research-article

Identification of Depression Subtypes Based on EEG and Machine Learning

Authors:

Lei FengAuthors Info & Claims

WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

Pages 302 - 306

https://doi.org/10.1145/3498851.3498970

Published: 11 April 2022 Publication History

Abstract

Depressive disorders are heterogeneous in symptoms and course of disease, while it is difficult to reveal the neurophysiological subtypes. In this study, we combined EEG data and machine learning methods to identify subtypes of depression, and further evaluated the rationality and reliability of subtypes. The results showed that the left-right asymmetry of prefrontal lobe in alpha band clustered 32 patients with depression into three different subtypes, each type showed unique clinical characteristics. High classification effects could be obtained through decision tree and logistic regression model. The study further investigated the new way to identify neurophysiological subtypes of depression with machine learning methods. This study also shows the important value of the classic EEG index of depression, namely the left-right asymmetry of prefrontal lobe in alpha band, in the identification of depression subtypes.

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Cited By

Simmatis LRusso EGeraci JHarmsen ISamuel N(2023)Technical and clinical considerations for electroencephalography-based biomarkers for major depressive disordernpj Mental Health Research10.1038/s44184-023-00038-72:1Online publication date: 25-Oct-2023
https://doi.org/10.1038/s44184-023-00038-7

Identification of Depression Subtypes Based on EEG and Machine Learning
1. Computing methodologies

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cover image ACM Conferences

WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

December 2021

541 pages

ISBN:9781450391870

DOI:10.1145/3498851

Copyright © 2021 ACM.

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Publication History

Published: 11 April 2022

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Beijing Municipal Natural Science Foundation
National Key Research and Development Program of China

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WI-IAT '21

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WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence

December 14 - 17, 2021

VIC, Melbourne, Australia

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Cited By

Simmatis LRusso EGeraci JHarmsen ISamuel N(2023)Technical and clinical considerations for electroencephalography-based biomarkers for major depressive disordernpj Mental Health Research10.1038/s44184-023-00038-72:1Online publication date: 25-Oct-2023
https://doi.org/10.1038/s44184-023-00038-7

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