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EEG diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network

Depression is an undetectable mental disease. Most of the patients with depressive symptoms do not know that they are suffering from depression. Since the novel Coronavirus pandemic 2019, the number of patients with depression has increased rapidly. There are two kinds of traditional depression diag...

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Autores principales: Wang, Baiyang, Kang, Yuyun, Huo, Dongyue, Feng, Guifang, Zhang, Jiawei, Li, Jiadong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9632488/
https://www.ncbi.nlm.nih.gov/pubmed/36338469
http://dx.doi.org/10.3389/fphys.2022.1029298
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author Wang, Baiyang
Kang, Yuyun
Huo, Dongyue
Feng, Guifang
Zhang, Jiawei
Li, Jiadong
author_facet Wang, Baiyang
Kang, Yuyun
Huo, Dongyue
Feng, Guifang
Zhang, Jiawei
Li, Jiadong
author_sort Wang, Baiyang
collection PubMed
description Depression is an undetectable mental disease. Most of the patients with depressive symptoms do not know that they are suffering from depression. Since the novel Coronavirus pandemic 2019, the number of patients with depression has increased rapidly. There are two kinds of traditional depression diagnosis. One is that professional psychiatrists make diagnosis results for patients, but it is not conducive to large-scale depression detection. Another is to use electroencephalography (EEG) to record neuronal activity. Then, the features of the EEG are extracted using manual or traditional machine learning methods to diagnose the state and type of depression. Although this method achieves good results, it does not fully utilize the multi-channel information of EEG. Aiming at this problem, an EEG diagnosis method for depression based on multi-channel data fusion cropping enhancement and convolutional neural network is proposed. First, the multi-channel EEG data are transformed into 2D images after multi-channel fusion (MCF) and multi-scale clipping (MSC) augmentation. Second, it is trained by a multi-channel convolutional neural network (MCNN). Finally, the trained model is loaded into the detection device to classify the input EEG signals. The experimental results show that the combination of MCF and MSC can make full use of the information contained in the single sensor records, and significantly improve the classification accuracy and clustering effect of depression diagnosis. The method has the advantages of low complexity and good robustness in signal processing and feature extraction, which is beneficial to the wide application of detection systems.
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spelling pubmed-96324882022-11-04 EEG diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network Wang, Baiyang Kang, Yuyun Huo, Dongyue Feng, Guifang Zhang, Jiawei Li, Jiadong Front Physiol Physiology Depression is an undetectable mental disease. Most of the patients with depressive symptoms do not know that they are suffering from depression. Since the novel Coronavirus pandemic 2019, the number of patients with depression has increased rapidly. There are two kinds of traditional depression diagnosis. One is that professional psychiatrists make diagnosis results for patients, but it is not conducive to large-scale depression detection. Another is to use electroencephalography (EEG) to record neuronal activity. Then, the features of the EEG are extracted using manual or traditional machine learning methods to diagnose the state and type of depression. Although this method achieves good results, it does not fully utilize the multi-channel information of EEG. Aiming at this problem, an EEG diagnosis method for depression based on multi-channel data fusion cropping enhancement and convolutional neural network is proposed. First, the multi-channel EEG data are transformed into 2D images after multi-channel fusion (MCF) and multi-scale clipping (MSC) augmentation. Second, it is trained by a multi-channel convolutional neural network (MCNN). Finally, the trained model is loaded into the detection device to classify the input EEG signals. The experimental results show that the combination of MCF and MSC can make full use of the information contained in the single sensor records, and significantly improve the classification accuracy and clustering effect of depression diagnosis. The method has the advantages of low complexity and good robustness in signal processing and feature extraction, which is beneficial to the wide application of detection systems. Frontiers Media S.A. 2022-10-20 /pmc/articles/PMC9632488/ /pubmed/36338469 http://dx.doi.org/10.3389/fphys.2022.1029298 Text en Copyright © 2022 Wang, Kang, Huo, Feng, Zhang and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Wang, Baiyang
Kang, Yuyun
Huo, Dongyue
Feng, Guifang
Zhang, Jiawei
Li, Jiadong
EEG diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network
title EEG diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network
title_full EEG diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network
title_fullStr EEG diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network
title_full_unstemmed EEG diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network
title_short EEG diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network
title_sort eeg diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9632488/
https://www.ncbi.nlm.nih.gov/pubmed/36338469
http://dx.doi.org/10.3389/fphys.2022.1029298
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