CN114869298A - Depression detection method and system based on electroencephalogram signals and storage medium - Google Patents
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Abstract
The invention discloses a depression detection method, a depression detection system and a storable medium based on an electroencephalogram signal, and relates to the technical field of electroencephalogram signal processing, wherein the method comprises the following steps: acquiring an original electroencephalogram signal; preprocessing the original electroencephalogram signals to obtain a plurality of frequency band data; extracting features from the frequency band data, and performing dimensionality reduction on the features; acquiring an original training set and an original test set, preprocessing the training set, and extracting corresponding training set characteristics and test set characteristics; the method can effectively extract the features in the electroencephalogram signals and carry out recognition classification calculation, reduces the calculation complexity and improves the recognition rate of depression detection.
Description
Technical Field
The invention relates to the technical field of electroencephalogram signal processing, in particular to a depression detection method and system based on electroencephalogram signals and a storage medium.
Background
At present, in the current society with great competitive pressure, the incidence of depression is high, and the depression can damage the living ability of individuals. Therefore, early detection of depression is crucial to improve the quality of life of patients with depression.
However, the detection and classification of the depression degree lacks objective evaluation criteria in clinical practice, cannot reflect the potential and real mental state of a tested person, and influences subsequent judgment, and the electroencephalogram signal can directly reflect the working state in the brain, so that the identification accuracy of depression detection based on the electroencephalogram signal in the prior art is low.
Therefore, how to provide a depression detection method capable of solving the above problems is a problem that needs to be solved urgently by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides a depression detection method, system and storage medium based on electroencephalogram signals, which can effectively extract features in the electroencephalogram signals and perform recognition classification calculation, reduce calculation complexity and improve the recognition rate of depression detection.
In order to achieve the purpose, the invention adopts the following technical scheme:
a depression detection method based on electroencephalogram signals comprises the following steps:
acquiring an original electroencephalogram signal;
preprocessing the original electroencephalogram signal to obtain a plurality of frequency band data;
extracting features from the frequency band data, and performing dimensionality reduction on the features;
acquiring an original training set and an original test set, preprocessing the training set, and extracting corresponding training set characteristics and test set characteristics;
fusing the training set characteristics and the test set characteristics by using an optimal transmission method to obtain an optimal transmission coefficient matrix;
reconstructing the features by using an optimal transmission coefficient matrix;
and classifying the reconstruction result to realize depression detection.
Preferably, the specific process of extracting features from the plurality of frequency band data includes:
and extracting approximate entropy characteristics, spectrum entropy characteristics, Hjorth Activity characteristics, Mobility characteristics and Complexity characteristics from the frequency band data.
Preferably, the specific expression for obtaining the optimal transmission coefficient matrix is as follows:
wherein < gamma, D > F The Frobenius norm of a dot product of a transmission coefficient matrix gamma and a cost matrix D to be optimized is represented, wherein D is a Riemann distance matrix between samples of a training set and a testing set, the scale of the matrix is ns-nt, and D is ij ＝RD(y tr (i),y te (j) Row i, column j of the training set, i.e., sample y tr (i) And the jth sample y in the test set te (j) The Euclidean distance of the transmission coefficient matrix to be optimized is consistent with the scale D, and is ns x nt, the lambda is a balance parameter and is between 0.01 and 0.1,
which is the square of the 1 norm of λ, is used to make λ a sparse matrix, i.e., the larger λ, the more elements of the transmission coefficient matrix γ to be optimized are required to be 0.
Preferably, the specific process of classifying the reconstruction result includes:
inputting a sample to be classified, the training set and a label corresponding to the training set;
clustering the labels to obtain a plurality of clustering centers;
and acquiring Euclidean distances between the test set and a plurality of clustering centers to realize classification.
Furthermore, the invention also provides a detection system using the electroencephalogram signal-based depression detection method, which comprises an electroencephalogram signal acquisition module, an electroencephalogram signal preprocessing module, a feature extraction module, a reconstruction module and a depression detection classification module which are sequentially connected;
the electroencephalogram signal acquisition module is used for acquiring an original electroencephalogram signal, the electroencephalogram signal preprocessing module is used for preprocessing the original electroencephalogram signal to obtain a plurality of frequency band data, the feature extraction module is used for extracting features from the plurality of frequency band data and performing dimensionality reduction processing on the features, the reconstruction module is used for acquiring an original training set and an original test set, preprocessing the training set and extracting corresponding training set features and test set features, fusing the training set features and the test set features by using an optimal transmission method to obtain an optimal transmission coefficient matrix and reconstructing the features by using the optimal transmission coefficient matrix, and the depression detection classification module is used for classifying the reconstruction results to realize depression detection.
Further, the present invention also provides a computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, realizes the depression detection method according to any one of the above.
According to the technical scheme, compared with the prior art, the depression detection method, the depression detection system and the storage medium based on the electroencephalogram signals are provided, aiming at the problems that the electroencephalogram signals are low in recognition rate in clinical depression detection and multi-lead electroencephalogram signal information redundancy and calculation complexity are high, the information redundancy caused by the multi-lead electroencephalogram signals is effectively removed, the calculation complexity is reduced, and the depression detection recognition rate is improved. The invention also solves the problems of poor interpretability, poor recognition effect, insufficient consideration to lead combination, high calculation loss, easy overfitting and the like of the existing lead selection method.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is an overall flowchart of a method for detecting depression based on electroencephalogram signals according to the present invention;
fig. 2 is a structural schematic block diagram of a depression detection system based on electroencephalogram signals provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to the attached drawing 1, the embodiment of the invention discloses a depression detection method based on electroencephalogram signals, which comprises the following steps:
acquiring original brain electrical signals by using brain electrical electrodes for collection, wherein the placement positions are according to the international 10-20 standard, three forehead electrodes are FP1, FPz and FP2 respectively, the brain electrical signals sampled by each channel are x 1 (n)、x 2 (n)、x 3 (n), the sampling frequency may be set to 250;
preprocessing an original electroencephalogram signal to obtain a plurality of frequency band data, wherein in the preprocessing process, a 5-order Butterworth filter is mainly used for frequency division filtering, the frequency bands are divided into B1-B6, 0.5-4Hz, 4-8Hz, 8-12Hz, 12-16Hz, 16-24Hz, 24-45Hz, and all frequency band B0 is a signal of 0.5-45 Hz;
extracting characteristics from the data of the multiple frequency bands, and performing dimensionality reduction on the characteristics, wherein the dimensionality reduction method mainly adopts a Principal Component Analysis (PCA) to reduce the dimensionality by 21 dimensions;
acquiring an original training set and an original test set, preprocessing the training set, and extracting corresponding training set characteristics and test set characteristics;
fusing the training set characteristics and the test set characteristics by using an optimal transmission method to obtain an optimal transmission coefficient matrix;
reconstructing the characteristics by using the optimal transmission coefficient matrix;
and classifying the reconstructed result to realize depression detection.
In a specific embodiment, the specific process of extracting features from the multiple frequency band data includes:
and extracting approximate entropy characteristics, spectrum entropy characteristics, Hjorth activity characteristics, mobility characteristics and Complexity characteristics from the frequency band data.
Specifically, the approximate entropy features total 21 features, and the obtaining process may include the following steps:
setting an N-dimensional time sequence u (1), u (2), u (N) obtained by sampling at equal time intervals;
defining algorithm related parameters m and r, wherein m is an integer to represent the length of a comparison vector, and r is a real number to represent a metric value of 'similarity';
reconstructing an m-dimensional vector X (1), X (2), and X (N-m +1), wherein X (i) is [ X (i), X (i +1), and X (i + m-1) ], and X (i) is the ith sampling value of the electroencephalogram signal;
for i is more than or equal to 1 and less than or equal to N-m +1, counting the number of vectors meeting the following conditions;
wherein, { d [ X (i), X * (j)]R is equal to or less than r, d [ X, X ] represents the number of elements satisfying the condition * ]Is defined as
X (i) is an element of the vector X, d represents the distance between the vector X (i) and the vector X (j), the two vectors X (i) and X (j) traverse corresponding elements to calculate a difference value, the largest one is selected, j and i have the value range of 1 ≦ j ≦ N-m +1, and j ≦ i is included;
definition of
The approximate entropy is defined as:
ApEn＝Φ m (r)-Φ m+1 (r)。
the total characteristic number of the spectrum entropy characteristics is 3, and the corresponding fourier transform result s (n) is obtained for each channel signal x (n), so that the spectrum probability distribution of the signal is as follows:
the corresponding spectral entropy of the signal is:
h _ sport Activity, Mobility, Complexity characteristics are respectively obtained from 7 frequency bands B0-B6 of each channel, and 3 × 7 × 3 ═ 63 characteristics are obtained, wherein:
Activity(x(n))＝var(x(n))
Specifically, the extracted 21+21+3+63 features are reduced to 21 dimensions by applying a Principal Component Analysis (PCA) method, the feature vector corresponding to the ith electroencephalogram sample x is set to y (i) (1, 2.... N), N is the total sample number, and y (i) is the feature vector of 21 dimensions.
In a specific embodiment, a specific expression for obtaining the optimal transmission coefficient matrix is as follows:
wherein < gamma, D > F Frobenius norm representing the dot product of a transmission coefficient matrix gamma to be optimized and a cost matrix D, where D is a Riemann distance matrix between training set and test set samples, the scale of the matrix is ns nt, where D ij ＝dist(y tr (i),y te (j) Is the ith row and jth column element, i.e., the ith sample y in the training set tr (i) And the jth sample y in the test set te (j) The metric γ of the transmission coefficient matrix to be optimized is the same as D, which is ns nt, λ is a balance parameter, between 0.01 and 0.1,
which is the square of the 1 norm of λ, is used to make λ a sparse matrix, i.e., the larger λ, the more elements of the transmission coefficient matrix γ to be optimized are required to be 0.
Specifically, for reconstructing the features, see the following formula:
the ith sample y of the test set in equation te (i) The result after reconstruction is
In a specific embodiment, the specific process of classifying the reconstructed result includes:
inputting a sample to be classified, a training set and a label corresponding to the training set;
clustering the labels to obtain a plurality of clustering centers;
and acquiring Euclidean distances between the test set and a plurality of clustering centers to realize classification.
Specifically, 1) inputting a training set y tr (i) Ns, which is the total number of samples in training, and corresponding labels, including all samples from healthy controls (0), mild depression (1), moderate depression (2), and major depression (3) patients. Test set y tr (i) (i 1.. nt), nt being the total number of samples tested, the tag is to be determined.
2) Samples of healthy control (0), mild depression (1), moderate depression (2) and severe depression (3) were clustered using the Kmeans clustering method, respectively, into N0, N1, N2, N3 cluster centers, respectively, representing samples typical of healthy control (0), mild depression (1), moderate depression (2) and severe depression (3).
3) Respectively calculating Euclidean distances between the test set (i 1.. ent. nt) and the centers, sequencing the Euclidean distances from small to large, judging that the test set is healthy if more healthy centers exist in the first 5 small distances, and marking the test set as 0; if the mild depression has more centers, the mild depression is judged to be mild depression, the label is 1, and the rest can be done in the same way.
Referring to fig. 2, an embodiment of the present invention further provides a detection system using the electroencephalogram signal-based depression detection method according to any one of the above embodiments, including an electroencephalogram signal acquisition module, an electroencephalogram signal preprocessing module, a feature extraction module, a reconstruction module, and a depression detection classification module, which are connected in sequence;
the electroencephalogram signal acquisition module is used for acquiring an original electroencephalogram signal, the electroencephalogram signal preprocessing module is used for preprocessing the original electroencephalogram signal to obtain a plurality of frequency band data, the feature extraction module is used for extracting features from the plurality of frequency band data and performing dimension reduction processing on the features, the reconstruction module is used for acquiring an original training set and an original test set, preprocessing the training set and extracting corresponding training set features and test set features, the training set features and the test set features are fused by using an optimal transmission method to obtain an optimal transmission coefficient matrix, the features are reconstructed by using the optimal transmission coefficient matrix, and the depression detection classification module is used for classifying reconstruction results to realize depression detection.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, and when executed by a processor, the computer program implements the depression detection method according to any one of the above embodiments.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. A depression detection method based on electroencephalogram signals is characterized by comprising the following steps:
acquiring an original electroencephalogram signal;
preprocessing the original electroencephalogram signals to obtain a plurality of frequency band data;
extracting features from the frequency band data, and performing dimensionality reduction on the features;
acquiring an original training set and an original test set, preprocessing the training set, and extracting corresponding training set characteristics and test set characteristics;
fusing the training set characteristics and the test set characteristics by using an optimal transmission method to obtain an optimal transmission coefficient matrix;
reconstructing the features by using an optimal transmission coefficient matrix;
and classifying the reconstruction result to realize depression detection.
2. The electroencephalogram signal-based depression detection method according to claim 1, wherein the specific process of extracting features from the plurality of frequency band data comprises:
and extracting approximate entropy characteristics, spectrum entropy characteristics, Hjorth Activity characteristics, Mobility characteristics and Complexity characteristics from the frequency band data.
3. The electroencephalogram signal-based depression detection method according to claim 1, wherein a specific expression for obtaining an optimal transmission coefficient matrix is as follows:
wherein < gamma, D > F Frobenius norm representing the dot product of a transmission coefficient matrix gamma to be optimized and a cost matrix D, where D is a Riemann distance matrix between training set and test set samples, the scale of the matrix is ns nt, where D ij ＝RD(y tr (i),y te (j) Row i and column j) of the training set, i.e., the ith sample y in the training set tr (i) And the jth sample y in the test set te (j) The Euclidean distance of the transmission coefficient matrix to be optimized is consistent with the scale D, and is ns x nt, the lambda is a balance parameter and is between 0.01 and 0.1,
which is the square of the 1 norm of λ, is used to make λ a sparse matrix, i.e., the larger λ, the more elements of the transmission coefficient matrix γ to be optimized are required to be 0.
4. The electroencephalogram signal-based depression detection method according to claim 3, wherein the specific process of classifying the reconstruction results comprises:
inputting a sample to be classified, the training set and a label corresponding to the training set;
clustering the labels to obtain a plurality of clustering centers;
and acquiring Euclidean distances between the test set and a plurality of clustering centers to realize classification.
5. A detection system using the electroencephalogram signal-based depression detection method as claimed in any one of claims 1 to 4, characterized by comprising an electroencephalogram signal acquisition module, an electroencephalogram signal preprocessing module, a feature extraction module, a reconstruction module and a depression detection classification module which are connected in sequence;
the electroencephalogram signal acquisition module is used for acquiring an original electroencephalogram signal, the electroencephalogram signal preprocessing module is used for preprocessing the original electroencephalogram signal to obtain a plurality of frequency band data, the feature extraction module is used for extracting features from the plurality of frequency band data and performing dimensionality reduction processing on the features, the reconstruction module is used for acquiring an original training set and an original test set, preprocessing the training set and extracting corresponding training set features and test set features, fusing the training set features and the test set features by using an optimal transmission method to obtain an optimal transmission coefficient matrix and reconstructing the features by using the optimal transmission coefficient matrix, and the depression detection classification module is used for classifying the reconstruction results to realize depression detection.
6. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a depression detection method according to any one of claims 1 to 4.
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