CN115778389A - Birth fear detection method and system based on electrocardio and electrodermal joint analysis - Google Patents
Birth fear detection method and system based on electrocardio and electrodermal joint analysis Download PDFInfo
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Abstract
The invention belongs to the technical field of emotion monitoring and adjustment, and particularly relates to a childbirth fear detection method and system based on electrocardio and electrodermal joint analysis. The invention comprises the following steps: the method comprises the steps that original electric signals of the heart and the skin of a pregnant woman are synchronously collected, and a doctor divides the childbirth fear level of the pregnant woman according to a childbirth attitude questionnaire; signal preprocessing, including filtering, denoising and standardization; extracting coupling characteristics of the standardized electrocardio and skin electric signals; acquiring network learning characteristics of the electrocardio-skin electric signals and the skin electric signals based on the convolutional neural network model, and fusing the network learning characteristics with the coupling characteristics; and inputting the integrated comprehensive characteristics into a bidirectional long-time and short-time memory network model to evaluate the childbirth fear grade, and obtaining a childbirth fear classification result. Compared with the childbirth fear detection based on the subjective scale, the method and the device deeply excavate the information related to the emotion of the pregnant woman contained in the electrocardio-signals and the skin-electric signals, can judge the childbirth fear more timely and accurately, and are beneficial to the pregnant woman to know and adjust the psychological state of the pregnant woman timely.
Description
Technical Field
The invention belongs to the technical field of emotion monitoring and adjustment, and particularly relates to a method and a system for detecting childbirth fear of pregnant women.
Background
Pregnancy is a critical period specific to women during which, in addition to being physiologically variable, a series of psychological changes also occur. Fear of childbirth (FOC) is a common psychological problem in pregnant women and has received much attention in recent years. Fear of childbirth is a negative cognitive assessment of childbirth, and is a negative impact on the quality of childbirth and on the safety of the labor, as regards the impending childbirth being fraught with fear and anxiety, and even the psychological state that one wants to escape. The fear of delivery is identified timely and accurately, so that effective intervention measures are collected, and the method has important significance.
In the field of emotion recognition, an emotion recognition method based on a human physiological signal is considered to be more reliable and more accurate than facial expressions, voices, gestures, postures, and the like. However, at present, the judgment of the fear of delivery of pregnant women is mainly realized by self-measuring scales, including a delivery attitude scale (CAQ), a delivery fear scale (FCQ), a Wijma delivery expectation/experience scale (W-DEQ) and the like. The evaluation of childbirth fear based on physiological signals is lacking in systematic studies.
The physiological signals used for emotion recognition mainly include electroencephalogram (EEG), electrocardiogram (ECG), electromyogram (EMG), galvanic Skin Response (GSR), respiration (RSP), skin temperature (SKT), photoplethysmography (PPG), and the like. Compared with other physiological signals, the electrocardio-signals and the skin electric signals are easier to acquire and are less interfered by noise. Meanwhile, considering the particularity of the body type of the pregnant woman, the two signals have the smallest influence on the pregnant woman, and are most suitable for constructing a childbirth fear evaluation model.
Disclosure of Invention
The invention aims to provide a childbirth fear detection method and a childbirth fear detection system based on electrocardio and electrodermal joint analysis, which can more timely and accurately judge childbirth fear, so that a pregnant woman can timely know and adjust the psychological state of the pregnant woman.
The invention provides a childbirth fear detection method based on electrocardio and skin electricity combined analysis, which comprises the steps of collecting and obtaining an original skin electric signal of a pregnant woman; synchronously collecting electrocardio and skin electric signals of the pregnant woman; filtering, denoising and standardizing the original signal; calculating time domain, frequency domain and nonlinear coupling characteristics of the electrocardio and skin electric signals; network training characteristics of the electrocardio-skin electric signals and the skin electric signals are obtained through a Convolutional Neural Network (CNN), and are fused with the coupling characteristics; and sending the obtained comprehensive characteristics into a bidirectional long-time memory network (biLSTM) to obtain a birth fear classification result. The method comprises the following specific steps:
step (1): synchronously collecting original electric signals of the heart and skin of the pregnant woman, and dividing the childbirth fear level of the pregnant woman by a doctor according to a childbirth attitude questionnaire;
step (2): signal preprocessing, including filtering, denoising and standardization;
and (3): extracting coupling characteristics of the standardized electrocardio and skin electric signals;
and (4): obtaining network learning characteristics of the electrocardio-skin electric signals and the skin electric signals by adopting a Convolutional Neural Network (CNN) model, and fusing the network learning characteristics with the coupling characteristics;
and (5): and inputting the integrated comprehensive characteristics into a bidirectional long-time memory network (biLSTM) model, and carrying out birth fear grade evaluation to obtain a birth fear classification result.
Further, the fear of childbirth grade in step (1) is divided into: no fear of childbirth, mild fear of childbirth, moderate fear of childbirth and severe fear of childbirth.
Further, the signal preprocessing in the step (2) comprises: for the acquired original electrocardiosignals, a second-order Butterworth band-pass filter of 0.05-75Hz is adopted for denoising and baseline drift removal, and a notch filter of 50Hz is used for removing power frequency interference; for the collected original skin electric signals, a second-order Butterworth band-pass filter with the frequency of 0.02-0.5Hz is adopted for denoising and baseline drift removing; then, the filtered skin electric and electrocardio signals are standardized by a Z-score method.
Further, the coupling indexes in the step (3) comprise Pearson correlation coefficients, amplitude square coherence functions, mutual information, mutual sample entropy and mutual fuzzy entropy, and the coupling relation between the skin electricity and the electrocardio electricity is analyzed from three aspects of time domain, frequency domain and nonlinearity respectively. For cardioelectric sequences { X i N = x (i), 1 ≦ i ≦ N } and electrodermal sequence Y j And = y (j), 1 ≦ j ≦ M }, wherein x (i) and y (j) respectively represent each electrocardiogram and skin electrical value in the time sequence, N represents the sequence length, and the coupling index is obtained by the following method:
(1) pearson correlation coefficient:
wherein the content of the first and second substances,
and
respectively represent X i And Y j The average value of (a) of (b),
and
respectively represent X i And Y j Standard deviation of (2).
(2) Amplitude squared coherence function:
wherein, P XY (f) Representing the cross-spectral density, P, between two time series XX (f) And P YY (f) Respectively represent X i And Y j MSCF represents the degree of linear correlation between the components of the two time series at each frequency.
(3) Given a reconstruction dimension m, X i And Y j Respectively reconstructing as follows:
definition of
And
the distance between them is:
wherein | represents the maximum norm;
given a threshold value r, define
Wherein Θ (-) is a Heaviside function;
increasing the reconstruction dimension m to m +1, calculating
Then the cross-sample entropy is:
(4) given a reconstruction dimension m, X i And Y j Respectively reconstructing as follows:
definition of
And
the distance between them is:
given a threshold value r, define
Comprises the following steps:
wherein the content of the first and second substances,
is a decreasing half Gaussian distribution function;
increasing the reconstruction dimension m to m +1, calculating
The cross-ambiguity entropy is:
further, the Convolutional Neural Network (CNN) model in step (4) is a dual-scale convolutional neural network. Each scale has 4 convolutional layers, 2 pooling layers, 2 dropout layers, 1 flat layer and 1 fully connected layer. The first convolutional layer is used to initially extract features and reduce tensor size, the pooling layer and dropout layer are used to downsample the feature tensor and prevent the over-fitting phenomenon, and the following three identical convolutional layers are used to further refine the filter. Then another pooling layer and a flattening layer are applied to further reduce the size of each tensor, and finally the tensors are connected and then passed through a dropout layer, and the tensor size is further reduced through a fully connected layer. And (4) after the network learning characteristic is obtained, fusing the network learning characteristic with the coupling characteristic in the step (3) to obtain a comprehensive characteristic.
Based on the delivery fear detection method, the invention also comprises a delivery fear detection system based on electrocardio and skin-electricity combined analysis. The system specifically comprises five modules which are respectively: the pregnant woman's original electrocardio and skin electric signal collection module, signal preprocessing module, electrocardio and skin electric signal coupling characteristic extraction module, the characteristic learning, the integration module based on Convolution Neural Network (CNN) model, the fear of childbirth grade evaluation module based on two-way long-and-short term memory network (bilSTM) model. The five modules respectively execute the operation contents of the five steps of the method.
The invention has the characteristics and beneficial effects that:
compared with the childbirth fear detection based on the subjective scale at present, the method and the device deeply excavate the information related to the emotion of the pregnant woman contained in the electrocardio-skin electric signal, can more timely and accurately judge the childbirth fear, and are beneficial for the pregnant woman to timely know and adjust the psychological state of the pregnant woman.
Drawings
Fig. 1 is an overall flowchart of the childbirth fear detection method based on the combined analysis of the electrocardio-signals and the skin electric signals.
Fig. 2 is an overall architecture of a dual scale convolutional neural network.
FIG. 3 is the overall architecture of a bidirectional long-short-term memory network (bilSTM).
Detailed Description
The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments. The whole process of the childbirth fear detection method provided by the invention is shown in figure 1, and comprises the following steps:
(1) The method comprises the steps of synchronously collecting original electric signals of the heart and the skin of a pregnant woman, and dividing the childbirth fear level by a doctor according to a childbirth attitude questionnaire;
in the embodiment, the portable equipment is used for collecting electrocardio and skin electric signals of 20 pregnant women in 32 weeks, the collection is carried out in a nursing room of an obstetrical department outpatient service, and the signal duration is at least 5 minutes. The pregnant woman fills out a general data survey table and a childbirth attitude scale, and a doctor divides a childbirth fear grade according to the scale. Specifically, the childbirth attitude scale contains 16 entries, with a score of 1-4, with a total score of 16-64, with higher scores indicating more severe childbirth fear, 16-27 scores representing no childbirth fear, 28-39 scores representing mild childbirth fear, 40-51 scores representing moderate childbirth fear, and 52-64 scores representing severe childbirth fear.
(2) And signal preprocessing, including filtering, denoising and normalizing.
In the embodiment, for the acquired original electrocardiosignals, a second-order Butterworth band-pass filter of 0.05-75Hz is adopted for denoising and baseline drift removal, and a notch filter of 50Hz is used for removing power frequency interference; for the collected original skin electric signals, a second-order Butterworth band-pass filter with the frequency of 0.02-0.5Hz is adopted for denoising and baseline drift removal; then, standardizing the filtered skin electric and electrocardio signals by adopting a Z-score method, wherein the calculation method comprises the following steps:
wherein Normalized (X) is the Normalized signal, mean (X) is the mean of the original signal,
is the standard deviation of the original signal.
(3) And extracting coupling characteristics of the standardized electrocardio and skin electric signals.
The coupling indexes comprise Pearson correlation coefficients, amplitude square coherence functions, mutual information, mutual sample entropy and mutual fuzzy entropy, and the coupling relation between the skin electricity and the electrocardio electricity is analyzed from the time domain, the frequency domain and the nonlinearity. For electrocardio-sequences { X i N = x (i), 1 ≦ i ≦ N } and electrodermal sequence Y j And j is more than or equal to 1 and less than or equal to N, x (i) and y (j) respectively represent each electrocardio and skin electrometric value in the time sequence, N represents the length of the sequence, and the coupling index is obtained by the following method:
(1) pearson correlation coefficient:
wherein the content of the first and second substances,
and
respectively represent X i And Y j The average value of (a) is calculated,
and
respectively represent X i And Y j Standard deviation of (2).
(2) Amplitude squared coherence function:
wherein, P XY (f) Representing the cross-spectral density, P, between two time series XX (f) And P YY (f) Respectively represent X i And Y j MSCF represents the degree of linear correlation between the components of the two time series at each frequency.
(3) Given a reconstruction dimension m, X i And Y j Respectively reconstructing as follows:
definition of
And
the distance between them is:
wherein | represents the maximum norm; given a threshold value r, define
Where Θ (-) is the Heaviside function; increasing the reconstruction dimension m to m +1, calculating
Mutual sample entropy
(4) Given a reconstruction dimension m, X i And Y j Respectively reconstructing as follows:
definition of
And
the distance between them is:
given a threshold value r, define
Comprises the following steps:
wherein the content of the first and second substances,
is a decreasing half Gaussian distribution function;
increasing the reconstruction dimension m to m +1, calculating
Then the cross-fuzzy entropy:
in this embodiment, the parameters of XSampEn and XFuzzyEn are set to reconstruction dimension m =2, and the threshold r =0.2.
(4) And obtaining network learning characteristics of the electrocardio-skin electric signals and the skin electric signals based on a Convolutional Neural Network (CNN) model, and fusing the network learning characteristics with the coupling characteristics. A double-scale convolutional neural network is adopted, and each scale comprises 4 convolutional layers, 2 pooling layers, 2 dropout layers, 1 flat layer and 1 full-connection layer. The first convolutional layer is used to initially extract features and reduce tensor size, the pooling layer and dropout layer are used to downsample the feature tensor and prevent the over-fitting phenomenon, and the following three identical convolutional layers are used to further refine the filter. Then another pooling layer and a flattening layer are applied to further reduce the size of each tensor, and finally the tensors are connected and then passed through a dropout layer, and the tensor size is further reduced through a fully connected layer.
In the present embodiment, a dual-scale network architecture is adopted as shown in fig. 2. The pooling type employs maximum pooling, with the addition of a Batch Normalization (BN) operation and a linear rectification unit (ReLU) per convolutional layer. And (4) after the convolutional neural network features are extracted, fusing with the coupling features in the step (3) to obtain comprehensive features.
(5) And inputting the fused comprehensive characteristics into a bidirectional long-term memory network (biLSTM) model for carrying out birth fear level assessment. The model contains three biLSTM layers and one dense layer. The three biLSTM layers contained a number of neurons in the order of 200,100 and 50, using a Sigmoid activation function, an Adam optimizer and a cross entropy loss function.
In this embodiment, the two-way long and short term memory network architecture is shown in fig. 3: sending the comprehensive characteristics acquired based on electrocardio and electrodermal into the network, and adopting five-fold cross validation, namely averagely dividing the data into 5 equal parts, wherein only 1 part is used as a test set each time, the other 4 parts are used as training sets, and the average value of 5 times of validation is used as a final delivery fear evaluation result. The result shows that the identification accuracy rate of the method for the childbirth fear absence and the childbirth fear is 82.05%.
According to the childbirth fear detection method based on electrocardio and electrodermal joint analysis, on one hand, coupling analysis is introduced to obtain the synchronism information of two body surface signals, and the limitation of a single signal is overcome; on the other hand, the information related to the emotion of the pregnant woman contained in the signal is deeply mined by using a deep learning method, so that the detection accuracy is improved. Compared with the conventional delivery fear detection based on the subjective scale, the method can objectively, accurately and timely evaluate the delivery fear, and is beneficial to timely emotion regulation and psychological persuasion of the pregnant woman, so that the precise management of the pregnancy period is realized.
The above embodiments are only for illustrating the present invention, and are not intended to limit the present invention. It will be understood by those skilled in the art that various combinations, modifications and equivalents of the embodiments of the invention described herein may be made without departing from the spirit and scope of the invention as defined in the claims.
Claims (7)
1. A childbirth fear detection method based on electrocardio and skin-electricity combined analysis is characterized by comprising the following specific steps:
step (1): synchronously collecting original electric signals of the heart and skin of the pregnant woman, and dividing the childbirth fear level of the pregnant woman by a doctor according to a childbirth attitude questionnaire;
step (2): signal preprocessing, including filtering, denoising and standardization;
and (3): extracting coupling characteristics of the standardized electrocardio and skin electric signals;
and (4): obtaining network learning characteristics of the electrocardio-skin electric signals and the skin electric signals based on a Convolutional Neural Network (CNN) model, and fusing the network learning characteristics with the coupling characteristics;
and (5): and inputting the integrated comprehensive characteristics into a bidirectional long-short-term memory network (biLSTM) model to evaluate the class of the fear of childbirth to obtain a classification result of the fear of childbirth.
2. The method for fear of childbirth detection according to claim 1, wherein the fear of childbirth in step (1) is graded as: no fear of childbirth, mild fear of childbirth, moderate fear of childbirth and severe fear of childbirth.
3. The method for fear of childbirth detection of claim 2 wherein the signal preprocessing of step (2) comprises: for the acquired original electrocardiosignals, a second-order Butterworth band-pass filter of 0.05-75Hz is adopted for denoising and baseline drift removal, and a notch filter of 50Hz is used for removing power frequency interference; for the collected original skin electric signals, a second-order Butterworth band-pass filter with the frequency of 0.02-0.5Hz is adopted for denoising and baseline drift removing; then, the filtered skin electric and electrocardio signals are standardized by a Z-score method.
4. The fear of childbirth detection method according to claim 3, wherein the coupling indicators in step (3) include Pearson's correlation coefficient, amplitude squared coherence function, mutual information, mutual sample entropy and mutual fuzzy entropy, and the coupling relationship between the skin current and the electrocardiogram is analyzed from three aspects of time domain, frequency domain and nonlinearity; for cardioelectric sequences { X i N = x (i), 1 ≦ i ≦ N } and electrodermal sequence Y j And j is more than or equal to 1 and less than or equal to N, x (i) and y (j) respectively represent each electrocardio and skin electrometric value in the time sequence, N represents the length of the sequence, and the coupling index is obtained by the following method:
(1) pearson correlation coefficient:
wherein the content of the first and second substances,
and
respectively represent X i And Y j The average value of (a) of (b),
and
respectively represent X i And Y j Standard deviation of (d);
(2) amplitude squared coherence function:
wherein, P XY (f) Representing the cross-spectral density, P, between two time series XX (f) And P γγ (f) Respectively represent X i And Y j The MSCF represents the linear correlation degree between the components of the two time series on each frequency;
(3) given a reconstruction dimension m, X i And Y j Respectively reconstructing as follows:
definition of
And
the distance between them is:
wherein | represents the maximum norm;
given a threshold value r, define
Wherein Θ (-) is a Heaviside function;
increasing the reconstruction dimension m to m +1, calculating
Then the cross-sample entropy is:
(4) given a reconstruction dimension m, X i And Y j Respectively reconstructing as follows:
definition of
And
the distance between them is:
given a threshold value r, define
Comprises the following steps:
wherein the content of the first and second substances,
is a decreasing half Gaussian distribution function;
increasing the reconstruction dimension m to m +1, calculating
The cross-ambiguity entropy is:
5. the fear of childbirth detection method of claim 4 wherein in step (4) the model is a two-scale convolutional neural network; each scale has 4 convolutional layers, 2 pooling layers, 2 dropout layers, 1 flat layer and 1 fully connected layer; the first convolutional layer is used for preliminarily extracting features and reducing tensor size, the pooling layer and the dropout layer are used for down-sampling the feature tensor and preventing the over-fitting phenomenon, and the following three same convolutional layers are used for further improving the filter; then applying another pooling layer and a flattening layer to further reduce the size of each tensor, and finally connecting the tensors together, passing through a dropout layer, and further reducing the tensor size through a full connection layer; and (4) after the network learning characteristic is obtained, fusing the network learning characteristic with the coupling characteristic in the step (3) to obtain a comprehensive characteristic.
6. The fear of childbirth claim 5 wherein the bidirectional stoke network model in step (5) comprises three biLSTM layers and one dense layer; the three biLSTM layers contained a number of neurons in the order of 200,100 and 50, using a Sigmoid activation function, an Adam optimizer and a cross entropy loss function.
7. A childbirth fear detection system based on the method of any one of claims 1-6, comprising five modules, respectively: the pregnant woman's original electrocardio and skin electric signal acquisition module, the signal preprocessing module, the electrocardio and skin electric signal coupling characteristic extraction module, the characteristic learning and fusion module based on the convolutional neural network model, the childbirth fear grade evaluation module based on the bidirectional long-time memory network model; the five modules respectively execute the operation contents of the five steps of the method.
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