WO2021184801A1 - Procédé et appareil de prédiction de pression artérielle sur la base d'un signal de synchronisation - Google Patents

Procédé et appareil de prédiction de pression artérielle sur la base d'un signal de synchronisation Download PDF

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WO2021184801A1
WO2021184801A1 PCT/CN2020/129642 CN2020129642W WO2021184801A1 WO 2021184801 A1 WO2021184801 A1 WO 2021184801A1 CN 2020129642 W CN2020129642 W CN 2020129642W WO 2021184801 A1 WO2021184801 A1 WO 2021184801A1
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data
dimensional
blood pressure
sequence
ppg
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张碧莹
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
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A61B5/352—Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
    • 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
    • A61B5/7246—Details of waveform analysis using correlation, e.g. template matching or determination of similarity
    • 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
    • A61B5/7285—Specific aspects of physiological measurement analysis for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal

Definitions

  • the present invention relates to the technical field of electrophysiological signal processing, in particular to a method and device for blood pressure prediction based on a synchronization 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 method and device for blood pressure prediction based on synchronization signals in view of the shortcomings of the prior art, by using non-invasive portable signal acquisition equipment to perform non-invasive electrocardiogram (ECG) signals and light volume on the tester Synchronous acquisition of Photoplethysmography (PPG) signals, and data fusion processing of the acquired ECG signals and PPG signals, and then through the blood pressure convolutional neural network (Convolutional Neural Network, CNN) model and blood pressure artificial neural network (
  • the blood pressure prediction model composed of Artificial Neural Network (ANN) model performs feature calculation and blood pressure data regression calculation on the fusion data to calculate the blood pressure data of the tester, and finally outputs the specific mean blood pressure prediction data pair (systolic blood pressure prediction) through the selection of the prediction mode identifier Data, diastolic blood pressure prediction data) is still a one-dimensional data sequence of ambulatory blood pressure prediction; through the embodiment of the present invention, the cumbersomeness and discomfort of conventional blood pressure collection methods are avoided, and
  • the first aspect of the embodiments of the present invention provides a method for blood pressure prediction based on a synchronization signal, and the method includes:
  • the blood pressure convolutional neural network CNN model perform blood pressure CNN input data fusion processing on the PPG one-dimensional data sequence and the ECG one-dimensional data sequence to generate a four-dimensional tensor of input data;
  • the prediction mode identifier includes two identifiers of mean prediction and dynamic prediction;
  • the average blood pressure calculation operation is performed on the two-dimensional matrix of blood pressure regression data to generate an average blood pressure prediction data pair;
  • the average blood pressure prediction data pair includes diastolic blood pressure prediction data and systolic blood pressure prediction data.
  • the prediction mode identifier is the dynamic prediction, perform an ambulatory blood pressure data extraction operation on the blood pressure regression data two-dimensional matrix to generate an ambulatory blood pressure prediction one-dimensional data sequence.
  • the PPG signal data is generated by using a non-invasive PPG signal acquisition device to perform a preset light source signal acquisition operation on the tester's local skin surface within a signal acquisition time threshold;
  • the ECG signal data is synchronized with the collection of the PPG signal data, and is generated by using a non-invasive ECG signal collection device to perform an ECG physiological signal collection operation on the tester within the signal collection time threshold;
  • the PPG one-dimensional data sequence is specifically a PPG one-dimensional data sequence [A]; the PPG one-dimensional data sequence [A] includes the A pieces of PPG data; and the A is the data sampling frequency threshold multiplied by the The product of the signal acquisition time threshold;
  • the ECG one-dimensional data sequence is specifically an ECG one-dimensional data sequence [A]; the ECG one-dimensional data sequence [A] includes the A pieces of ECG data.
  • the input data length threshold N of the blood pressure convolutional neural network CNN model perform blood pressure CNN input data fusion processing on the PPG one-dimensional data sequence and the ECG one-dimensional data sequence to generate a four-dimensional tensor of input data , Specifically including:
  • the PPG one-dimensional data sequence [A] is subjected to sequential data fragment division processing to generate a PPG fragment data two-dimensional matrix [M, N], and the ECG one Dimensional data sequence [A] performs sequential data segmentation processing to generate a two-dimensional matrix of ECG segment data [M,N];
  • the B 1 is the fourth dimension parameter of the input data four-dimensional tensor [B 1 , H 1 , W 1 , C 1 ], and the B 1 is the total number of fragments M;
  • the H 1 is the The input data four-dimensional tensor [B 1 , H 1 , W 1 , C 1 ] is the third-dimensional parameter, and the value of H 1 is 2;
  • the W 1 is the input data four-dimensional tensor [B 1 , H 1 , W 1 , C 1 ] second dimension parameter, and the W 1 is the input data length threshold N;
  • the C 1 is the input data four-dimensional tensor [B 1 , H 1 , W 1 , C 1 ] is the first dimension parameter, and the value of C
  • the PPG one-dimensional data sequence [A] is divided into sequential data fragments to generate a PPG fragment data two-dimensional matrix [M, N], Perform sequential data segment division processing on the ECG one-dimensional data sequence [A] to generate a two-dimensional ECG segment data matrix [M, N], which specifically includes:
  • the PPG one-dimensional temporary data sequence [L] is subjected to continuous data segment division processing; the PPG one-dimensional temporary data sequence [L] includes the total number of segments M PPG one One-dimensional segment data sequence [N]; the PPG one-dimensional segment data sequence [N] includes the input data length threshold N pieces of the PPG data;
  • ECG one-dimensional data sequence [A] taking the first data as the starting data extraction position and the temporary data length L as the data extraction length, extract a piece of continuous data to generate an ECG one-dimensional temporary data sequence [L ]; and according to the input data length threshold N, the ECG one-dimensional temporary data sequence [L] is subjected to continuous data segment division processing; the ECG one-dimensional temporary data sequence [L] includes the total number of segments M ECG one One-dimensional fragment data sequence [N]; the ECG one-dimensional fragment data sequence [N] includes the input data length threshold value N of the ECG data;
  • the input data fusion processing is performed on the two-dimensional matrix of PPG segment data [M, N] and the two-dimensional matrix of ECG segment data [M, N] to generate a two-dimensional matrix of fused segment data [M, 2*N], including:
  • Step 51 Obtain a preset fusion sorting flag; construct the two-dimensional matrix of fusion fragment data [M, 2*N], and initialize the two-dimensional matrix of fusion fragment data [M, 2*N] to be empty; construct the first A sequence [2*N]; the fusion sequence identifier is one of PPG+ECG sequence and ECG+PPG sequence; the first sequence [2*N] includes 2*N sequence data;
  • Step 52 Initialize the value of the first index to 1, and initialize the value of the first total to the total number of fragments M;
  • Step 53 Taking the product of the difference of the first index minus 1 and the input data length threshold N plus 1 as the starting data extraction position, and the input data length threshold N as the extraction data length. Extract a piece of continuous data from the two-dimensional matrix of PPG fragment data [M, N] to generate a second sequence [N], and extract a piece of continuous data from the two-dimensional matrix of ECG fragment data [M, N] to generate a third sequence [ N];
  • Step 54 Use the second sequence [N] and the third sequence [N] to perform value assignment processing on the first sequence [2*N] according to the fusion order identifier; when the fusion order identifier is When the PPG+ECG is sorted, use the second sequence [N] to perform the assignment processing on the first N sequence data included in the first sequence [2*N], and use the third sequence [N ] Perform value assignment processing on the last N sequence data included in the first sequence [2*N]; when the fusion sequence identifier is the ECG+PPG sequence, use the third sequence [N] pair The first N pieces of the sequence data included in the first sequence [2*N] are assigned values, and the second sequence [N] is used to perform the assignment processing on the last N pieces of data included in the first sequence [2*N]. Assign value to the sequence data;
  • Step 55 Use the first sequence [2*N] to perform a sequence data addition operation on the two-dimensional matrix [M, 2*N] of fusion fragment data;
  • Step 56 Add 1 to the first index
  • Step 57 Determine whether the first index is greater than the first total, if the first index is greater than the first total, go to step 58, if the first index is less than or equal to the first total, go to Step 53;
  • Step 58 Return the two-dimensional matrix [M, 2*N] of the fused segment data to the upper application.
  • the four-dimensional tensor conversion process of blood pressure CNN input data is performed on the two-dimensional matrix [M, 2*N] of the fused segment data to generate the four-dimensional tensor of the input data [B 1 , H 1 , W 1 ,C 1 ], specifically including:
  • the blood pressure CNN model is used to perform multi-layer convolution pooling calculation on the four-dimensional tensor of the input data to generate a four-dimensional tensor of characteristic data, which specifically includes:
  • Step 71 Initialize the value of the second index to 1; initialize the second total to the threshold of the number of convolutional layers; initialize the temporary four-dimensional tensor of the second index to the four-dimensional tensor of the input data [B 1 , H 1 , W 1 ,C 1 ];
  • Step 72 Use the second index layer convolution layer of the blood pressure CNN model to perform convolution calculation processing on the second index temporary four-dimensional tensor to generate a second index convolution output data four-dimensional tensor; use the blood pressure
  • the second index pooling layer of the CNN model performs pooling calculation processing on the four-dimensional tensor of the second index convolution output data to generate the four-dimensional tensor of the second index pooling output data;
  • the blood pressure CNN model includes multiple layers. Said convolutional layer and multiple layers of said pooling layer;
  • Step 73 Set the temporary four-dimensional tensor of the second index as the four-dimensional tensor of the second index pooling output data
  • Step 74 Add 1 to the second index
  • Step 75 Determine whether the second index is greater than the second total, if the second index is greater than the second total, go to step 76, if the second index is less than or equal to the second total, go to Step 72;
  • Step 76 Set the feature data four-dimensional tensor as the second index temporary four-dimensional tensor; the feature data four-dimensional tensor is specifically the feature data four-dimensional tensor [B 2 , H 2 , W 2 , C 2 ];
  • the B2 is the fourth dimension parameter of the characteristic data four-dimensional tensor [B 2 , H 2 , W 2 , C 2 ] and the B 2 is the B 1 ;
  • the H 2 is the characteristic data four-dimensional
  • the W 2 is the second dimension of the feature data four-dimensional tensor [B 2 , H 2 , W 2 , C 2 ] Parameters;
  • the C 2 is the first dimension parameter of the characteristic data four-dimensional tensor [B 2 , H 2 , W 2 , C 2 ].
  • the two-dimensional matrix construction operation of the input data of the blood pressure artificial neural network ANN model is performed according to the four-dimensional tensor of the characteristic data to generate a two-dimensional matrix of input data; and the two-dimensional matrix of input data is calculated by the blood pressure ANN model Perform feature data regression calculation to generate a two-dimensional matrix of blood pressure regression data, which specifically includes:
  • the feature data four-dimensional tensor [B 2 , H 2 , W 2 , C 2 ] perform tensor data dimensionality reduction processing on the feature data four-dimensional tensor [B 2 , H 2 , W 2 , C 2] Construct the input data two-dimensional matrix;
  • the input data two-dimensional matrix is specifically the input data two-dimensional matrix [W 3 , C 3 ];
  • the W 3 is the input data two-dimensional matrix [W 3 , C 3 ]
  • the second dimension parameter of the W 3 is the B 2 ;
  • the C 3 is the first dimension parameter of the input data two-dimensional matrix [W 3 , C 3 ] and the C 3 is the H 2 Multiply by the product of W 2 and then by the product of C 2;
  • the blood pressure regression data two-dimensional matrix is specifically blood pressure regression data two-dimensional Matrix [X, 2];
  • the X is the second dimension parameter of the blood pressure regression data two-dimensional matrix [X, 2] and the X is the W 3 ;
  • the blood pressure regression data two-dimensional matrix [X, 2] includes the X regression data one-dimensional data sequence [2];
  • the regression data one-dimensional data sequence [2] includes the fragment systolic blood pressure data and the fragment diastolic blood pressure data.
  • performing an average blood pressure calculation operation on the two-dimensional matrix of blood pressure regression data to generate an average blood pressure prediction data pair specifically includes:
  • the average blood pressure prediction data pair is set; and the diastolic blood pressure prediction data of the average blood pressure prediction data pair is initialized to be empty, and the average blood pressure prediction data pair is initialized The systolic blood pressure data of is empty;
  • the sum of the diastolic blood pressure data of all the pieces of the regression data one-dimensional data sequence [2] included in the blood pressure regression data two-dimensional matrix [X, 2] is calculated to generate the sum of diastolic blood pressure, which is divided by the sum of diastolic blood pressure Use the quotient of X to generate the first mean value; generate the sum of the systolic blood pressure data of all the regression data one-dimensional data sequence [2] included in the blood pressure regression data two-dimensional matrix [X, 2]
  • the sum of systolic blood pressures, the second mean value is generated according to the quotient of the sum of systolic blood pressures divided by the X;
  • performing an ambulatory blood pressure data extraction operation on the two-dimensional matrix of blood pressure regression data to generate a one-dimensional ambulatory blood pressure prediction data sequence specifically includes:
  • the prediction mode identifier is the dynamic prediction
  • the one-dimensional data sequence [2] of the regression data included in the two-dimensional matrix [X, 2] of the blood pressure regression data is sequentially extracted to generate the current data sequence [2]; the systolic blood pressure data of the blood pressure data group is set to all The segment systolic blood pressure data of the current data sequence [2], the diastolic blood pressure data of the blood pressure data group is set to the segment diastolic blood pressure data of the current data sequence [2]; and the blood pressure data The group performs a data group addition operation to the ambulatory blood pressure prediction one-dimensional data sequence.
  • the first aspect of the embodiments of the present invention provides a method for blood pressure prediction based on synchronization signals, which performs feature data fusion on the synchronized ECG signal and PPG signal, and then uses a blood pressure prediction model composed of a blood pressure CNN model and a blood pressure ANN model to fuse the data Perform feature calculation and blood pressure data regression calculation to calculate the tester's blood pressure data, and finally output the mean blood pressure prediction data pair (systolic blood pressure prediction data, diastolic blood pressure prediction data) or ambulatory blood pressure prediction one-dimensional data through the selection of the prediction mode identifier sequence.
  • 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 method for blood pressure prediction based on a synchronization signal according to Embodiment 1 of the present invention
  • FIG. 2 is a schematic diagram of a method for blood pressure prediction based on a synchronization signal according to Embodiment 2 of the present invention
  • FIG. 3 is a schematic diagram of the device structure of an apparatus for blood pressure prediction based on a synchronization signal 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.
  • the blood pressure can be predicted directly by using the PPG signal.
  • the ECG signal is a group of electrophysiological signals of the heart's cardiac cycle collected from body surface records using an electrocardiographic signal acquisition device.
  • the ECG signal used in the embodiment of the present invention is a group of single-lead ECG signals. Because the ECG signal reflects the activity of the heart, and the PPG reflects the blood turbulence, this blood turbulence is closely related to each contraction of the heart: there is a pulse propagation time between the PPG signal cycle and the ECG signal cycle (Pulse Translation Time, PTT) time difference. After years of research, it has been found that under certain conditions, there is a linear relationship between PTT and arterial blood pressure.
  • the blood pressure prediction models mentioned in the embodiment of the present invention include a blood pressure CNN model and a blood pressure ANN model.
  • the former is responsible for feature calculation and the latter is responsible for regression calculation.
  • the latter is responsible for regression calculation.
  • the ratio between the two is 1: 1 (Complete the fusion processing of input data through blood pressure CNN).
  • CNN has long been one of the core algorithms in the field of feature recognition. Used in image recognition, it can be used in fine classification and recognition to extract the discriminative features of the image for learning by other classifiers.
  • the blood pressure feature extraction calculation is performed on the input data: after the input data is convolved and pooled, the feature information related to blood pressure is retained for other networks to learn.
  • the blood pressure CNN model mentioned in the article is a CNN model that has been trained through blood pressure feature extraction. It is specifically composed of a convolutional layer and a pooling layer. The convolutional layer is responsible for performing blood pressure feature extraction calculations on the input data of the CNN model.
  • the pooling layer is to down-sample the extraction results of the convolutional layer; the blood pressure CNN model in this article is divided into multiple CNN network layers, and each CNN network layer includes a convolutional layer and a pooling layer.
  • the input data and output data format of the blood pressure CNN model are both in the form of a 4-dimensional tensor: [B, H, W, C]. After each layer of convolutional layer or pooling layer, the value of some dimensional parameters of the output data will change, that is, the total data length of the tensor will be shortened.
  • the characteristic of the change is: B as the fourth dimension parameter (PPG The total number of fragments of one-dimensional data sequence or ECG one-dimensional data sequence) will not change; H and W are the third and two-dimensional parameters in four dimensions.
  • the setting of the step size is also related to the pooling window size and sliding step size of the pooling layer;
  • C is the first dimension parameter in the four-dimensional, and its change is related to the selected output space dimension in the convolutional layer (volume The number of product cores) is related.
  • ANN refers to a complex network structure formed by interconnecting a large number of processing units (neurons). It is a certain abstraction, simplification and simulation of the human brain tissue structure and operating mechanism. ANN simulates neuron activity with mathematical models. It is an information processing system based on imitating the structure and function of the brain's neural network. A common application of ANN is to perform classification and regression calculations on data.
  • the blood pressure ANN model mentioned in the article is an ANN model that has been trained through blood pressure classification regression; specifically, the blood pressure ANN model consists of a fully connected layer, where each node of the fully connected layer is connected to the previous layer.
  • the input of the blood pressure ANN model is a two-dimensional matrix, so the output of the CNN needs to be converted from a four-dimensional tensor [B, H, W, C] to a two-dimensional matrix; the output of the blood pressure ANN model is also a two-dimensional Matrix [X, 2], in which the second dimension parameter X and B are equal to indicate the total number of segments, and the first dimension parameter of 2 means that the length of the X one-dimensional data sequences included in the matrix are all 2.
  • Each one-dimensional data sequence [2] includes two values, the higher value is the systolic blood pressure predicted by the corresponding PPG+ECG (or ECG+PPG) segment, and the lower value is the corresponding PPG+ECG (or ECG) +PPG) Diastolic blood pressure predicted by the fragment.
  • the output of the blood pressure ANN model is a pair of blood pressure prediction values (systolic blood pressure and diastolic blood pressure) corresponding to each PPG+ECG (or ECG+PPG) segment.
  • different processing methods can be adopted, such as averaging Value, so as to obtain the average blood pressure data within the signal acquisition time threshold; or directly output the blood pressure value sequence to obtain a segment of ambulatory blood pressure signal.
  • FIG. 1 is a schematic diagram of a method for blood pressure prediction based on a synchronization signal according to Embodiment 1 of the present invention. The method mainly includes the following steps:
  • Step 1 Obtain the PPG signal data of the photoplethysmography method and the ECG signal data synchronized with it; and according to the data sampling frequency threshold, perform signal sampling processing on the PPG signal data and the ECG signal data to generate PPG one-dimensional data Sequence and ECG one-dimensional data sequence;
  • the PPG signal data is generated by using a non-invasive PPG signal acquisition device to perform a preset light source signal acquisition operation on the tester’s local skin surface within the signal acquisition time threshold; here, when the PPG signal is acquired, the preset The set light source signal includes at least one of a red light source signal, an infrared light source signal, and a green light source signal;
  • the ECG signal data is synchronized with the acquisition of the PPG signal data, and is generated by using a non-invasive ECG signal acquisition device to perform the ECG physiological signal acquisition operation on the tester within the signal acquisition time threshold; here, when the ECG signal is acquired, It is a single-lead ECG signal; and the ECG signal must be collected synchronously with the PPG signal, so that the PTT time difference between the two is true and reliable;
  • the ECG one-dimensional data sequence [A] is a one-dimensional data sequence including 1250 ECG collected data.
  • Step 2 According to the input data length threshold N of the blood pressure convolutional neural network CNN model, perform blood pressure CNN input data fusion processing on the PPG one-dimensional data sequence and the ECG one-dimensional data sequence to generate a four-dimensional tensor of input data;
  • Step 21 Calculate the total number of fragments M based on the input data length threshold N and A; when A is divisible by the input data length threshold N, set the total number of fragments M as the quotient of A divided by the input data length threshold N; when A cannot When divisible by the input data length threshold N, the total number of fragments M is set as the result of rounding the quotient of A divided by the input data length threshold N;
  • the blood pressure CNN model will be used to perform feature calculations on the data in the PPG (or ECG) one-dimensional data sequence, in view of the input requirements of the blood pressure CNN (input data length threshold N), according to the input data length of the blood pressure CNN
  • the threshold N divides the PPG (or ECG) one-dimensional data sequence into segments.
  • the method for setting the total number of fragments here: If the total data length of the PPG (or ECG) one-dimensional data sequence can be evenly divided by the input data length threshold N, then the total number of fragments is the quotient of the division of the two; if the PPG (or ECG) is one The total data length of the one-dimensional data sequence cannot be divisible by the input data length threshold N, then the total number of segments is the rounded result of the quotient of the division of the two. The last segment of the PPG (or ECG) one-dimensional data sequence is regarded as the segment with insufficient length. Discard the fragments with incomplete data.
  • Step 22 According to the total number of fragments M and the input data length threshold N, the PPG one-dimensional data sequence [A] is divided into sequential data fragments to generate the PPG fragment data two-dimensional matrix [M, N], and the ECG one-dimensional data sequence [A] ] Perform sequential data segmentation processing to generate a two-dimensional matrix of ECG segment data [M, N];
  • step 221 generating a temporary data length L according to the product of the total number of fragments M and the input data length threshold N;
  • Step 222 From the PPG one-dimensional data sequence [A], take the first data as the starting data extraction position and the temporary data length L as the data extraction length, extract a piece of continuous data to generate the PPG one-dimensional temporary data sequence [L] ; And according to the input data length threshold N, the PPG one-dimensional temporary data sequence [L] is divided into continuous data segments;
  • the PPG one-dimensional temporary data sequence [L] includes the total number of segments M PPG one-dimensional segment data sequences [N]; the PPG one-dimensional segment data sequence [N] includes the input data length threshold N PPG data;
  • the method is to extract the PPG one-dimensional temporary data sequence [L] (effective data sequence) from the original PPG one-dimensional data sequence [A] according to the temporary data length L (also the effective data length) obtained by the upper calculation;
  • the method is to extract backwards from the earliest data;
  • Step 223 From the ECG one-dimensional data sequence [A], take the first data as the starting data extraction position and the temporary data length L as the data extraction length, extract a piece of continuous data to generate the ECG one-dimensional temporary data sequence [L] ; And according to the input data length threshold N, the ECG one-dimensional temporary data sequence [L] is divided into continuous data segments;
  • the ECG one-dimensional temporary data sequence [L] includes the total number of segments M ECG one-dimensional segment data sequences [N]; the ECG one-dimensional segment data sequence [N] includes the input data length threshold N ECG data;
  • the one-dimensional ECG temporary data sequence [L] (effective data sequence) is extracted from the original ECG one-dimensional data sequence [A] according to the temporary data length L (also the effective data length) obtained by the upper calculation;
  • the method is to extract backwards from the earliest data;
  • the extraction unit takes the extraction unit (input data length threshold N) as the segment length to complete the segmentation of the ECG one-dimensional temporary data sequence [L];
  • Step 224 Construct a two-dimensional PPG fragment data matrix [M, N], and initialize all matrix elements of the PPG fragment data two-dimensional matrix [M, N] to be empty; extract sequentially from the PPG one-dimensional temporary data sequence [L] The PPG one-dimensional segment data sequence [N] included in the PPG one-dimensional temporary data sequence [L] assigns values to the matrix elements of the PPG segment data two-dimensional matrix [M, N];
  • the PPG fragment data two-dimensional matrix [M, N] although it is a two-dimensional matrix, the actual number of matrix elements M*N it contains is equal to the total number of data included in the PPG one-dimensional temporary data sequence [L].
  • Step 225 Construct a two-dimensional ECG fragment data matrix [M, N], and initialize all matrix elements of the ECG fragment data two-dimensional matrix [M, N] to be empty; extract sequentially from the ECG one-dimensional temporary data sequence [L] The ECG one-dimensional segment data sequence [N] assigns values to the matrix elements of the ECG segment data two-dimensional matrix [M, N];
  • the two-dimensional matrix of ECG fragment data [M,N] is a two-dimensional matrix, the actual number of matrix elements M*N contained in it is the total number of data included in the ECG one-dimensional temporary data sequence [L] Equal, here is the tensor upscaling process of the shape of the ECG one-dimensional temporary data sequence [L] from the one-dimensional sequence;
  • Step 23 Perform input data fusion processing on the two-dimensional matrix of PPG fragment data [M, N] and the two-dimensional matrix of ECG fragment data [M, N] to generate a two-dimensional matrix of fused fragment data [M, 2*N];
  • step 231 obtaining a preset fusion sorting flag; constructing a two-dimensional matrix of fusion fragment data [M, 2*N], and initializing the two-dimensional matrix of fusion fragment data [M, 2*N] to be empty; constructing the first Sequence [2*N];
  • the fusion sorting identifier is one of PPG+ECG sorting and ECG+PPG sorting, and the first sequence [2*N] includes 2*N sequence data;
  • an identifier that specifies the sorting order during data fusion is provided: the fusion sorting identifier; the identifier includes two possible values: PPG+ECG sorting or ECG+PPG sorting; when the fusion sorting identifier is PPG+ECG sorting, fusion In each one-dimensional vector [2*N] (first sequence [2*N]) included in the two-dimensional matrix of fragment data, the first N are PPG data and the last N are ECG data; when the fusion sorting flag is ECG +When PPG sorting, in each one-dimensional vector [2*N] (first sequence [2*N]) included in the two-dimensional matrix of fusion fragment data, the first N are ECG data, and the last N are PPG data;
  • Step 232 Initialize the value of the first index to 1, and initialize the value of the first total to the total number of fragments M;
  • Step 233 taking the product of the first index minus 1 and the input data length threshold N plus 1 as the starting data extraction position, and the input data length threshold N as the extraction data length, from the two-dimensional matrix of PPG fragment data [ Extract a piece of continuous data from M, N] to generate a second sequence [N], and extract a piece of continuous data from a two-dimensional matrix of ECG fragment data [M, N] to generate a third sequence [N];
  • the fusion principle of the embodiment of the present invention is to keep all the data without modification while keeping the total number of fragments unchanged; then, we know that the total number of the two sets of data is 2*M*N, and the next step is to generate one
  • the two-dimensional matrix of fusion fragment data [M,2*N] with the shape of M*(2*N) includes all PPG and ECG data; for the two-dimensional matrix of fusion fragment data [M,2*N] we can regard it as M data sequences with a length of 2N, each data sequence includes N PPG data and N ECG data; here, the embodiment of the present invention provides a fusion order identifier to identify the front and back of N PPG data and N ECG data Order:
  • the fusion sorting flag is PPG+ECG sort
  • PPG segment data two-dimensional matrix [M, N] is PPG segment data two-dimensional matrix [5,250]
  • ECG segment data two-dimensional Matrix [M, N] is the two-dimensional matrix of ECG fragment data [5,250]
  • the final fused two-dimensional matrix of fused fragment data [M, 2*N] is the two-dimensional matrix of fused fragment data [5,500];
  • Step 234 Use the second sequence [N] and the third sequence [N] to assign values to the first sequence [2*N] according to the fusion order identifier; when the fusion order identifier is PPG+ECG order, use the second sequence [N] Perform assignment processing on the first N sequence data included in the first sequence [2*N], and use the third sequence [N] to perform assignment processing on the last N sequence data included in the first sequence [2*N] ;
  • the fusion sorting flag is ECG+PPG sorting
  • use the second sequence [N] to The last N sequence data included in sequence [2*N] are assigned values;
  • Step 235 using the first sequence [2*N] to perform a sequence data addition operation on the two-dimensional matrix [M, 2*N] of the fusion fragment data;
  • the 2N-length data sequence (the first sequence [2*N]) at the end of each assignment is added to the two-dimensional matrix [M, 2*N] of the fusion fragment data, and finally after M times are added Complete the whole construction process of the two-dimensional matrix [M,2*N] of the fusion fragment data;
  • Step 236 Add 1 to the first index
  • the first index is the segment index extracted from the two-dimensional matrix [M, N] of PPG (ECG) segment data;
  • Step 237 Determine whether the first index is greater than the first total, if the first index is greater than the first total, go to step 24, and if the first index is less than or equal to the first total, go to step 233.
  • Step 24 Perform a four-dimensional tensor conversion process of blood pressure CNN input data on the two-dimensional matrix [M,2*N] of the fused segment data to generate a four-dimensional tensor of input data [B 1 , H 1 , W 1 , C 1 ];
  • the matrix elements included in the two-dimensional matrix of fragment data [M,2*N] add data items to the four-dimensional tensor [B 1 , H 1 , W 1 , C 1] of the input data;
  • the input data four-dimensional tensor [B 1 ,H 1 ,W 1 ,C 1 ] is specifically the input data four-dimensional tensor [M,2,N,1];
  • B 1 is the input data four-dimensional tensor [B 1 ,H 1 ,W 1 ,C 1 ], the fourth dimension parameter, and B 1 is the total number of fragments M;
  • H 1 is the third dimension parameter of the input data four-dimensional tensor [B 1 ,H 1 ,W 1 ,C 1 ], and The value of H 1 is 2;
  • W 1 is the second dimension parameter of the input data four-dimensional tensor [B 1 , H 1 , W 1 , C 1 ], and W 1 is the input data length threshold N;
  • C 1 is the input data four-dimensional The first dimension parameter of the tensor [B 1 ,H 1 ,W 1 ,C 1 ], and the value of C 1 is 1.
  • the input and output parameters of the blood pressure CNN model used in the embodiment of the present invention are all in the form of a four-dimensional tensor, so here is a four-dimensional tensor increase for the two-dimensional matrix [M,2*N] of the fusion fragment data.
  • Step 3 According to the threshold of the number of convolutional layers, use the blood pressure CNN model to perform multi-layer convolution pooling calculation on the four-dimensional tensor of the input data to generate the four-dimensional tensor of feature data;
  • Step 31 Initialize the value of the second index to 1; initialize the second total to the threshold of the number of convolutional layers; initialize the temporary four-dimensional tensor of the second index to the four-dimensional tensor of the input data [B 1 ,H 1 ,W 1 , C 1 ];
  • Step 32 Use the second index layer convolution layer of the blood pressure CNN model to perform convolution calculation processing on the second index temporary four-dimensional tensor to generate the second index convolution output data four-dimensional tensor; use the second index of the blood pressure CNN model Pooling layer, which performs pooling calculation processing on the four-dimensional tensor of the second index convolution output data to generate a four-dimensional tensor of second index pooling output data;
  • the blood pressure CNN model includes a multi-layer convolutional layer and a multi-layer pooling layer;
  • the blood pressure CNN model consists of a multi-layer convolutional layer and a pooling layer.
  • the general structure is one layer of convolution and one layer of pooling.
  • the final number of layers of the blood pressure CNN model is determined by the number of convolutional layer thresholds.
  • a network with 4 convolutional layers and 4 pooling layers is called a 4-layer convolutional network, where convolution The layer performs convolution operations to convert the input into outputs of different dimensions. These outputs can be regarded as another way of expressing the input, and the pooling layer is used to control the number of outputs, simplify the operation and prompt the network to extract more effective information. ;
  • Step 33 Set the temporary four-dimensional tensor of the second index as the four-dimensional tensor of the second index pooling output data
  • Step 34 Add 1 to the second index
  • Step 35 Determine whether the second index is greater than the second total, if the second index is greater than the second total, go to step 36, if the second index is less than or equal to the second total, go to step 32;
  • Step 36 Set the feature data four-dimensional tensor as the second index temporary four-dimensional tensor
  • the four-dimensional tensor of feature data is specifically the four-dimensional tensor of feature data [B 2 , H 2 , W 2 , C 2 ];
  • B2 is the fourth tensor of feature data [B 2 , H 2 , W 2 , C 2 ]
  • B 2 is B 1 ;
  • H 2 is the third-dimensional parameter of the feature data four-dimensional tensor [B 2 , H 2 , W 2 , C 2 ];
  • W 2 is the feature data four-dimensional tensor [B 2 ,H 2 , W 2 , C 2 ] the second dimension parameter;
  • C 2 is the first dimension parameter of the feature data four-dimensional tensor [B 2 , H 2 , W 2 , C 2 ].
  • the convolution principle of each layer of the blood pressure CNN model is the same as the two-dimensional convolution principle.
  • the difference from image convolution is that the height H of the PPG signal and the ECG signal is 1, so the convolution kernel in the convolution layer is the first All dimensions are 1, such as [1x3], [1x5], [1x7] and so on.
  • the shape of the input data will change, but it still maintains the 4-dimensional tensor form.
  • the fourth dimension parameter (the total number of fragments) will not change, and the third and two-dimensional parameters (H and W) change are related to the size of the convolution kernel of each convolutional layer and the setting of the sliding step length, and it is also related to the pool
  • the pooling window size of the transformation layer is related to the sliding step size.
  • the first dimension parameter (the number of channels) is related to the selected output space dimension (the number of convolution kernels) in the convolution layer, and the number of layers in the network is set ,
  • the setting of various parameters of each layer must be determined based on experience and experimental results, not a fixed value, here it is assumed that after several layers of the network, the output of the network becomes 4 of the shape [5,2,20,64] Dimension tensor
  • Step 4 According to the four-dimensional tensor of the characteristic data, perform the operation of constructing a two-dimensional matrix of blood pressure artificial neural network ANN input data to generate a two-dimensional matrix of input data; and use the blood pressure ANN model to perform characteristic data regression calculation on the two-dimensional matrix of input data to generate blood pressure regression data.
  • Dimensional matrix
  • Step 41 according to the feature data four-dimensional tensor [B 2 , H 2 , W 2 , C 2 ], perform tensor data reduction on the feature data four-dimensional tensor [B 2 , H 2 , W 2 , C 2] Dimensional processing to construct a two-dimensional matrix of input data;
  • the input data two-dimensional matrix is specifically the input data two-dimensional matrix [W 3 , C 3 ];
  • W 3 is the second dimension parameter of the input data two-dimensional matrix [W 3 , C 3 ] and W 3 is B 2 ;
  • C 3 is the first dimension parameter of the input data two-dimensional matrix [W 3 , C 3 ] and C 3 is the product of H 2 multiplied by W 2 and then multiplied by C 2;
  • the characteristic data four-dimensional tensor [B 2 , H 2 , W 2 , C 2 ] is input into the blood pressure ANN for regression
  • the shape of the tensor needs to be reduced before calculation.
  • the feature data four-dimensional tensor [B 2 ,H 2 ,W 2 ,C 2 ] is the feature data four-dimensional tensor [5,2,20,64]. After its shape is reduced in dimensionality, it becomes a two-dimensional matrix of input data [5,2*20*64], which is a two-dimensional matrix of input data [5,2560];
  • Step 42 using the blood pressure ANN model to perform feature data regression calculation on the two-dimensional matrix of input data [W 3 , C 3 ] to generate a two-dimensional matrix of blood pressure regression data;
  • the blood pressure regression data two-dimensional matrix is specifically the blood pressure regression data two-dimensional matrix [X, 2]; X is the second dimension parameter of the blood pressure regression data two-dimensional matrix [X, 2] and X is W 3 ; the blood pressure regression data two
  • the dimensional matrix [X, 2] includes X regression data one-dimensional data sequence [2]; the regression data one-dimensional data sequence [2] includes fragment systolic blood pressure data and fragment diastolic blood pressure data.
  • the blood pressure ANN model is composed of fully connected layers. Each node of the fully connected layer is connected to all the nodes in the previous layer. It is used to integrate the features extracted from the front. Each fully connected layer can be set The number of nodes in this layer and the activation function (there are more ReLUs, you can also change to other), for example, if the number of nodes in the current connection layer is set to 512, the output of the current connection layer becomes [5,512], after several layers The connection layer converts the final output into a matrix of shape [X, 2].
  • the second dimension parameter X represents the total number of fragments
  • the first dimension parameter 2 means that two regression calculation values are output for each fragment: respectively represent The systolic and diastolic blood pressure
  • the blood pressure ANN model is a mature model after training.
  • the blood pressure CNN model and the blood pressure ANN model are connected together and trained with the same batch of training data;
  • Step 5 Obtain the prediction mode identifier
  • the prediction mode identifier includes two types of identifiers: mean prediction and dynamic prediction.
  • the prediction mode identifier is a system variable.
  • This variable can be used to clarify the further prediction output content: when the prediction mode identifier is the mean value When predicting, it means that the tester's average blood pressure data is required to be output within the acquisition time threshold; when the prediction mode identifier is dynamic prediction, it means that the tester's blood pressure change data sequence within the acquisition time threshold is required to be output.
  • Step 6 When the prediction mode identifier is average prediction, perform an average blood pressure calculation operation on the two-dimensional matrix of blood pressure regression data to generate an average blood pressure prediction data pair;
  • Step 61 when the prediction mode identifier is average prediction, set the average blood pressure prediction data pair; initialize the diastolic blood pressure prediction data of the average blood pressure prediction data pair to be empty, and initialize the systolic blood pressure data of the average blood pressure prediction data pair to be empty ;
  • Step 62 Calculate the sum of the diastolic blood pressure data of all regression data one-dimensional data sequence [2] included in the two-dimensional matrix of blood pressure regression data [X,2] to generate the sum of diastolic blood pressure, which is generated according to the quotient of the sum of diastolic blood pressure divided by X
  • the first mean the systolic blood pressure data of all regression data one-dimensional data sequence [2] included in the two-dimensional matrix of blood pressure regression data [X,2] is calculated to generate the total systolic blood pressure, and the quotient of the total systolic blood pressure divided by X Generate the second mean;
  • Step 63 Set the diastolic blood pressure prediction data of the average blood pressure prediction data pair as the first average value; set the systolic blood pressure prediction data of the average blood pressure prediction data pair as the second average value.
  • FIG. 2 is a schematic diagram of a method for blood pressure prediction based on a synchronization signal according to Embodiment 2 of the present invention. The method mainly includes the following steps:
  • Step 101 Obtain the PPG signal data of the photoplethysmography method and the ECG signal data synchronized with it; and according to the data sampling frequency threshold, perform signal sampling processing on the PPG signal data and the ECG signal data to generate PPG one-dimensional data Sequence and ECG one-dimensional data sequence;
  • the PPG signal data is generated by using a non-invasive PPG signal acquisition device to perform a preset light source signal acquisition operation on the tester’s local skin surface within the signal acquisition time threshold; here, when the PPG signal is acquired, the preset The set light source signal includes at least one of a red light source signal, an infrared light source signal, and a green light source signal;
  • the ECG signal data is synchronized with the acquisition of the PPG signal data, and is generated by using a non-invasive ECG signal acquisition device to perform the ECG physiological signal acquisition operation on the tester within the signal acquisition time threshold; here, when the ECG signal is acquired, It is a single-lead ECG signal; and the ECG signal must be collected synchronously with the PPG signal, so that the PTT time difference between the two is true and reliable;
  • the ECG one-dimensional data sequence [A] is a one-dimensional data sequence including 1250 ECG collected data.
  • Step 102 Perform blood pressure CNN input data fusion processing on the PPG one-dimensional data sequence and the ECG one-dimensional data sequence according to the input data length threshold N of the blood pressure convolutional neural network CNN model to generate a four-dimensional tensor of input data;
  • Step 1021 Calculate the total number of fragments M based on the input data length threshold N and A; when A is divisible by the input data length threshold N, set the total number of fragments M as the quotient of A divided by the input data length threshold N; when A cannot When divisible by the input data length threshold N, the total number of fragments M is set as the result of rounding the quotient of A divided by the input data length threshold N;
  • the blood pressure CNN model will be used to perform feature calculations on the data in the PPG (or ECG) one-dimensional data sequence, in view of the input requirements of the blood pressure CNN (input data length threshold N), according to the input data length of the blood pressure CNN
  • the threshold N divides the PPG (or ECG) one-dimensional data sequence into segments.
  • the method for setting the total number of fragments here: If the total data length of the PPG (or ECG) one-dimensional data sequence can be evenly divided by the input data length threshold N, then the total number of fragments is the quotient of the division of the two; if the PPG (or ECG) is one The total data length of the one-dimensional data sequence cannot be divisible by the input data length threshold N, then the total number of segments is the rounded result of the quotient of the division of the two. The last segment of the PPG (or ECG) one-dimensional data sequence is regarded as the segment with insufficient length. Discard the fragments with incomplete data.
  • Step 1022 According to the total number of fragments M and the input data length threshold N, the PPG one-dimensional data sequence [A] is divided into sequential data fragments to generate a PPG fragment data two-dimensional matrix [M, N], and the ECG one-dimensional data sequence [A] ] Perform sequential data segmentation processing to generate a two-dimensional matrix of ECG segment data [M, N];
  • Step 10221 generating a temporary data length L according to the product of the total number of fragments M and the input data length threshold N;
  • Step 10222 From the PPG one-dimensional data sequence [A], take the first data as the starting data extraction position and the temporary data length L as the data extraction length, extract a piece of continuous data to generate the PPG one-dimensional temporary data sequence [L] ; And according to the input data length threshold N, the PPG one-dimensional temporary data sequence [L] is divided into continuous data segments;
  • the PPG one-dimensional temporary data sequence [L] includes the total number of segments M PPG one-dimensional segment data sequences [N]; the PPG one-dimensional segment data sequence [N] includes the input data length threshold N PPG data;
  • the method is to extract the PPG one-dimensional temporary data sequence [L] (effective data sequence) from the original PPG one-dimensional data sequence [A] according to the temporary data length L (also the effective data length) obtained by the upper calculation;
  • the method is to extract backwards from the earliest data;
  • Step 10223 From the ECG one-dimensional data sequence [A], take the first data as the data start extraction position and the temporary data length L as the data extraction length, extract a piece of continuous data to generate the ECG one-dimensional temporary data sequence [L] ; And according to the input data length threshold N, the ECG one-dimensional temporary data sequence [L] is divided into continuous data segments;
  • the ECG one-dimensional temporary data sequence [L] includes the total number of segments M ECG one-dimensional segment data sequences [N]; the ECG one-dimensional segment data sequence [N] includes the input data length threshold N ECG data;
  • the one-dimensional ECG temporary data sequence [L] (effective data sequence) is extracted from the original ECG one-dimensional data sequence [A] according to the temporary data length L (also the effective data length) obtained by the upper calculation;
  • the method is to extract backwards from the earliest data;
  • the extraction unit takes the extraction unit (input data length threshold N) as the segment length to complete the segmentation of the ECG one-dimensional temporary data sequence [L];
  • Step 10224 construct a two-dimensional PPG fragment data matrix [M, N], and initialize all matrix elements of the PPG fragment data two-dimensional matrix [M, N] to be empty; extract sequentially from the PPG one-dimensional temporary data sequence [L]
  • the PPG one-dimensional segment data sequence [N] included in the PPG one-dimensional temporary data sequence [L] assigns values to the matrix elements of the PPG segment data two-dimensional matrix [M, N];
  • the PPG fragment data two-dimensional matrix [M, N] although it is a two-dimensional matrix, the actual number of matrix elements M*N it contains is equal to the total number of data included in the PPG one-dimensional temporary data sequence [L].
  • Step 10225 construct a two-dimensional matrix of ECG fragment data [M, N], and initialize all matrix elements of the two-dimensional matrix of ECG fragment data [M, N] to be empty; extract sequentially from the ECG one-dimensional temporary data sequence [L] The ECG one-dimensional segment data sequence [N] assigns values to the matrix elements of the ECG segment data two-dimensional matrix [M, N];
  • the two-dimensional matrix of ECG fragment data [M,N] is a two-dimensional matrix, the actual number of matrix elements M*N contained in it is the total number of data included in the ECG one-dimensional temporary data sequence [L] Equal, here is the tensor upscaling process of the shape of the ECG one-dimensional temporary data sequence [L] from the one-dimensional sequence;
  • Step 1023 Perform input data fusion processing on the two-dimensional matrix of PPG fragment data [M, N] and the two-dimensional matrix of ECG fragment data [M, N] to generate a two-dimensional matrix of fused fragment data [M, 2*N];
  • Step 10231 Obtain a preset fusion sorting flag; construct a two-dimensional matrix of fusion fragment data [M, 2*N], and initialize the two-dimensional matrix of fusion fragment data [M, 2*N] to be empty; construct the first Sequence [2*N];
  • the fusion sorting identifier is one of PPG+ECG sorting and ECG+PPG sorting, and the first sequence [2*N] includes 2*N sequence data;
  • an identifier that specifies the sorting order during data fusion is provided: the fusion sorting identifier; the identifier includes two possible values: PPG+ECG sorting or ECG+PPG sorting; when the fusion sorting identifier is PPG+ECG sorting, fusion In each one-dimensional vector [2*N] (first sequence [2*N]) included in the two-dimensional matrix of fragment data, the first N are PPG data and the last N are ECG data; when the fusion sorting flag is ECG +When PPG sorting, in each one-dimensional vector [2*N] (first sequence [2*N]) included in the two-dimensional matrix of fusion fragment data, the first N are ECG data, and the last N are PPG data;
  • Step 10232 Initialize the value of the first index to 1, and initialize the value of the first total to the total number of fragments M;
  • Step 10233 using (first index-1)*input data length threshold N+1 as the starting data extraction position, and input data length threshold N as the extraction data length, from the two-dimensional matrix of PPG fragment data [M, N] Extract a piece of continuous data to generate the second sequence [N], and extract a piece of continuous data from the two-dimensional matrix of ECG fragment data [M, N] to generate the third sequence [N];
  • the fusion principle of the embodiment of the present invention is to keep all the data without modification while keeping the total number of fragments unchanged; then, we know that the total number of the two sets of data is 2*M*N, and the next step is to generate one
  • the two-dimensional matrix of fusion fragment data [M,2*N] with the shape of M*(2*N) includes all PPG and ECG data; for the two-dimensional matrix of fusion fragment data [M,2*N] we can regard it as M data sequences with a length of 2N, each data sequence includes N PPG data and N ECG data; here, the embodiment of the present invention provides a fusion order identifier to identify the front and back of N PPG data and N ECG data Order:
  • the fusion sorting flag is PPG+ECG sort
  • PPG segment data two-dimensional matrix [M, N] is PPG segment data two-dimensional matrix [5,250]
  • ECG segment data two-dimensional Matrix [M, N] is the two-dimensional matrix of ECG fragment data [5,250]
  • the final fused two-dimensional matrix of fused fragment data [M, 2*N] is the two-dimensional matrix of fused fragment data [5,500];
  • Step 10234 Use the second sequence [N] and the third sequence [N] to assign values to the first sequence [2*N] according to the fusion order identifier; when the fusion order identifier is PPG+ECG order, use the second sequence [N] Perform assignment processing on the first N sequence data included in the first sequence [2*N], and use the third sequence [N] to perform assignment processing on the last N sequence data included in the first sequence [2*N] ; When the fusion sorting flag is ECG+PPG sorting, use the third sequence [N] to assign values to the first N sequence data included in the first sequence [2*N], and use the second sequence [N] to The last N sequence data included in sequence [2*N] are assigned values;
  • Step 10235 using the first sequence [2*N] to perform a sequence data addition operation on the two-dimensional matrix [M, 2*N] of the fusion fragment data;
  • the 2N-length data sequence (the first sequence [2*N]) at the end of each assignment is added to the two-dimensional matrix [M, 2*N] of the fusion fragment data, and finally after M times are added Complete the whole construction process of the two-dimensional matrix [M,2*N] of the fusion fragment data;
  • Step 10236 add 1 to the first index
  • the first index is the slice index extracted from the two-dimensional matrix [M, N] of PPG (ECG) slice data;
  • Step 10237 Determine whether the first index is greater than the first total. If the first index is greater than the first total, go to step 1024; if the first index is less than or equal to the first total, go to step 10233.
  • Step 1024 Perform four-dimensional tensor conversion processing of blood pressure CNN input data on the two-dimensional matrix [M,2*N] of the fused segment data to generate a four-dimensional tensor of input data [B 1 , H 1 , W 1 , C 1 ];
  • the matrix elements included in the two-dimensional matrix of fragment data [M,2*N] add data items to the four-dimensional tensor [B 1 , H 1 , W 1 , C 1] of the input data;
  • the input data four-dimensional tensor [B 1 ,H 1 ,W 1 ,C 1 ] is specifically the input data four-dimensional tensor [M,2,N,1];
  • B 1 is the input data four-dimensional tensor [B 1 ,H 1 ,W 1 ,C 1 ], the fourth dimension parameter, and B 1 is the total number of fragments M;
  • H 1 is the third dimension parameter of the input data four-dimensional tensor [B 1 ,H 1 ,W 1 ,C 1 ], and The value of H 1 is 2;
  • W 1 is the second dimension parameter of the input data four-dimensional tensor [B 1 , H 1 , W 1 , C 1 ], and W 1 is the input data length threshold N;
  • C 1 is the input data four-dimensional The first dimension parameter of the tensor [B 1 ,H 1 ,W 1 ,C 1 ], and the value of C 1 is 1.
  • the input and output parameters of the blood pressure CNN model used in the embodiment of the present invention are all in the form of a four-dimensional tensor, so here is a four-dimensional tensor increase for the two-dimensional matrix [M,2*N] of the fusion fragment data.
  • Step 103 According to the threshold of the number of convolution layers, use the blood pressure CNN model to perform multi-layer convolution pooling calculation on the four-dimensional tensor of the input data to generate a four-dimensional tensor of feature data;
  • Step 1031 initialize the value of the second index to 1; initialize the second total to the threshold of the number of convolutional layers; initialize the temporary four-dimensional tensor of the second index to the four-dimensional tensor of the input data [B 1 ,H 1 ,W 1 , C 1 ];
  • Step 1032 Use the second index layer convolution layer of the blood pressure CNN model to perform convolution calculation processing on the second index temporary four-dimensional tensor to generate the second index convolution output data four-dimensional tensor; use the second index of the blood pressure CNN model Pooling layer, which performs pooling calculation processing on the four-dimensional tensor of the second index convolution output data to generate a four-dimensional tensor of second index pooling output data;
  • the blood pressure CNN model includes a multi-layer convolutional layer and a multi-layer pooling layer;
  • the blood pressure CNN model consists of a multi-layer convolutional layer and a pooling layer.
  • the general structure is one layer of convolution and one layer of pooling.
  • the final number of layers of the blood pressure CNN model is determined by the number of convolutional layer thresholds.
  • a network with 4 convolutional layers and 4 pooling layers is called a 4-layer convolutional network, where convolution The layer performs convolution operations to convert the input into outputs of different dimensions. These outputs can be regarded as another way of expressing the input, and the pooling layer is used to control the number of outputs, simplify the operation and prompt the network to extract more effective information. ;
  • Step 1033 Set the temporary four-dimensional tensor of the second index as the four-dimensional tensor of the second index pooling output data
  • Step 1034 add 1 to the second index
  • Step 1035 Determine whether the second index is greater than the second total, if the second index is greater than the second total, go to step 1036, if the second index is less than or equal to the second total, go to step 1032;
  • Step 1036 Set the four-dimensional tensor of feature data as a temporary four-dimensional tensor of the second index
  • the four-dimensional tensor of feature data is specifically the four-dimensional tensor of feature data [B 2 , H 2 , W 2 , C 2 ];
  • B2 is the fourth tensor of feature data [B 2 , H 2 , W 2 , C 2 ]
  • B 2 is B 1 ;
  • H 2 is the third-dimensional parameter of the feature data four-dimensional tensor [B 2 , H 2 , W 2 , C 2 ];
  • W 2 is the feature data four-dimensional tensor [B 2 ,H 2 , W 2 , C 2 ] the second dimension parameter;
  • C 2 is the first dimension parameter of the feature data four-dimensional tensor [B 2 , H 2 , W 2 , C 2 ].
  • the convolution principle of each layer of the blood pressure CNN model is the same as the two-dimensional convolution principle.
  • the difference from image convolution is that the height H of the PPG signal and the ECG signal is 1, so the convolution kernel in the convolution layer is the first All dimensions are 1, such as [1x3], [1x5], [1x7] and so on.
  • the shape of the input data will change, but it still maintains the 4-dimensional tensor form.
  • the fourth dimension parameter (the total number of fragments) will not change, and the third and two-dimensional parameters (H and W) change are related to the size of the convolution kernel of each convolutional layer and the setting of the sliding step length, and it is also related to the pool
  • the pooling window size of the transformation layer is related to the sliding step size.
  • the first dimension parameter (the number of channels) is related to the selected output space dimension (the number of convolution kernels) in the convolution layer, and the number of layers in the network is set ,
  • the setting of various parameters of each layer must be determined based on experience and experimental results, not a fixed value, here it is assumed that after several layers of the network, the output of the network becomes 4 of the shape [5,2,20,64] Dimension tensor
  • Step 104 Perform a blood pressure artificial neural network ANN input data two-dimensional matrix construction operation based on the feature data four-dimensional tensor to generate a two-dimensional matrix of input data; and use the blood pressure ANN model to perform feature data regression calculation on the two-dimensional matrix of input data to generate blood pressure regression data.
  • Dimensional matrix
  • Step 1041 according to the feature data four-dimensional tensor [B 2 , H 2 , W 2 , C 2 ], perform tensor data reduction on the feature data four-dimensional tensor [B 2 , H 2 , W 2 , C 2] Dimensional processing to construct a two-dimensional matrix of input data;
  • the input data two-dimensional matrix is specifically the input data two-dimensional matrix [W 3 , C 3 ];
  • W 3 is the second dimension parameter of the input data two-dimensional matrix [W 3 , C 3 ] and W 3 is B 2 ;
  • C 3 is the first dimension parameter of the input data two-dimensional matrix [W 3 , C 3 ] and C 3 is the product of H 2 multiplied by W 2 and then multiplied by C 2;
  • the characteristic data four-dimensional tensor [B 2 , H 2 , W 2 , C 2 ] is input into the blood pressure ANN for regression
  • the shape of the tensor needs to be reduced before calculation.
  • the feature data four-dimensional tensor [B 2 ,H 2 ,W 2 ,C 2 ] is the feature data four-dimensional tensor [5,2,20,64]. After its shape is reduced in dimensionality, it becomes a two-dimensional matrix of input data [5,2*20*64], which is a two-dimensional matrix of input data [5,2560];
  • Step 1042 using the blood pressure ANN model to perform characteristic data regression calculation on the two-dimensional matrix of input data [W 3 , C 3 ] to generate a two-dimensional matrix of blood pressure regression data;
  • the blood pressure regression data two-dimensional matrix is specifically the blood pressure regression data two-dimensional matrix [X, 2]; X is the second dimension parameter of the blood pressure regression data two-dimensional matrix [X, 2] and X is W 3 ; the blood pressure regression data two
  • the dimensional matrix [X, 2] includes X regression data one-dimensional data sequence [2]; the regression data one-dimensional data sequence [2] includes fragment systolic blood pressure data and fragment diastolic blood pressure data.
  • the blood pressure ANN model is composed of fully connected layers. Each node of the fully connected layer is connected to all the nodes in the previous layer. It is used to integrate the features extracted from the front. Each fully connected layer can be set The number of nodes in this layer and the activation function (there are more ReLUs, you can also change to other), for example, if the number of nodes in the current connection layer is set to 512, the output of the current connection layer becomes [5,512], after several layers The connection layer converts the final output into a matrix of shape [X, 2].
  • the second dimension parameter X represents the total number of fragments
  • the first dimension parameter 2 means that two regression calculation values are output for each fragment: respectively represent The systolic and diastolic blood pressure; the blood pressure ANN model is a mature model after training.
  • the blood pressure CNN model and the blood pressure ANN model are connected together and trained with the same batch of training data.
  • Step 105 Obtain a prediction mode identifier
  • the prediction mode identifier includes two types of identifiers: mean prediction and dynamic prediction.
  • the prediction mode identifier is a system variable.
  • This variable can be used to clarify the further prediction output content: when the prediction mode identifier is the mean value When predicting, it means that the tester's average blood pressure data is required to be output within the acquisition time threshold; when the prediction mode identifier is dynamic prediction, it means that the tester's blood pressure change data sequence within the acquisition time threshold is required to be output.
  • Step 106 When the prediction mode identifier is dynamic prediction, perform an ambulatory blood pressure data extraction operation on a two-dimensional matrix of blood pressure regression data to generate a one-dimensional ambulatory blood pressure prediction data sequence;
  • step 1061 when the prediction mode identifier is dynamic prediction, initialize the ambulatory blood pressure prediction one-dimensional data sequence to be empty; set the blood pressure data group; initialize the diastolic blood pressure data of the blood pressure data group to be empty; initialize the systolic blood pressure of the blood pressure data group Data is empty;
  • Step 1062 sequentially extract the regression data one-dimensional data sequence [2] included in the blood pressure regression data two-dimensional matrix [X, 2] to generate the current data sequence [2]; set the systolic blood pressure data of the blood pressure data group as the current data sequence [2] Set the diastolic blood pressure data of the blood pressure data group as the fragment diastolic blood pressure data of the current data sequence [2]; add the blood pressure data group to the ambulatory blood pressure prediction one-dimensional data sequence for data group addition operation.
  • the first data diastolic blood pressure data group is [D 11, 12 D] fragment diastolic pressure data
  • the first data systolic blood pressure data group is [D 11, D 12] of the The segmental systolic blood pressure data
  • the diastolic blood pressure data of the fifth blood pressure data group is the segment systolic blood pressure data in [D 51 ,D 52 ]
  • the systolic blood pressure data of the fifth blood pressure data group is [D 51 ,D 52 ] Fragment systolic blood pressure data.
  • FIG. 3 is a schematic diagram of a device structure of an apparatus for performing blood pressure prediction based on a synchronization 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 above 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 method and device for blood pressure prediction based on synchronization signals, which perform feature data fusion on synchronized ECG signals and PPG signals, and then perform fusion data on the fusion data through a blood pressure prediction model composed of a blood pressure CNN model and a blood pressure ANN model Feature calculation and blood pressure data regression calculation to calculate the blood pressure data of the tester, and finally output the mean blood pressure prediction data pair (systolic blood pressure prediction data, diastolic blood pressure prediction data) or ambulatory blood pressure prediction one-dimensional data sequence through the selection of the prediction mode identifier .
  • the mean blood pressure prediction data pair systolic blood pressure prediction data, diastolic blood pressure prediction data
  • ambulatory blood pressure prediction one-dimensional data sequence through the selection of the prediction mode identifier .
  • 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

L'invention concerne un procédé et un appareil de prédiction de pression artérielle sur la base d'un signal de synchronisation. Le procédé consiste à : obtenir des données de signal PPG et des données de signal ECG synchronisées avec les données de signal PPG ; effectuer respectivement un traitement d'échantillonnage de signal sur les données de signal PPG et les données de signal ECG selon un seuil de fréquence d'échantillonnage de données pour générer des séquences de données unidimensionnelles de PPG et d'ECG ; réaliser un traitement de fusion de données d'entrée de CNN de pression artérielle sur les séquences de données unidimensionnelles de PPG et d'ECG pour générer un tenseur à quatre dimensions de données d'entrée ; selon un seuil de nombre de couches de convolution, effectuer un calcul de regroupement de convolution multicouche à l'aide d'un modèle de CNN de pression artérielle pour générer un tenseur à quatre dimensions de données caractéristiques ; construire une matrice bidimensionnelle de données d'entrée ANN de pression artérielle ; réaliser un calcul de régression de données caractéristiques à l'aide du modèle ANN de pression artérielle pour générer une matrice bidimensionnelle de données de régression de pression artérielle ; obtenir un identifiant de mode de prédiction ; lorsque l'identifiant de mode de prédiction est une prédiction moyenne, générer une paire de données de prédiction de pression artérielle moyenne ; et lorsque l'identifiant de mode de prédiction est une prédiction dynamique, générer une séquence de données unidimensionnelle de prédiction de pression artérielle dynamique.
PCT/CN2020/129642 2020-03-17 2020-11-18 Procédé et appareil de prédiction de pression artérielle sur la base d'un signal de synchronisation WO2021184801A1 (fr)

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