CN112244875A - Schizophrenia detection method based on electroencephalogram and deep learning - Google Patents
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Abstract
The invention discloses a method for detecting schizophrenia based on electroencephalogram and deep learning, which comprises the steps of extracting a test sample, converting the obtained test sample into a test input image, inputting the obtained test input image into a deep learning model, extracting robustness characteristics to obtain robustness characteristics, inputting the extracted robustness characteristics into an extreme learning machine classifier, detecting schizophrenia and outputting a detection result. The deep learning model is composed of a convolution neural network and a long-term and short-term memory neural network. The convolutional neural network can utilize the convolutional layer to extract the characteristics, so that proper characteristics do not need to be searched repeatedly, and a large amount of time is saved. Meanwhile, the feature extraction is carried out by utilizing the convolution layer, the accuracy of schizophrenia detection can be improved by changing the size of the convolution kernel, the operation is simple and convenient, the speed is higher, and the accuracy can reach more than 90 percent.
Description
Technical Field
The invention relates to the technical field of schizophrenia detection, in particular to a schizophrenia detection method based on electroencephalogram and deep learning.
Background
Schizophrenia is a psychiatric disease that affects humans throughout life. Its clinical symptoms are complex and diverse, involving perception, thinking, emotion, will behavior and cognitive function. Manifested by delusions and hallucinations, or cognitive deficits, depressed mood and disorganized thought. Since the clinical manifestations of schizophrenia are complex, the definition, criteria and understanding of the diagnosis of schizophrenia symptoms by clinicians vary, leading to misdiagnosis and mistreatment of schizophrenia. Therefore, the method has important significance for the deep research of the schizophrenia detection method and the improvement of the diagnosis accuracy.
Today, there are many methods for the detection of schizophrenia. Most researches explore and detect by using the characteristic of abnormal expression of electroencephalogram signals. There are mainly two methods, one is to visually check abnormality by a doctor through electroencephalogram, and the other is to find characteristics of electroencephalograms of schizophrenic patients and healthy persons and to perform detection by using a classification method such as machine learning. Both methods, however, suffer from the disadvantage of being relatively time consuming and not accurate.
Disclosure of Invention
The invention aims to provide a schizophrenia detection method based on electroencephalogram and deep learning, aiming at the technical defects of time consumption and low precision of the schizophrenia detection method in the prior art.
The technical scheme adopted for realizing the purpose of the invention is as follows:
a schizophrenia detection method based on electroencephalogram and deep learning comprises the following steps:
step A: extracting an original electroencephalogram time sequence signal of a person to be tested, and carrying out noise filtering processing on the original electroencephalogram time sequence signal, wherein the electroencephalogram time sequence signal of the person to be tested after the noise filtering processing is used as a test sample;
and B: converting the test sample obtained in the step A into a test input image;
and C: b, inputting the test input image obtained in the step B into a deep learning model, and extracting robust features in the deep learning model to obtain robust features;
step D: inputting the robustness characteristics extracted in the step C into an Extreme Learning Machine (ELM) classifier, and detecting schizophrenia and outputting a detection result by the extreme learning machine classifier. Reference documents: [1] li, S.Song, and Y.Wan, "Laplacian twist extension learning machine for semi-collaborative classification", neuro-typing, vol.321, pp.17-27,2018.
[2]Y.Yu and Z.Sun,“Sparse coding extreme learning machine for classification”,Neurocomputing,vol.261,pp.50-56,2017.
In the above technical solution, the test sample conversion method in step B is based on a short-time fourier transform algorithm.
In the above technical solution, the short-time fourier transform algorithm is completed by the following formula:
where z (u) is the electroencephalogram time-series signal at time u, g (u-t) is a window function, f is the electroencephalogram time-series signal sampling rate, j is a complex number, j is 1 × i, and i is an imaginary unit.
In the above technical solution, when the obtained test sample is converted into a test input image, a window of 3 seconds is used to perform short-time fourier transform.
In the above technical solution, in step C, the deep learning model is composed of a Convolutional Neural Network (CNN) and a long-short term memory neural network (LSTM). CNN and LSTM are stacked, and the characteristic matrix extracted by CNN is integrated into the matrix which can be input by LSTM. Reference [3] Saintath T N, Vinyals O, Senior A, et al. volumetric, Long Short-Term Memory, full connected Deep Neural Networks [ C ]// ICASSP 2015 + 2015IEEE International Conference on Acoustics, speed and Signal Processing (ICASSP). IEEE,2016.
In the above technical solution, the convolutional neural network has three layers, the first layer convolution kernel size is 3 × (3-20), preferably 3 × 20, the second layer convolution kernel size is 2 × (2-20), preferably 2 × 20, and the third layer convolution kernel size is 2 × (2-20), preferably 2 × 20. When the size of the convolution kernel in the convolution neural network is in the range, the accuracy rate can be ensured to be more than 90%.
In the above technical solution, the long-term and short-term memory neural network has three layers, the first layer is an input layer, the second layer is a hidden layer, the hidden unit of the hidden layer is 100, and the third layer is an output layer.
In the above technical solution, the training method of the deep learning model includes the following steps:
step 1: extracting original electroencephalogram time sequence signals of the schizophrenia patient and the healthy person, and performing noise filtering processing, wherein the electroencephalogram time sequence signals of the schizophrenia patient and the healthy person after the noise filtering processing are used as training samples;
step 2: dividing the training samples extracted in the step 1 into a training set and a verification set; the number of the electroencephalogram time sequences in the training set is 70% of the total number of the electroencephalogram time sequences in the training sample, and the number of the electroencephalogram time sequences in the verification set is 30% of the total number of the electroencephalogram time sequences in the training sample.
And step 3: based on a short-time Fourier transform algorithm, respectively converting the training set and the verification set divided in the step (2) into a training set input image and a verification set input image which can be identified by a convolutional neural network, wherein the training set input image and the verification set input image form a model input image set;
and 4, step 4: and (4) inputting the model input image set obtained by conversion in the step (3) into the deep learning model, extracting robustness characteristics, and training optimal parameters of the deep learning model.
And 5: and (4) outputting the optimal parameters of the deep learning model trained in the step (4).
In the above-described embodiment, in step D, the output detection result indicates whether schizophrenia is present or absent.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention provides a schizophrenia detection method based on electroencephalogram and deep learning. The convolutional neural network can utilize the convolutional layer to extract the characteristics, so that proper characteristics do not need to be searched repeatedly, and a large amount of time is saved.
2. The method for detecting the schizophrenia based on the electroencephalogram and the deep learning, provided by the invention, has the advantages that the characteristic extraction is carried out by utilizing the convolution layer, the accuracy of the schizophrenia detection can be improved by changing the size of the convolution kernel, the operation is simple and convenient, the speed is higher, and the accuracy can be up to more than 90%.
3. According to the method for detecting the schizophrenia based on the electroencephalogram and the deep learning, the long-term and short-term memory neural network can judge the time dependence on the basis of the features extracted by the convolutional layer to further extract the robustness features.
Drawings
Fig. 1 is a flowchart of a schizophrenia detection method based on electroencephalogram and deep learning.
FIG. 2 is a diagram showing the comparison of the brain electrical time series signals of schizophrenia and healthy people.
Figure 3 shows a 3s window short time fourier transform plot of schizophrenia versus healthy persons.
FIG. 4 shows a block diagram of CNN-LSTM-ELM.
Detailed Description
The present invention will be described in further detail with reference to specific examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
A deep learning model is composed of a convolution neural network and a long-short term memory neural network. The convolutional neural network has three layers, the size of a convolution kernel of the first layer is 3X20, the size of a convolution kernel of the second layer is 2X20, and the size of a convolution kernel of the third layer is 2X 20. The long-term and short-term memory neural network comprises three layers, wherein the first layer is an input layer, the second layer is a hidden layer, a hidden unit arranged in the hidden layer is 100, and the third layer is an output layer.
The training method of the deep learning model comprises the following steps:
step 1: extracting training samples
Extracting original electroencephalogram time sequence signals of 45 schizophrenia patients and 39 healthy persons, and performing noise filtering processing, wherein the electroencephalogram time sequence signals of the schizophrenia patients and the healthy persons after the noise filtering processing are used as training samples.
Step 2: partitioning training samples
Dividing the training sample extracted in the step 1 into a training set and a verification set, wherein the number of the electroencephalogram time sequences in the training set is 70% of the total number of the electroencephalogram time sequences in the training sample, and the number of the electroencephalogram time sequences in the verification set is 30% of the total number of the electroencephalogram time sequences in the training sample.
And step 3: transforming a model input image set
And (3) respectively converting the training set and the verification set divided in the step (2) into a training set input image and a verification set input image which can be identified by the convolutional neural network (the image format which can be identified by the convolutional neural network is a two-dimensional matrix format) based on a short-time Fourier transform algorithm, wherein the training set input image and the verification set input image form a model input image set.
And 4, step 4: and (4) inputting the model input image set obtained by conversion in the step (3) into the deep learning model, extracting robustness characteristics, and training optimal parameters of the deep learning model.
And 5: and (4) outputting the optimal parameters of the deep learning model trained in the step (4).
Example 2
A schizophrenia detection method based on electroencephalogram and deep learning comprises the following steps:
step A: extracting an original electroencephalogram time sequence signal of a person to be tested, and carrying out noise filtering processing on the original electroencephalogram time sequence signal, wherein the electroencephalogram time sequence signal of the person to be tested after the noise filtering processing is used as a test sample;
and B: converting the test sample obtained in the step A into a test input image which can be identified by a convolutional neural network based on a short-time Fourier transform algorithm;
and C: inputting the test input image obtained in the step B into the deep learning model trained in the embodiment 1, and extracting the robustness characteristics in the trained deep learning model to obtain the robustness characteristics;
step D: and D, inputting the robustness features extracted in the step C into an extreme learning machine classifier, and detecting schizophrenia and outputting a detection result by the extreme learning machine classifier.
Example 3
This example is based on example 2 and describes the detailed method or preference thereof.
In step 3 of example 1 and step B of example 2, the short-time fourier transform algorithm is completed by the following formula:
where z (u) is the electroencephalogram time-series signal at time u, g (u-t) is a window function, f is the electroencephalogram time-series signal sampling rate, j is a complex number, j is 1 × i, and i is an imaginary unit.
In step B, when the obtained test sample is converted into a test input image, short-time fourier transform is performed using a window of 3 seconds.
In the step D, the output detection result is that whether the testee has schizophrenia or does not have schizophrenia is judged according to the electroencephalogram time sequence signal.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A schizophrenia detection method based on electroencephalogram and deep learning is characterized in that: the method comprises the following steps:
step A: extracting an original electroencephalogram time sequence signal of a person to be tested, and carrying out noise filtering processing on the original electroencephalogram time sequence signal, wherein the electroencephalogram time sequence signal after the noise filtering processing is used as a test sample;
and B: converting the test sample obtained in the step A into a test input image;
and C: inputting the test input image obtained in the step B into a trained deep learning model, and extracting robustness features in the deep learning model to obtain robustness features;
step D: and D, inputting the robustness features extracted in the step C into an extreme learning machine classifier, and detecting schizophrenia and outputting a detection result by the extreme learning machine classifier.
2. The method for detecting schizophrenia as set forth in claim 1, wherein: in the step B, the test sample conversion method is based on a short-time Fourier transform algorithm.
3. The method for detecting schizophrenia as set forth in claim 2, wherein: the short-time Fourier transform algorithm is completed by the following formula:
where z (u) is the electroencephalogram time-series signal at time u, g (u-t) is a window function, f is the electroencephalogram time-series signal sampling rate, j is a complex number, j is 1 × i, and i is an imaginary unit.
4. The method for detecting schizophrenia as set forth in claim 3, wherein: when the obtained test sample is converted into a test input image, short-time fourier transform is performed using a window of 3 seconds.
5. The method for detecting schizophrenia as set forth in claim 1, wherein: in the step C, the deep learning model is composed of a convolution neural network and a long-short term memory neural network.
6. The method for detecting schizophrenia as set forth in claim 5, wherein: the convolutional neural network has three layers, the first layer convolution kernel size is 3x (3-20), preferably 3x20, the second layer convolution kernel size is 2x (2-20), preferably 2x20, and the third layer convolution kernel size is 2x (2-20), preferably 2x 20.
7. The method for detecting schizophrenia as set forth in claim 5, wherein: the long-term and short-term memory neural network comprises three layers, wherein the first layer is an input layer, the second layer is a hidden layer, the hidden unit arranged in the hidden layer is 50-200, and the third layer is an output layer.
8. The method for detecting schizophrenia as set forth in claim 7, wherein: the training method of the deep learning model comprises the following steps:
step 1: extracting original electroencephalogram time sequence signals of the schizophrenia patient and the healthy person, and performing noise filtering processing, wherein the electroencephalogram time sequence signals of the schizophrenia patient and the healthy person after the noise filtering processing are used as training samples;
step 2: dividing the training samples extracted in the step 1 into a training set and a verification set;
and step 3: based on a short-time Fourier transform algorithm, respectively converting the training set and the verification set divided in the step (2) into a training set input image and a verification set input image which can be identified by a convolutional neural network, wherein the training set input image and the verification set input image form a model input image set;
and 4, step 4: inputting the model input image set obtained by conversion in the step 3 into the deep learning model, extracting robustness characteristics, and training optimal parameters of the deep learning model;
and 5: and (4) outputting the optimal parameters of the deep learning model trained in the step (4) and applying the optimal parameters to the deep learning model in the testing stage.
9. The method for detecting schizophrenia as set forth in claim 7, wherein: in step 2, the number of the electroencephalogram time sequences in the training set is 70% of the total number of the electroencephalogram time sequences in the training sample, and the number of the electroencephalogram time sequences in the verification set is 30% of the total number of the electroencephalogram time sequences in the training sample.
10. The method for detecting schizophrenia as set forth in claim 1, wherein: in step D, the output detection result is whether schizophrenia is output or not.
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