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MRÁZEK, V. JAWED, S. ARIF, M. MALIK, A.
Original Title
Effective EEG Feature Selection for Interpretable MDD (Major Depressive Disorder) Classification
Type
conference paper
Language
English
Original Abstract
In this paper, we propose an interpretable electroencephalogram (EEG)-based solution for the diagnostics of major depressive disorder (MDD). The acquisition of EEG experimental data involved 32 MDD patients and 29 healthy controls. A feature matrix is constructed involving frequency decomposition of EEG data based on power spectrum density (PSD) using the Welch method. Those PSD features were selected, which were statistically significant. To improve interpretability, the best features are first selected from feature space via the non-dominated sorting genetic (NSGA-II) evolutionary algorithm. The best features are utilized for support vector machine (SVM), and k-nearest neighbors (k-NN) classifiers, and the results are then correlated with features to improve the interpretability. The results show that the features (gamma bands) extracted from the left temporal brain regions can distinguish MDD patients from control significantly. The proposed best solution by NSGA-II gives an average sensitivity of 93.3%, specificity of 93.4% and accuracy of 93.5%. The complete framework is published as open-source at https://github.com/ehw-fit/eeg-mdd.
Keywords
electroencephalogram (EEG), feature extraction, major depressive disorder
Authors
MRÁZEK, V.; JAWED, S.; ARIF, M.; MALIK, A.
Released
14. 4. 2023
Publisher
Association for Computing Machinery
Location
Lisbon
ISBN
979-8-4007-0119-1
Book
GECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference
Pages from
1427
Pages to
1435
Pages count
9
URL
https://dl.acm.org/doi/10.1145/3583131.3590398
Full text in the Digital Library
http://hdl.handle.net/11012/244320
BibTex
@inproceedings{BUT185129, author="Vojtěch {Mrázek} and Soyiba {Jawed} and Muhammad {Arif} and Aamir Saeed {Malik}", title="Effective EEG Feature Selection for Interpretable MDD (Major Depressive Disorder) Classification", booktitle="GECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference", year="2023", pages="1427--1435", publisher="Association for Computing Machinery", address="Lisbon", doi="10.1145/3583131.3590398", isbn="979-8-4007-0119-1", url="https://dl.acm.org/doi/10.1145/3583131.3590398" }