Publication Details
Effective EEG Feature Selection for Interpretable MDD (Major Depressive Disorder) Classification
Jawed Soyiba, Dr., MSc (Automata@FIT)
Arif Muhammad, Ph.D.
Malik Aamir Saeed, prof., Ph.D. (DCSY)
electroencephalogram (EEG), feature extraction, major depressive
disorder
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.
@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"
}