CN113397564A - Depression identification method, device, terminal and medium based on image processing - Google Patents
Depression identification method, device, terminal and medium based on image processing Download PDFInfo
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Abstract
The application discloses a depression identification method, a depression identification device, a depression identification terminal and a depression identification medium based on image processing. The method comprises the following steps: acquiring electroencephalogram signals respectively corresponding to a plurality of electroencephalogram channels of a detected user; generating a to-be-processed picture comprising frequency spectrograms respectively corresponding to the plurality of electroencephalogram channels based on the electroencephalogram signals respectively corresponding to the plurality of electroencephalogram channels; and carrying out image identification processing on the picture to be processed to obtain depression assessment classification to which the detected user belongs. According to the method, the electroencephalogram signals are converted into the images to perform depression identification, so that the automation of depression identification is realized, the problem that the depression identification is easily influenced by subjective factors is solved, the effect of accelerating the depression identification speed by using an image identification technology is also achieved, and the purpose of improving the depression identification efficiency is achieved.
Description
Technical Field
The application relates to the technical field of computers, in particular to a depression identification method, a depression identification device, a depression identification terminal and a depression identification medium based on image processing.
Background
Depression is also called depressive disorder, and is an affective disorder mental disease with high incidence rate. Patients with depression often have typical symptoms of depressed mood, loss of interest and pleasure, loss of energy or fatigue. The diagnosis of depression should be based on medical history, clinical symptoms, course of disease, physical examination and laboratory examination. Therefore, the diagnosis method is susceptible to subjective factors, and misdiagnosis and missed diagnosis are easily caused. Researches find that the electroencephalogram signals of depression patients and healthy contrast persons have different variation rules on parameters such as wave bands, power, wave amplitudes and the like. Therefore, in order to overcome the problem that the diagnosis of depression is easily affected by subjective factors, the analysis of electroencephalogram signals is mainly adopted in the related art to diagnose depression. However, the method for diagnosing the depression through the electroencephalogram signals has the problems of long analysis time and low efficiency.
Disclosure of Invention
In order to solve at least one technical problem, the present application provides a depression recognition method, apparatus, terminal and medium based on image processing.
According to a first aspect of the present application, there is provided a method for depression recognition based on image processing, the method comprising:
acquiring electroencephalogram signals corresponding to a plurality of electroencephalogram channels respectively;
generating a to-be-processed picture comprising frequency spectrograms respectively corresponding to the plurality of electroencephalogram channels based on the electroencephalogram signals respectively corresponding to the plurality of electroencephalogram channels;
determining the image characteristics of the picture to be processed;
and determining the depression assessment classification to which the detected user belongs according to the image characteristics.
According to a second aspect of the present application, there is provided an image processing-based depression recognition apparatus, the apparatus including:
the electroencephalogram signal acquisition module is used for acquiring electroencephalogram signals corresponding to a plurality of electroencephalogram channels of a detected user respectively;
the electroencephalogram image conversion module is used for generating a to-be-processed image comprising frequency spectrograms respectively corresponding to the electroencephalogram channels based on the electroencephalogram signals respectively corresponding to the electroencephalogram channels;
the image characteristic determining module is used for determining the image characteristics of the picture to be processed;
and the depression classification identification module is used for determining the depression assessment classification to which the detected user belongs according to the image characteristics.
According to a third aspect of the present application, there is provided a terminal comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the computer program to implement the above-mentioned image processing based depression identification method.
According to a fourth aspect of the present application, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described image-processing-based depression recognition method.
The electroencephalogram signals corresponding to the plurality of electroencephalogram channels of the detected user are obtained to generate the to-be-processed picture comprising the frequency spectrums corresponding to the plurality of electroencephalogram channels, the to-be-processed picture is subjected to image identification processing, depression assessment classification to which the detected user belongs is obtained, the mode of performing depression identification by converting the electroencephalogram signals into images is realized, the automation of depression identification is realized, the problem that the depression identification is easily influenced by subjective factors is solved, the effect of accelerating the depression identification speed by utilizing an image identification technology is also realized, and the purpose of improving the depression identification efficiency is achieved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below.
Fig. 1 is a schematic flowchart of a depression identification method based on image processing according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an application system of a depression identification method based on image processing according to an embodiment of the present application;
fig. 3 is a schematic process diagram of an embodiment of a to-be-processed picture in a method for identifying depression based on image processing according to an embodiment of the present application;
fig. 4 is a schematic diagram illustrating a result of binarization processing of a to-be-processed picture in a depression identification method based on image processing according to an embodiment of the present application; and
fig. 5 is a schematic block diagram of a depression identification apparatus based on image processing according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It should be noted that although functional blocks are partitioned in a schematic diagram of an apparatus and a logical order is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the partitioning of blocks in the apparatus or the order in the flowchart.
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
According to an embodiment of the present application, there is provided a depression recognition method based on image processing, as shown in fig. 1, the method including steps S101 to S104.
Step S101: acquiring electroencephalogram signals corresponding to a plurality of electroencephalogram channels of a detected user respectively.
Specifically, the electronic device obtains electroencephalogram signals corresponding to a plurality of electroencephalogram channels of a user to be tested. The electronic equipment can be electroencephalogram interface BCI equipment, a PC (personal computer), a tablet, a server and the like, and can also be electroencephalogram EEG signal acquisition equipment.
For example, if the electronic device is a brain electrical interface BCI device, the brain electrical EEG acquisition device acquires brain electrical signals corresponding to a plurality of brain electrical channels of the user to be tested, so that the brain electrical interface BCI device acquires the brain electrical signals corresponding to the plurality of brain electrical channels of the user to be tested, which are sent by the brain electrical EEG acquisition device.
For example, if the electronic device is a mobile terminal such as a mobile phone or a tablet, the mobile terminal acquires electroencephalogram signals corresponding to a plurality of electroencephalogram channels of the user to be tested, which are sent from the electroencephalogram EEG acquisition device.
Step S102: and generating a to-be-processed picture comprising frequency spectrograms respectively corresponding to the plurality of electroencephalogram channels based on the electroencephalogram signals respectively corresponding to the plurality of electroencephalogram channels.
Specifically, electroencephalograms corresponding to a plurality of electroencephalogram channels can be converted in the same image, so that a to-be-processed image including spectrograms corresponding to the plurality of electroencephalogram channels is obtained.
Specifically, the electroencephalogram signals corresponding to the plurality of electroencephalogram channels can be converted in different pictures, and the spectrograms corresponding to the plurality of electroencephalogram channels are transplanted to the same graph, so that the picture to be processed can be obtained.
Step S103: and determining the image characteristics of the picture to be processed.
Specifically, a preset image feature extraction algorithm may be adopted to process the image to be processed, so as to obtain the image features of the image to be processed. The image feature extraction algorithm may include the following steps: scale Invariant Feature Transformation (SIFT), acceleration robust feature (SURF), Histogram of Oriented Gradients (HOG), difference of Gaussian function (DOG), Local Binary Pattern (LBP) feature, Haar feature and the like. It should be noted that, the algorithm for extracting the image feature includes, but is not limited to, the above algorithm, and any algorithm capable of extracting the feature of the image to be processed is within the scope of the present application.
Step S104: and determining the depression assessment classification to which the detected user belongs according to the image characteristics.
In an embodiment of the present application, the depression assessment classification is used to characterize the depression condition of the tested user.
In particular, the depression assessment category may be divided according to business needs or degree of depression. For example, a depression assessment category may include: no depression symptom, mild depression, severe depression and the like.
The electroencephalogram signals corresponding to the plurality of electroencephalogram channels of the detected user are obtained to generate the to-be-processed picture comprising the frequency spectrogram corresponding to the plurality of electroencephalogram channels, the to-be-processed picture is subjected to image identification processing to obtain depression assessment classification to which the detected user belongs, the mode of performing depression identification by converting the electroencephalogram signals into the image is realized, the automation of depression identification is realized, the problem that the depression identification is easily influenced by subjective factors is solved, the effect of accelerating the depression identification speed by utilizing an image identification technology is also achieved, and the purpose of improving the depression identification efficiency is achieved.
In some embodiments, before step S102, the method further comprises:
and filtering and normalizing the electroencephalogram signals corresponding to the plurality of electroencephalogram channels respectively.
Specifically, the power frequency interference may be removed by a preset notch filter. For example, 0.5-40Hz bandpass filtering of the signal removes irrelevant unwanted information from the EEG signal.
In some embodiments, step S102 further comprises:
fourier transform is carried out on the electroencephalogram signals corresponding to the plurality of electroencephalogram channels respectively to obtain frequency spectrograms corresponding to the plurality of electroencephalogram channels respectively;
and generating a picture to be processed according to the frequency spectrogram corresponding to the plurality of electroencephalogram channels.
The embodiment of the application realizes the purpose of converting the signals into the pictures through the Fourier transform algorithm, thereby realizing the purpose of identifying depression classification by using an image identification technology.
In some embodiments, step S104 further comprises:
matching the image characteristics in a preset image characteristic database, wherein the image characteristic database comprises a plurality of depression assessment classifications, and the depression assessment classifications respectively correspond to the image characteristics;
and determining a depression evaluation classification aiming at the image characteristics according to the matching result.
Specifically, the image features may be represented in an array. In particular, different depression assessment categories correspond to different arrays of image features.
The method has the effect of accelerating the depression estimation classification on the lines through the image feature database.
In some embodiments, step S103 further comprises:
and determining the image characteristics of the picture to be processed based on the pre-constructed image recognition model.
When the method is applied, a training sample is firstly obtained, and a preset neural network model is trained by using the training sample, so that an image recognition model is obtained. Specifically, the training sample comprises a first image of a healthy user, which comprises spectrograms corresponding to a plurality of electroencephalogram channels respectively, and a second image of a depression patient, which comprises spectrograms corresponding to a plurality of electroencephalogram channels respectively. More specifically, the neural network model may be trained such that the number of first images and the number of second images may be in a 1:9 ratio.
Specifically, the image recognition model may be obtained by training using a CNN model.
In some embodiments, prior to step S103, the method further comprises:
and carrying out binarization processing on the picture to be processed.
Specifically, the average value K of the pixels of the picture to be processed can be calculated, each pixel value of the image is scanned, and if the pixel value of any pixel point is greater than K, the pixel value of any pixel point is adjusted to be 255 (white); if the pixel value of any pixel point is less than or equal to the K pixel value, the pixel value of any pixel point is set to be 0 (black).
Specifically, a histogram method can be used to find the binarization threshold, where the histogram is an important feature of the image, and the histogram method selects the binarization threshold mainly by finding the two highest peaks of the image and then taking the lowest peak valley between the two peaks at the threshold value.
According to the method and the device, the data volume in the image is reduced through binarization processing, so that the speed of feature extraction of the image to be processed is increased.
In some embodiments, the method further comprises:
and determining guide information for the tested user based on the depression assessment classification to which the tested user belongs.
In the examples of the present application, the instructional information is used to characterize the treatment regimen, cautions, etc. for the patient with depression.
Specifically, the depression evaluation category to which the detected user belongs may be matched in a preset guidance information database, thereby obtaining guidance information for the depression evaluation category. For example, the guidance information database includes guidance information corresponding to each of a plurality of categories of depression assessments. When the method is applied, the terminal acquires the update data packet of the guide information database from the server, so that the precision of the guide information for the detected user is improved.
To further illustrate the methods provided by embodiments of the present application, a detailed description is provided below in conjunction with the system shown in FIG. 2. The system shown in fig. 2 comprises a brain EEG acquisition device 10 and a brain-computer interface BCI device. When the brain-computer EEG acquisition equipment 10 is used, the brain-computer interface BCI equipment can be connected in a wired mode or in a wireless connection mode such as Bluetooth. The electroencephalogram EEG acquisition device 10 comprises a plurality of electrodes, and the plurality of electrodes can be respectively placed on the brain of a tested user according to the positions of points Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1 and O2 of the international electroencephalogram 10-20 system, so that the electroencephalogram EEG acquisition device 10 can acquire electroencephalogram signals respectively corresponding to a plurality of channels and transmit the acquired electroencephalogram signals respectively corresponding to the plurality of channels to the electroencephalogram interface BCI device 20. The brain electrical interface BCI device 20 processes the acquired brain electrical signals corresponding to the plurality of channels to obtain a to-be-processed picture including spectrograms corresponding to the plurality of brain electrical channels as shown in fig. 3, and after the picture to be processed is subjected to binarization processing to obtain an image shown in figure 4, the picture to be processed is subjected to feature extraction by utilizing a pre-constructed image identification model to obtain the image features of the picture to be processed, thereby determining the depression evaluation classification of the tested user according to the image characteristics, realizing the purpose of diagnosing the depression by utilizing the image recognition technology, the method not only solves the problem that the diagnosis accuracy is easily influenced by subjective factors in manual diagnosis, but also achieves the effect of accelerating the speed of identifying the depression by utilizing an image identification technology due to the fact that the identification of the electroencephalogram signals is converted into the image identification, and achieves the purpose of improving the depression identification efficiency.
Yet another embodiment of the present application provides a depression recognition apparatus based on image processing, as shown in fig. 5, the apparatus 50 including: an electroencephalogram signal acquisition module 501, an electroencephalogram image conversion module 502, an image feature determination module 503 and a depression classification identification module 504.
The electroencephalogram signal acquisition module 501 is configured to acquire electroencephalogram signals corresponding to a plurality of electroencephalogram channels of a user to be tested;
the electroencephalogram image conversion module 502 is configured to generate a to-be-processed image including a spectrogram corresponding to each of the plurality of electroencephalogram channels based on an electroencephalogram signal corresponding to each of the plurality of electroencephalogram channels;
an image feature determining module 503, configured to determine an image feature of the to-be-processed picture;
a depression classification identification module 504, configured to determine a depression assessment classification to which the detected user belongs according to the image features.
The electroencephalogram signals corresponding to the plurality of electroencephalogram channels of the detected user are obtained to generate the to-be-processed picture comprising the frequency spectrogram corresponding to the plurality of electroencephalogram channels, the to-be-processed picture is subjected to image identification processing to obtain depression assessment classification to which the detected user belongs, the mode of performing depression identification by converting the electroencephalogram signals into the image is realized, the automation of depression identification is realized, the problem that the depression identification is easily influenced by subjective factors is solved, the effect of accelerating the depression identification speed by utilizing an image identification technology is also achieved, and the purpose of improving the depression identification efficiency is achieved.
Further, before the step of generating the to-be-processed picture including the spectrogram corresponding to the plurality of electroencephalogram channels respectively based on the electroencephalogram signals corresponding to the plurality of electroencephalogram channels respectively, the electroencephalogram image conversion module further includes:
and filtering and normalizing the electroencephalogram signals corresponding to the plurality of electroencephalogram channels respectively.
Further, the electroencephalogram image conversion module includes:
the frequency spectrum map conversion sub-module is used for carrying out Fourier conversion on the electroencephalogram signals corresponding to the plurality of electroencephalogram channels respectively to obtain frequency spectrum maps corresponding to the plurality of electroencephalogram channels respectively;
and the image generation sub-module is used for generating the image to be processed according to the frequency spectrogram corresponding to the plurality of electroencephalogram channels respectively.
Further, the image feature determination module includes:
and the model identification submodule is used for determining the image characteristics of the picture to be processed based on a pre-constructed image identification model.
Further, before the step of extracting the features of the picture to be processed, the image feature determining module further includes:
and the image binarization submodule is used for carrying out binarization processing on the picture to be processed.
Further, the depression classification identification module includes:
the image feature matching sub-module is used for matching the image features in a preset image feature database, wherein the image feature database comprises a plurality of depression assessment classifications and image features corresponding to the depression assessment classifications;
a classification result determination sub-module for determining a depression assessment classification for the image features in dependence on the matching result.
Further, the depression classification identification module further comprises:
and the guiding information determining sub-module is used for determining guiding information aiming at the tested user based on the depression assessment classification to which the tested user belongs.
The image processing based depression recognition apparatus of this embodiment can execute the image processing based depression recognition method provided in the embodiments of this application, and the implementation principles thereof are similar, and are not described herein again.
Another embodiment of the present application provides a terminal, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the computer program to implement the above-mentioned image processing based depression identification method.
In particular, the processor may be a CPU, general purpose processor, DSP, ASIC, FPGA or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. A processor may also be a combination of computing functions, e.g., comprising one or more microprocessors, a DSP and a microprocessor, or the like.
In particular, the processor is coupled to the memory via a bus, which may include a path for communicating information. The bus may be a PCI bus or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc.
The memory may be, but is not limited to, a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, an EEPROM, a CD-ROM or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
Optionally, the memory is used for storing codes of computer programs for executing the scheme of the application, and the processor is used for controlling the execution. The processor is used for executing application program codes stored in the memory so as to realize the actions of the depression recognition device based on image processing provided by the embodiment.
Yet another embodiment of the present application provides a computer-readable storage medium on which a computer program is stored, which, when executed by a processor, implements the above-described image-processing-based depression recognition method.
The above-described embodiments of the apparatus are merely illustrative, and the units illustrated as separate components may or may not be physically separate, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
One of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
While the present invention has been described with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. An image processing-based depression identification method, comprising:
acquiring electroencephalogram signals respectively corresponding to a plurality of electroencephalogram channels of a detected user;
generating a to-be-processed picture comprising frequency spectrograms respectively corresponding to the plurality of electroencephalogram channels based on the electroencephalogram signals respectively corresponding to the plurality of electroencephalogram channels;
determining the image characteristics of the picture to be processed;
and determining the depression assessment classification to which the detected user belongs according to the image characteristics.
2. The method of claim 1, wherein before the step of generating the to-be-processed picture including the spectrogram corresponding to each of the plurality of electroencephalogram channels based on the electroencephalogram signals corresponding to each of the plurality of electroencephalogram channels, the method further comprises:
and filtering and normalizing the electroencephalogram signals corresponding to the plurality of electroencephalogram channels respectively.
3. The method of claim 1, wherein the step of generating the to-be-processed picture including the spectrogram corresponding to each of the plurality of electroencephalogram channels based on the electroencephalogram signals corresponding to each of the plurality of electroencephalogram channels comprises:
fourier transform is carried out on the electroencephalogram signals corresponding to the plurality of electroencephalogram channels respectively to obtain frequency spectrograms corresponding to the plurality of electroencephalogram channels respectively;
and generating the picture to be processed according to the frequency spectrogram corresponding to the plurality of electroencephalogram channels.
4. The method according to claim 1, wherein the step of determining the image characteristics of the picture to be processed comprises:
and determining the image characteristics of the picture to be processed based on a pre-constructed image recognition model.
5. The method of claim 4, wherein the step of determining the image characteristics of the picture to be processed is preceded by the method further comprising:
and carrying out binarization processing on the picture to be processed.
6. The method according to claim 1, wherein the step of determining a depression assessment category to which the detected user belongs according to the image features comprises:
matching the image features in a preset image feature database, wherein the image feature database comprises a plurality of depression assessment classifications, and the plurality of depression assessment classifications respectively correspond to the image features;
determining a depression assessment classification for the image features in dependence on the matching result.
7. The method of claim 1, further comprising:
determining guidance information for the tested user based on a depression assessment category to which the tested user belongs.
8. An image processing-based depression recognition apparatus, comprising:
the electroencephalogram signal acquisition module is used for acquiring electroencephalogram signals corresponding to a plurality of electroencephalogram channels of a detected user respectively;
the electroencephalogram image conversion module is used for generating a to-be-processed image comprising frequency spectrograms respectively corresponding to the electroencephalogram channels based on the electroencephalogram signals respectively corresponding to the electroencephalogram channels;
the image characteristic determining module is used for determining the image characteristics of the picture to be processed;
and the depression classification identification module is used for determining the depression assessment classification to which the detected user belongs according to the image characteristics.
9. A terminal, comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method of any one of claims 1 to 7.
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| CN109656366A (en) * | 2018-12-19 | 2019-04-19 | 电子科技大学中山学院 | Emotional state identification method and device, computer equipment and storage medium |
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| CN109656366A (en) * | 2018-12-19 | 2019-04-19 | 电子科技大学中山学院 | Emotional state identification method and device, computer equipment and storage medium |
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