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"
}