理，生成所述PPG归一矩阵[M,N]。
步骤5，获取RGB色盘矩阵，并且根据RGB色盘矩阵对PPG归一矩阵[M,N]进行PPG时频张量转换生成PPG时频三维张量[M,N,3]；
具体包括：步骤51，获取RGB色盘矩阵；
其中，所述RGB色盘矩阵具体为[X,3]；所述RGB色盘矩阵包括所述X个颜色向量[3]；所述X为整数；
RGB色彩模式是工业界的一种颜色标准，是通过对红(R)、绿(G)、蓝(B)三个颜色通道的变化以及它们相互之间的叠加来得到各式各样的颜色的，这个标准几乎包括了人类视力所能感知的所有颜色，是运用最广的颜色系统之一；假设RGB色盘矩阵包括256个颜色向量，则每个颜色向量的长度为3，分别包括三种基色的数值；假设X等于256，则RGB色盘矩阵包括256个颜色；
步骤52，按行数为所述M、列数为所述N构建矩阵，生成临时级别矩阵[M,N]，并初始化所述临时级别矩阵[M,N]的所有矩阵元素为空；
步骤53，以所述X为量化级数，将0到1之间等分为所述X个数据段；所述数据段包括数据段索引和数据段阈值范围；所述数据段索引的取值从1到所述X；
步骤54，依次提取所述PPG归一矩阵[M,N]矩阵元素生成第一当前元素，使用所述第一当前元素的值对所有数据段的数据段阈值范围进行轮询比对，当所述第一当前元素的值处于比对的数据段阈值范围之内时，提取当前比对的数据段索引向所述临时级别矩阵[M,N]进行数据项添加操作；
此处，假设X为256，从0到1平均划分为256段数据段，0-1/256为第一段，1/256到2/256为第二段，以此类推，255/256到1为第256段；将PPG归一矩阵[M,N]中的所有元素按取值对照数据段进行比对，假设某元素的值为1/257，那么就属于第一段，那么这个元素的级别就应该是1，即临时级别矩阵[M,N]中与这个元素对应的元素的值就为1；
步骤55，初始化所述PPG时频三维张量[M,N,3]的所有矩阵元素为空；
步骤56，依次提取所述临时级别矩阵[M,N]的矩阵元素生成第二当前元素，以所述第二当前元素的值为索引从所述RGB色盘矩阵中提取对应的颜色向量[3]生成当前颜色向量[3]，并将所述当前颜色向量[3]向所述PPG时频三维张量[M,N,3]进行数据项添加操作。
此处，临时级别矩阵[M,N]中所有的元素值都是1-256的整数，根据取值可以从RGB色盘矩阵提取出一个对应的颜色点来，再将该颜色点作为维度补充的一维向量对临时级别矩阵[M,N]进行矩阵升维处理，生成PPG时频三维张量[M,N,3]，实际PPG时频三维张量[M,N,3]就是由M*N个颜色点组成的三维张量。
在利用广义Morse小波进行连续小波变换后，原信号被分解为包含小波 系数的二维复矩阵，其中每行对应单个伸缩因子(尺度因子)，即由规定的倍频程来划分得到的频带；随后对小波系数进行量化，具体过程为对该复矩阵的每个元素进行取模运算，并将取模得到的实矩阵进行归一化，最终得到一个元素取值范围为的矩阵；接着将矩阵元素映射到二维平面中，通过规定的颜色空间映射为三维RGB颜色值，并对图片进行尺寸调整以适应卷积神经网络的输入。
步骤6，根据预置的卷积网络输入宽度阈值，使用双三次插值算法对PPG时频三维张量[M,N,3]进行张量形状重构操作生成PPG卷积三维张量[Y,Y,3]；
其中，Y为卷积网络输入宽度阈值。
此处，有可能PPG时频三维张量[M,N,3]的尺寸与卷积神经网络分类回归模型的输入尺寸要求有偏差，在当PPG时频三维张量[M,N,3]的尺寸偏小时使用双三次插值算法增加中间数值点，以达到改变三维张量形状的效果，最终生成符合要求的PPG卷积三维张量[Y,Y,3]。
步骤,7，使用卷积神经网络分类回归模型对PPG卷积三维张量[Y,Y,3]进行分类回归计算，生成PPG预测血压数据对；
其中，PPG预测血压数据对包括舒张压数据和收缩压数据。
此处，使用的卷积网络是一种定制的卷积网络结构，该卷积神经网络分类回归模型包括：二维卷积层、最大池化层、批量均一化层、激活层、相加层、全局平均池化层、随机丢弃层和全连接层，通过对网络结构的修改，最终可以实现同时输出舒张压和收缩压的回归模型。
如图2为本发明实施例二提供的一种光体积变化描记图法信号的小波变换时频图生成方法示意图所示，本方法主要包括如下步骤：
步骤101，对测试者使用光体积变化描记图法PPG信号采集设备按预置的采样频率进行信号采集生成PPG信号；对PPG信号按预置的片段时长阈值进行片段划分生成多个PPG信号片段。
步骤102，获取小波基类型、伸缩因子数组和移动因子数组；
其中，伸缩因子数组包括M个伸缩因子；移动因子数组包括N个移动因子；M与N均为整数。
步骤103，根据伸缩因子数组的伸缩因子、移动因子数组的移动因子和小波基类型，对PPG信号片段使用连续小波变换方式进行信号分解处理，生成PPG小波系数矩阵[M,N]；
其中，PPG小波系数矩阵[M,N]的矩阵元素为复数形式的小波系数；
具体包括：步骤1031，按行数为M、列数为N构建矩阵，生成临时PPG小波系数矩阵[M,N]，并初始化临时PPG小波系数矩阵[M,N]的所有矩阵元素为空；
步骤1032，初始化第一索引的值为1；
步骤1033，初始化第二索引的值为1；
步骤1034，从伸缩因子数组中提取与第一索引对应的伸缩因子生成因子a，从移动因子数组中提取与第二索引对应的移动因子生成因子b；
步骤1035，以因子a和因子b为变换参数，使用与小波基类型对应的连续小波变换公式，对PPG信号片段进行连续小波变换计算，生成小波系数WT f(a,b)；小波系数WT f(a,b)为复数；
此处，具体的：当小波基类型为广义Morse小波时，选择小波基伸缩平移函数为
Figure PCTCN2020129636-appb-000007
其中，a为因子a；b为因子b；
Figure PCTCN2020129636-appb-000008
为标准常数；e为欧拉数；H(t)为单位阶跃函数；t为时间变量；
根据小波基伸缩平移函数ψ a,b(t)，对PPG信号片段使用公式
Figure PCTCN2020129636-appb-000009
Figure PCTCN2020129636-appb-000010
进行连续小波变换计算生成小波系数WT f(a,b)；其中，R为实数；f(t)为PPG信号片段；
步骤1036，将小波系数WT(a,b)向临时PPG小波系数矩阵[M,N]进行数据项添加操作；
步骤1037，将第二索引加1；
步骤1038，判断第二索引是否大于N，如果第二索引大于N则转至步骤1039，如果第二索引小于或等于N则转至步骤1034；
步骤1039，将第一索引加1；
步骤1040，判断第一索引是否大于M，如果第一索引大于M则转至步骤1041，如果第一索引小于或等于M则转至步骤1033；
步骤1041，设置PPG小波系数矩阵[M,N]为临时PPG小波系数矩阵[M,N]。
步骤104，对PPG小波系数矩阵[M,N]以对矩阵元素取模的方式进行实数矩阵转换，并将转后的矩阵进行数值归一化处理，生成PPG归一矩阵[M,N]；
其中，PPG归一矩阵[M,N]的矩阵元素的取值范围为从0到1。
步骤105，获取RGB色盘矩阵，并且根据RGB色盘矩阵对PPG归一矩阵[M,N]进行PPG时频张量转换生成PPG时频三维张量[M,N,3]。
RGB色彩模式是工业界的一种颜色标准，是通过对红(R)、绿(G)、蓝(B)三个颜色通道的变化以及它们相互之间的叠加来得到各式各样的颜色的，这个标准几乎包括了人类视力所能感知的所有颜色，是运用最广的颜色系统之一；假设RGB色盘矩阵包括256个颜色向量，则每个颜色向量的长度为3，分别包括三种基色的数值；假设X等于256，则RGB色盘矩阵包括256个颜色；
临时级别矩阵[M,N]中所有的元素值都是1-256的整数，根据取值可以从RGB色盘矩阵提取出一个对应的颜色点来，再将该颜色点作为维度补充的一维向量对临时级别矩阵[M,N]进行矩阵升维处理，生成PPG时频三维张量[M,N,3]，实际PPG时频三维张量[M,N,3]就是由M*N个颜色点组成的三维张量。
在利用广义Morse小波进行连续小波变换后，原信号被分解为包含小波系数的二维复矩阵，其中每行对应单个伸缩因子(尺度因子)，即由规定的倍频程来划分得到的频带；随后对小波系数进行量化，具体过程为对该复矩阵的每个元素进行取模运算，并将取模得到的实矩阵进行归一化，最终得到一个元素取值范围为的矩阵；接着将矩阵元素映射到二维平面中，通过规定的颜色 空间映射为三维RGB颜色值，并对图片进行尺寸调整以适应卷积神经网络的输入。
步骤106，对PPG时频三维张量[M,N,3]进行图像转换生成PPG时频图数据。
此处如果PPG时频三维张量[M,N,3]作为图像数据量不够大的话，可以使用双三次插值算法在点与点之间增加像素点从而实现将图像放大的作用。这里，在图像放大时双三次插值法是对基于某个原始像素点扩张周围的4*4个像素点。同理，如果PPG时频三维张量[M,N,3]作为图像数据量足够大了需要缩小时，也可以使用双三次插值算法进行缩写。例如PPG时频三维张量[M,N,3]为[224,128,3]，表明原始图像为一个224*128大小的位图，通过双三次插值法我们可以将图形发大或者调整到448*256或者224*224大小。
如图3为本发明实施例三提供的一种基于光体积变化描记图法信号的血压预测的装置的设备结构示意图所示，该设备包括：处理器和存储器。存储器可通过总线与处理器连接。存储器可以是非易失存储器，例如硬盘驱动器和闪存，存储器中存储有软件程序和设备驱动程序。软件程序能够执行本发明实施例提供的上述方法的各种功能；设备驱动程序可以是网络和接口驱动程序。处理器用于执行软件程序，该软件程序被执行时，能够实现本发明实施例提供的方法。
需要说明的是，本发明实施例还提供了一种计算机可读存储介质。该计算机可读存储介质上存储有计算机程序，该计算机程序被处理器执行时，能够实现本发明实施例提供的方法。
本发明实施例还提供了一种包含指令的计算机程序产品。当该计算机程序产品在计算机上运行时，使得处理器执行上述方法。
本发明实施例提供的一种基于光体积变化描记图法信号的血压预测方法和装置，使用PPG采集设备对测试者进行无创数据采集解决了常规监测中不能对监测者进行持续观察的问题；为充分从PPG信号中获得有效信号数据 本发明实施例采用连续小波变换的方式对PPG信号进行信号分解；为实现自动学习与预测能力本发明实施例使用具备分类回归功能的卷积神经网络模型对分解信号进行预测得出测试者的血压数据(舒张压、收缩压)；通过本发明实施例，既避免了常规测试手段的繁琐和不适感，又产生了一种自动智能的数据分析方法，从而使得应用方可以方便地对被测对象进行多次连续监测。
专业人员应该还可以进一步意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、计算机软件或者二者的结合来实现，为了清楚地说明硬件和软件的可互换性，在上述说明中已经按照功能一般性地描述了各示例的组成及步骤。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。
结合本文中所公开的实施例描述的方法或算法的步骤可以用硬件、处理器执行的软件模块，或者二者的结合来实施。软件模块可以置于随机存储器(RAM)、内存、只读存储器(ROM)、电可编程ROM、电可擦除可编程ROM、寄存器、硬盘、可移动磁盘、CD-ROM、或技术领域内所公知的任意其它形式的存储介质中。
以上所述的具体实施方式，对本发明的目的、技术方案和有益效果进行了进一步详细说明，所应理解的是，以上所述仅为本发明的具体实施方式而已，并不用于限定本发明的保护范围，凡在本发明的精神和原则之内，所做的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

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  1. 一种基于光体积变化描记图法信号的血压预测方法，其特征在于，所述方法包括：
    获取光体积变化描记图法PPG信号，并对其进行片段划分生成PPG信号片段；
    获取小波基类型、伸缩因子数组和移动因子数组；所述伸缩因子数组包括M个伸缩因子；所述移动因子数组包括N个移动因子；所述M与所述N均为整数；
    根据所述伸缩因子数组的伸缩因子、所述移动因子数组的移动因子和所述小波基类型，对所述PPG信号片段使用连续小波变换方式进行信号分解处理，生成PPG小波系数矩阵[M,N]；
    通过对矩阵元素取模的方式对所述PPG小波系数矩阵[M,N]进行实数矩阵转换，并将转换后的矩阵进行矩阵元素值归一化处理，生成PPG归一矩阵[M,N]；
    获取RGB色盘矩阵，并且根据所述RGB色盘矩阵对所述PPG归一矩阵[M,N]进行PPG时频张量转换生成PPG时频三维张量[M,N,3]；
    根据预置的卷积网络输入宽度阈值，使用双三次插值算法对所述PPG时频三维张量[M,N,3]进行张量形状重构操作生成PPG卷积三维张量[Y,Y,3]；所述Y为所述卷积网络输入宽度阈值；
    使用卷积神经网络分类回归模型对所述PPG卷积三维张量[Y,Y,3]进行分类回归计算，生成PPG预测血压数据对。
  2. 根据权利要求1所述的基于光体积变化描记图法信号的血压预测方法，其特征在于，
    所述PPG小波系数矩阵[M,N]的矩阵元素为复数形式的小波系数；
    所述PPG归一矩阵[M,N]的矩阵元素的取值范围为从0到1；
    所述卷积神经网络分类回归模型包括：二维卷积层、最大池化层、批量均一化层、激活层、相加层、全局平均池化层、随机丢弃层和全连接层；
    所述PPG预测血压数据对包括舒张压数据和收缩压数据。
  3. 根据权利要求1所述的基于光体积变化描记图法信号的血压预测方法，其特征在于，所述获取光体积变化描记图法PPG信号，并对其进行片段划分生成PPG信号片段，具体包括：
    对测试者使用PPG信号采集设备按预置的采样频率进行信号采集生成所述PPG信号；对所述PPG信号按预置的片段时长阈值进行片段划分生成多个所述PPG信号片段。
  4. 根据权利要求1所述的基于光体积变化描记图法信号的血压预测方法，其特征在于，所述根据所述伸缩因子数组的伸缩因子、所述移动因子数组的移动因子和所述小波基类型，对所述PPG信号片段使用连续小波变换方式进行信号分解处理，生成PPG小波系数矩阵[M,N]，具体包括：
    步骤41，按行数为所述M、列数为所述N构建矩阵，生成临时PPG小波系数矩阵[M,N]，并初始化所述临时PPG小波系数矩阵[M,N]的所有矩阵元素为空；
    步骤42，初始化第一索引的值为1；
    步骤43，初始化第二索引的值为1；
    步骤44，从所述伸缩因子数组中提取与所述第一索引对应的伸缩因子生成因子a，从所述移动因子数组中提取与所述第二索引对应的移动因子生成因子b；
    步骤45，以所述因子a和所述因子b为变换参数，使用与所述小波基类型对应的连续小波变换公式，对所述PPG信号片段进行连续小波变换计算，生成小波系数WT f(a,b)；所述小波系数WT f(a,b)为复数；
    步骤46，将所述小波系数WT(a,b)向所述临时PPG小波系数矩阵[M,N]进行数据项添加操作；
    步骤47，将所述第二索引加1；
    步骤48，判断所述第二索引是否大于所述N，如果所述第二索引大于所 述N则转至步骤49，如果所述第二索引小于或等于所述N则转至步骤44；
    步骤49，将所述第一索引加1；
    步骤50，判断所述第一索引是否大于所述M，如果所述第一索引大于所述M则转至步骤51，如果所述第一索引小于或等于所述M则转至步骤43；
    步骤51，设置所述PPG小波系数矩阵[M,N]为所述临时PPG小波系数矩阵[M,N]。
  5. 根据权利要求4所述的基于光体积变化描记图法信号的血压预测方法，其特征在于，所述以所述因子a和所述因子b为变换参数，使用与所述小波基类型对应的连续小波变换公式，对所述PPG信号片段进行连续小波变换计算，生成小波系数WT f(a,b)，具体包括：
    当所述小波基类型为广义Morse小波时，选择小波基伸缩平移函数为
    Figure PCTCN2020129636-appb-100001
    其中，所述a为所述因子a；所述b为所述因子b；所述
    Figure PCTCN2020129636-appb-100002
    为标准常数；所述e为欧拉数；所述H(t)为单位阶跃函数；所述t为时间变量；
    根据所述小波基伸缩平移函数ψ a,b(t)，对所述PPG信号片段使用公式
    Figure PCTCN2020129636-appb-100003
    进行连续小波变换计算生成所述小波系数WT f(a,b)；其中，所述R为实数；所述f(t)为所述PPG信号片段。
  6. 根据权利要求1所述的基于光体积变化描记图法信号的血压预测方法，其特征在于，所述通过对矩阵元素取模的方式对所述PPG小波系数矩阵[M,N]进行实数矩阵转换，并将转换后的矩阵进行矩阵元素值归一化处理，生成PPG归一矩阵[M,N]，具体包括：
    按行数为所述M、列数为所述N构建矩阵，生成PPG实数矩阵[M,N]，并初始化所述PPG实数矩阵[M,N]的所有矩阵元素为空；
    依次提取所述PPG小波系数矩阵[M,N]的矩阵元素生成临时小波系数，对所述临时小波系数进行复数取模计算生成小波系数模计算结果，并将所述小 波系数模计算结果向所述PPG实数矩阵[M,N]进行数据项添加操作；所述小波系数模计算结果为实数；
    对所述PPG实数矩阵[M,N]的所有矩阵元素的数值做归一化处理，生成所述PPG归一矩阵[M,N]。
  7. 根据权利要求1所述的基于光体积变化描记图法信号的血压预测方法，其特征在于，所述获取RGB色盘矩阵；根据所述RGB色盘矩阵对所述PPG归一矩阵[M,N]进行PPG时频张量转换生成PPG时频三维张量[M,N,3]，具体包括：
    获取RGB色盘矩阵；所述RGB色盘矩阵具体为[X,3]；所述RGB色盘矩阵包括所述X个颜色向量[3]；所述X为整数；
    按行数为所述M、列数为所述N构建矩阵，生成临时级别矩阵[M,N]，并初始化所述临时级别矩阵[M,N]的所有矩阵元素为空；
    以所述X为量化级数，将0到1之间等分为所述X个数据段；所述数据段包括数据段索引和数据段阈值范围；所述数据段索引的取值从1到所述X；
    依次提取所述PPG归一矩阵[M,N]矩阵元素生成第一当前元素，使用所述第一当前元素的值对所有数据段的数据段阈值范围进行轮询比对，当所述第一当前元素的值处于比对的数据段阈值范围之内时，提取当前比对的数据段索引向所述临时级别矩阵[M,N]进行数据项添加操作；
    初始化所述PPG时频三维张量[M,N,3]的所有矩阵元素为空；
    依次提取所述临时级别矩阵[M,N]的矩阵元素生成第二当前元素，以所述第二当前元素的值为索引从所述RGB色盘矩阵中提取对应的颜色向量[3]生成当前颜色向量[3]，并将所述当前颜色向量[3]向所述PPG时频三维张量[M,N,3]进行数据项添加操作。
  8. 根据权利要求1所述的基于光体积变化描记图法信号的血压预测方法，其特征在于，所述方法还包括：
    在生成所述PPG时频三维张量[M,N,3]之后，对所述PPG时频三维张量 [M,N,3]进行图像转换生成PPG时频图数据。
  9. 一种设备，包括存储器和处理器，其特征在于，所述存储器用于存储程序，所述处理器用于执行如权利要求1至8任一项所述的方法。
  10. 一种包含指令的计算机程序产品，当其在计算机上运行时，使得计算机执行如权利要求1至8任一项所述的方法。
  11. 一种计算机可读存储介质，包括指令，当所述指令在计算机上运行时，使所述计算机执行根据权利要求1至8任一项所述的方法。
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