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Interpretation of Frequency Channel-Based CNN on Depression Identification
Online end-to-end electroencephalogram (EEG) classification with high performance can assess the brain status of patients with Major Depression Disabled (MDD) and track their development status in time with minimizing the risk of falling into danger and suicide. However, it remains a grand research...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8750060/ https://www.ncbi.nlm.nih.gov/pubmed/35027888 http://dx.doi.org/10.3389/fncom.2021.773147 |
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author | Ke, Hengjin Cai, Cang Wang, Fengqin Hu, Fang Tang, Jiawei Shi, Yuxin |
author_facet | Ke, Hengjin Cai, Cang Wang, Fengqin Hu, Fang Tang, Jiawei Shi, Yuxin |
author_sort | Ke, Hengjin |
collection | PubMed |
description | Online end-to-end electroencephalogram (EEG) classification with high performance can assess the brain status of patients with Major Depression Disabled (MDD) and track their development status in time with minimizing the risk of falling into danger and suicide. However, it remains a grand research challenge due to (1) the embedded intensive noises and the intrinsic non-stationarity determined by the evolution of brain states, (2) the lack of effective decoupling of the complex relationship between neural network and brain state during the attack of brain diseases. This study designs a Frequency Channel-based convolutional neural network (CNN), namely FCCNN, to accurately and quickly identify depression, which fuses the brain rhythm to the attention mechanism of the classifier with aiming at focusing the most important parts of data and improving the classification performance. Furthermore, to understand the complexity of the classifier, this study proposes a calculation method of information entropy based on the affinity propagation (AP) clustering partition to measure the complexity of the classifier acting on each channel or brain region. We perform experiments on depression evaluation to identify healthy and MDD. Results report that the proposed solution can identify MDD with an accuracy of 99±0.08%, the sensitivity of 99.07±0.05%, and specificity of 98.90±0.14%. Furthermore, the experiments on the quantitative interpretation of FCCNN illustrate significant differences between the frontal, left, and right temporal lobes of depression patients and the healthy control group. |
format | Online Article Text |
id | pubmed-8750060 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87500602022-01-12 Interpretation of Frequency Channel-Based CNN on Depression Identification Ke, Hengjin Cai, Cang Wang, Fengqin Hu, Fang Tang, Jiawei Shi, Yuxin Front Comput Neurosci Neuroscience Online end-to-end electroencephalogram (EEG) classification with high performance can assess the brain status of patients with Major Depression Disabled (MDD) and track their development status in time with minimizing the risk of falling into danger and suicide. However, it remains a grand research challenge due to (1) the embedded intensive noises and the intrinsic non-stationarity determined by the evolution of brain states, (2) the lack of effective decoupling of the complex relationship between neural network and brain state during the attack of brain diseases. This study designs a Frequency Channel-based convolutional neural network (CNN), namely FCCNN, to accurately and quickly identify depression, which fuses the brain rhythm to the attention mechanism of the classifier with aiming at focusing the most important parts of data and improving the classification performance. Furthermore, to understand the complexity of the classifier, this study proposes a calculation method of information entropy based on the affinity propagation (AP) clustering partition to measure the complexity of the classifier acting on each channel or brain region. We perform experiments on depression evaluation to identify healthy and MDD. Results report that the proposed solution can identify MDD with an accuracy of 99±0.08%, the sensitivity of 99.07±0.05%, and specificity of 98.90±0.14%. Furthermore, the experiments on the quantitative interpretation of FCCNN illustrate significant differences between the frontal, left, and right temporal lobes of depression patients and the healthy control group. Frontiers Media S.A. 2021-12-27 /pmc/articles/PMC8750060/ /pubmed/35027888 http://dx.doi.org/10.3389/fncom.2021.773147 Text en Copyright © 2021 Ke, Cai, Wang, Hu, Tang and Shi. 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 | Neuroscience Ke, Hengjin Cai, Cang Wang, Fengqin Hu, Fang Tang, Jiawei Shi, Yuxin Interpretation of Frequency Channel-Based CNN on Depression Identification |
title | Interpretation of Frequency Channel-Based CNN on Depression Identification |
title_full | Interpretation of Frequency Channel-Based CNN on Depression Identification |
title_fullStr | Interpretation of Frequency Channel-Based CNN on Depression Identification |
title_full_unstemmed | Interpretation of Frequency Channel-Based CNN on Depression Identification |
title_short | Interpretation of Frequency Channel-Based CNN on Depression Identification |
title_sort | interpretation of frequency channel-based cnn on depression identification |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8750060/ https://www.ncbi.nlm.nih.gov/pubmed/35027888 http://dx.doi.org/10.3389/fncom.2021.773147 |
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