Effective EEG Feature Selection for Interpretable MDD (Major Depressive Disorder) Classification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F23%3APU149356" target="_blank" >RIV/00216305:26230/23:PU149356 - isvavai.cz</a>

Result on the web

<a href="https://dl.acm.org/doi/10.1145/3583131.3590398" target="_blank" >https://dl.acm.org/doi/10.1145/3583131.3590398</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3583131.3590398" target="_blank" >10.1145/3583131.3590398</a>

Result language

angličtina

Original language name

Effective EEG Feature Selection for Interpretable MDD (Major Depressive Disorder) Classification

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA21-13001S" target="_blank" >GA21-13001S: Automated design of hardware accelerators for resource-aware machine learning</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

GECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference

ISBN

979-8-4007-0119-1

ISSN

—

e-ISSN

—

Number of pages

9

Pages from-to

1427-1435

Publisher name

Association for Computing Machinery

Place of publication

Lisbon

Event location

Lisbon

Event date

Jul 15, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001031455100159