CN113180660A - Method and system for detecting depression state based on EEG signal - Google Patents
Method and system for detecting depression state based on EEG signal Download PDFInfo
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Abstract
The invention discloses a depression state detection method and system based on an EEG signal, wherein the method comprises the following steps: the BCI equipment collects an EEG signal of a user and sends the EEG signal to a data analysis system; and the data analysis system extracts the electroencephalogram alertness maintaining time of the user from the EEG signal to be used as a depression state characteristic value, and inputs the depression state characteristic value into the classification algorithm model for identification and classification. The method improves the detection efficiency and reduces the labor cost.
Description
Technical Field
The invention relates to the technical field of brain waves, in particular to a depression state detection method and system based on an EEG signal.
Background
At present, depression is screened manually, judgment of depression is carried out by carrying out psychological tests and conversations on people to be detected, and perhaps the degree of depression is judged, so that depression detection needs a lot of time and labor cost, and a mode of automatically identifying depression does not exist at present, so that the detection efficiency is low and a lot of labor cost is needed.
Therefore, how to improve the detection efficiency and reduce the labor cost is an urgent problem to be solved.
Disclosure of Invention
The invention aims to provide a depression state detection method and system based on an EEG signal so as to improve detection efficiency and reduce labor cost.
In order to solve the above technical problem, the present invention provides a method for detecting a depression state based on an EEG signal, comprising:
the BCI equipment collects an EEG signal of a user and sends the EEG signal to a data analysis system;
and the data analysis system extracts the electroencephalogram alertness maintaining time of the user from the EEG signal to be used as a depression state characteristic value, and inputs the depression state characteristic value into the classification algorithm model for identification and classification.
Preferably, before the data analysis system extracts the electroencephalogram alert duration of the user from the EEG signal, the data analysis system further includes:
the EEG signal is filtered to obtain a clean EEG signal.
Preferably, the data analysis system extracts the electroencephalogram alert maintenance duration of the user from the EEG signal, and includes:
recognizing electroencephalogram dominant waves from the EEG signals;
and extracting the electroencephalogram alertness maintaining time of the user from the electroencephalogram dominant wave.
Preferably, the identifying the electroencephalogram dominant wave from the EEG signal comprises:
carrying out frequency domain conversion on the EEG signal to obtain the energy value contrast ratio of alpha waves with different frequencies;
and (3) identifying the electroencephalogram dominant wave from the EEG signal by utilizing the energy value contrast ratio of alpha waves with different frequencies.
Preferably, after the BCI device acquires the EEG signal of the user, the method further includes:
and amplifying and performing digital-to-analog conversion on the EEG signal to obtain a converted EEG signal.
Preferably, the frequency domain converting the EEG signal to obtain the energy value contrast ratio of different frequency alpha waves includes:
and carrying out Fourier transform or wavelet transform on the EEG signal to obtain the energy value contrast ratio of alpha waves with different frequencies.
Preferably, the extracting the electroencephalogram alert maintenance duration of the user from the electroencephalogram dominant wave includes:
and calculating the duration time of the electroencephalogram dominant wave, and evaluating the duration time to obtain the electroencephalogram alertness maintaining duration of the user based on the time length.
Preferably, after inputting the characteristic value of the depression state into the classification algorithm model for identification and classification, the method further comprises:
and acquiring a depression state result, and sending the depression state result to the terminal for displaying.
The invention also provides a depression state detection system based on the EEG signal, which is used for realizing the method and comprises the following steps:
the BCI equipment is used for collecting an EEG signal of a user and sending the EEG signal to the data analysis system;
and the data analysis system is used for extracting the electroencephalogram alertness maintaining time of the user from the EEG signal to be used as a depression state characteristic value, and inputting the depression state characteristic value into the classification algorithm model for identification and classification.
According to the method and the system for detecting the depression state based on the EEG signals, provided by the invention, the electroencephalogram alertness maintaining time of a user is extracted from the EEG signals, the electroencephalogram alertness maintaining time is taken as a depression state characteristic value, namely the electroencephalogram alertness maintaining time is taken as a judgment parameter of the depression state, the electroencephalogram alertness maintaining time is identified and classified through a classification algorithm model, and the depression state result of the user is obtained by utilizing a classification algorithm, so that the depression state detection is automatically carried out, manual participation is not needed, a large amount of time is not needed, the detection efficiency is improved, and the labor cost is reduced.
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 a flowchart of a method for detecting a depression state based on an EEG signal according to the present invention;
FIG. 2 is a flow chart of the portable BCI device operation;
fig. 3 is a schematic structural diagram of a system for detecting a depression state based on an EEG signal according to the present invention.
Detailed Description
The core of the invention is to provide a depression state detection method and system based on an EEG signal so as to improve the detection efficiency and reduce the labor cost.
In order to make the technical solutions of the present invention better understood, 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 fig. 1, fig. 1 is a flowchart of a method for detecting a depression state based on an EEG signal, the method including the following steps:
s11: the BCI equipment collects an EEG signal of a user and sends the EEG signal to a data analysis system;
s12: and the data analysis system extracts the electroencephalogram alertness maintaining time of the user from the EEG signal to be used as a depression state characteristic value, and inputs the depression state characteristic value into the classification algorithm model for identification and classification.
Therefore, in the method, the electroencephalogram alertness maintaining time of the user is extracted from the EEG signal, the electroencephalogram alertness maintaining time is used as a characteristic value of the depression state, namely the electroencephalogram alertness maintaining time is used as a judgment parameter of the depression state, and the electroencephalogram alertness maintaining time is identified and classified through a classification algorithm model, so that the depression state result of the user can be obtained, the depression state is automatically detected, manual participation is not needed, a large amount of time is not needed, the detection efficiency is improved, and the labor cost is reduced.
Among them, EEG (Electroencephalogram) signals are patterns obtained by recording spontaneous biopotentials of the brain from the scalp by amplifying them with a precision electronic instrument, and are spontaneous and rhythmic electrical activities of brain cell groups recorded by electrodes. There are conventional electroencephalograms, dynamic electroencephalogram monitoring, video electroencephalogram monitoring. BCI (Brain Computer Interface) equipment is capable of acquiring Brain wave signals, i.e., EEG signals.
Based on step S12, further, in step S12, the data analysis system filters the EEG signal to obtain a clean EEG signal before extracting the electroencephalogram alert duration of the user from the EEG signal. The data analysis system extracts the user's electroencephalogram alert maintenance duration from the clean EEG signal.
Based on step S12, further, the process of extracting the electroencephalogram alert duration of the user from the EEG signal by the data analysis system specifically includes the following steps:
s21: recognizing electroencephalogram dominant waves from the EEG signals;
s22: and extracting the electroencephalogram alertness maintaining time of the user from the electroencephalogram dominant wave.
Based on step S21, the process of step S21 specifically includes the steps of:
s31: carrying out frequency domain conversion on the EEG signal to obtain the energy value contrast ratio of alpha waves with different frequencies;
the frequency domain conversion comprises Fourier transform or wavelet transform, and the Fourier transform or wavelet transform is carried out on the EEG signals to obtain the energy value contrast ratios of alpha waves with different frequencies.
S32: and (3) identifying the electroencephalogram dominant wave from the EEG signal by utilizing the energy value contrast ratio of alpha waves with different frequencies.
Based on step S22, the process of step S22 specifically includes: and calculating the duration time of the electroencephalogram dominant wave, and evaluating the duration time to obtain the electroencephalogram alertness maintaining duration of the user based on the time length.
Based on step S11, further, after the BCI device collects the EEG signal of the user, the EEG signal is amplified and subjected to digital-to-analog conversion, so as to obtain a converted EEG signal. The data analysis system extracts the electroencephalogram alert maintenance duration of the user from the converted EEG signal.
In step S12, the data analysis system inputs the characteristic values of the depression state into the classification algorithm model for identification and classification, and then obtains the result of the depression state, and sends the result of the depression state to the terminal for display.
In step S12, the depression status feature value is input to the classification algorithm model for identification and classification, and in detail, the depression status feature value is compared with the model threshold value according to the size of the depression status feature value, so as to determine whether the depression level of the user is normal, mild, moderate or severe depression. And after obtaining the depression state result of the user, sending the depression state result to the mobile terminal for displaying. And when detecting that the user is in moderate or severe depression at present, sending early warning information for seeking medical advice in time to the user.
The classification algorithm model comprises an SVM (support vector machine), a decision tree, a KNN (K nearest neighbor) algorithm, a random forest, a naive Bayes classification, a least square method or a logistic regression. In addition, the user data characteristic value can be transmitted back to the background database, and the user data characteristic value is brought into the algorithm model for automatic correction, so that a more accurate normal standard is established.
Fig. 2 is a flow chart of the portable BCI device operation. The BCI device comprises an EEG device, and particularly EEG signals are acquired from the EEG device. According to the method, the depression degree of the classified users is automatically identified by the aid of the portable BCI equipment and an artificial intelligence algorithm, detection efficiency is improved, and application scenes are widened.
The method can automatically detect the depression degree of the user in real time through an artificial intelligence algorithm, can extract characteristic values from frontal lobe electroencephalogram activity based on portable EEG equipment to detect depression, and is convenient for the user to use. And depression detection based on the user's resting state EEG signal can reduce the time cost and manpower cost of detection. The EEG signals of the user are collected through a special high-precision EEG signal collecting device.
Regarding EEG equipment, EEG equipment electrode is the dry electrode, and main symmetric distribution is in prefrontal lobe or frontal lobe, and the electrode point position bilateral symmetry distributes, and single channel electrode has higher sampling rate, can satisfy the accurate portrayal to the EEG signal. The device collects the electroencephalogram signals of the resting eye-closing state of the user, the collection time is freely set, and the preferred time is 15 minutes.
Regarding the EEG signal, based on the method, the specific implementation flow is as follows:
1. filtering the original data by adopting a filtering algorithm, and filtering high-frequency artifacts and low-frequency artifacts in the original data, power frequency interference, eye electrical noise and the like to obtain a pure electroencephalogram signal;
2. fourier transform or wavelet transform is carried out on the frontal lobe electroencephalogram signals, the energy value contrast ratio of alpha waves with different frequencies is calculated, and electroencephalogram dominant waves are identified;
wherein, the high alpha wave range is: 7-10Hz, low alpha wave range: 10-13 Hz;
3. calculating the duration time of the dominant wave, evaluating the electroencephalogram alertness maintaining duration of the user, and extracting the duration time as a characteristic value;
4. sending the characteristic values into an algorithm model for model identification and classification, and detecting the depression degree of the user at the moment;
5. comparing the characteristic value with a model threshold value according to the characteristic value, and judging whether the depression level of the user is normal, mild, moderate or severe depression;
6. and the algorithm model sends the classification result to the mobile phone APP in a network form, and feeds back the visual depression degree to the user.
The classification result sending mode is network sending, and specifically, a report is sent in a mobile data or WiFi mode. The feedback mode of the APP to the user can be a text mode, a chart mode or a voice mode, and the detection result is presented in a mode of combining text and the chart mode.
In addition, when detecting that the user is currently in moderate or severe depression, early warning information for seeking medical advice in time is sent to the user. The early warning information can be sent in a mode of directly reminding through an APP interface or in a mode of reminding through a short message. Preferably, the method and the device adopt a mode of image-text reminding through a mobile phone APP interface to give an early warning prompt to the user. And moreover, the data characteristics of the user are transmitted back to the background database and are brought into the algorithm model for automatic correction, a more accurate normal standard is established, and the classification accuracy of the algorithm model on the depression degree is improved.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a system for detecting a depression state based on an EEG signal, which is provided by the present invention and is used for implementing the method for detecting a depression state based on an EEG signal, including:
the BCI equipment 101 is used for collecting an EEG signal of a user and sending the EEG signal to a data analysis system;
and the data analysis system 102 is used for extracting the electroencephalogram alertness maintaining time of the user from the EEG signal as a depression state characteristic value, and inputting the depression state characteristic value into the classification algorithm model for identification and classification.
Therefore, the system extracts the electroencephalogram alertness maintaining time of the user from the EEG signal, takes the electroencephalogram alertness maintaining time as a characteristic value of the depression state, namely takes the electroencephalogram alertness maintaining time as a judgment parameter of the depression state, and identifies and classifies the electroencephalogram alertness maintaining time through a classification algorithm model, so that the depression state result of the user can be obtained, the depression state detection is automatically carried out, manual participation is not needed, a large amount of time is not needed, the detection efficiency is improved, and the labor cost is reduced.
For the introduction of the system for detecting a depression state based on an EEG signal provided by the present invention, please refer to the aforementioned embodiment of the method for detecting a depression state based on an EEG signal, which is not described herein again. The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The present invention provides a method and a system for detecting a depression state based on an EEG signal. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (9)
1. A method for detecting a depression state based on an EEG signal, comprising:
the BCI equipment collects an EEG signal of a user and sends the EEG signal to a data analysis system;
and the data analysis system extracts the electroencephalogram alertness maintaining time of the user from the EEG signal to be used as a depression state characteristic value, and inputs the depression state characteristic value into the classification algorithm model for identification and classification.
2. The method of claim 1, wherein prior to said data analysis system extracting the user's electroencephalogram alertness duration from the EEG signal, further comprising:
the EEG signal is filtered to obtain a clean EEG signal.
3. The method of claim 1, wherein the data analysis system extracts a user's electroencephalogram alert hold duration from an EEG signal, comprising:
recognizing electroencephalogram dominant waves from the EEG signals;
and extracting the electroencephalogram alertness maintaining time of the user from the electroencephalogram dominant wave.
4. The method of claim 3, wherein said identifying a dominant wave of brain electricity from an EEG signal comprises:
carrying out frequency domain conversion on the EEG signal to obtain the energy value contrast ratio of alpha waves with different frequencies;
and (3) identifying the electroencephalogram dominant wave from the EEG signal by utilizing the energy value contrast ratio of alpha waves with different frequencies.
5. The method of claim 1, wherein after the BCI device acquires the user's EEG signals, further comprising:
and amplifying and performing digital-to-analog conversion on the EEG signal to obtain a converted EEG signal.
6. The method of claim 4, wherein said frequency domain converting the EEG signal to obtain a ratio of energy values of different frequency alpha waves comprises:
and carrying out Fourier transform or wavelet transform on the EEG signal to obtain the energy value contrast ratio of alpha waves with different frequencies.
7. The method of claim 3, wherein said extracting the user's brain electrical alert maintenance duration from the brain electrical dominant wave comprises:
and calculating the duration time of the electroencephalogram dominant wave, and evaluating the duration time to obtain the electroencephalogram alertness maintaining duration of the user based on the time length.
8. The method of claim 3, wherein after inputting the characteristic values of the depressive state into the classification algorithm model for identification and classification, the method further comprises:
and acquiring a depression state result, and sending the depression state result to the terminal for displaying.
9. A system for depression state detection based on EEG signals, for implementing a method according to any of claims 1 to 8, comprising:
the BCI equipment is used for collecting an EEG signal of a user and sending the EEG signal to the data analysis system;
and the data analysis system is used for extracting the electroencephalogram alertness maintaining time of the user from the EEG signal to be used as a depression state characteristic value, and inputting the depression state characteristic value into the classification algorithm model for identification and classification.
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