Toward practical machine-learning-based diagnosis for drug-naïve women with major depressive disorder using EEG channel reduction approach

Miseon Shim; Han-Jeong Hwang; Seung-Hwan Lee

doi:10.1016/j.jad.2023.06.007

Toward practical machine-learning-based diagnosis for drug-naïve women with major depressive disorder using EEG channel reduction approach

J Affect Disord. 2023 Oct 1:338:199-206. doi: 10.1016/j.jad.2023.06.007. Epub 2023 Jun 10.

Authors

Miseon Shim¹, Han-Jeong Hwang², Seung-Hwan Lee³

Affiliations

¹ Department of Electronics and Information Engineering, Korea University, Sejong, Republic of Korea; Industry Development Institute, Korea University, Sejong, Republic of Korea.
² Department of Electronics and Information Engineering, Korea University, Sejong, Republic of Korea; Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong, Republic of Korea. Electronic address: hwanghj@korea.ac.kr.
³ Psychiatry Department, Ilsan Paik Hospital, Inje University, Goyang, Republic of Korea; Clinical Emotion and Cognition Research Laboratory, Goyang, Republic of Korea; BWAVE Inc., Goyang, Republic of Korea. Electronic address: lshpss@paik.ac.kr.

PMID: 37302509
DOI: 10.1016/j.jad.2023.06.007

Abstract

Background: A machine-learning-based computer-aided diagnosis (CAD) system can complement the traditional diagnostic error for major depressive disorder (MDD) using trait-like neurophysiological biomarkers. Previous studies have shown that the CAD system has the potential to differentiate between female MDD patients and healthy controls. The aim of this study was to develop a practically useful resting-state electroencephalography (EEG)-based CAD system to assist in the diagnosis of drug-naïve female MDD patients by considering both the drug and gender effects. In addition, the feasibility of the practical use of the resting-state EEG-based CAD system was evaluated using a channel reduction approach.

Methods: Eyes-closed, resting-state EEG data were recorded from 49 drug-naïve female MDD patients and 49 sex-matched healthy controls. Six different EEG feature sets were extracted: power spectrum densities (PSDs), phase-locking values (PLVs), and network indices for both sensor- and source-level, and four different EEG channel montages (62, 30, 19, and 10-channels) were designed to investigate the channel reduction effects in terms of classification performance.

Results: The classification performances for each feature set were evaluated using a support vector machine with leave-one-out cross-validation. The optimum classification performance was achieved when using sensor-level PLVs (accuracy: 83.67 % and area under curve: 0.92). Moreover, the classification performance was maintained until the number of EEG channels was reduced to 19 (over 80 % accuracy).

Conclusion: We demonstrated the promising potential of sensor-level PLVs as diagnostic features when developing a resting-state EEG-based CAD system for the diagnosis of drug-naïve female MDD patients and verified the feasibility of the practical use of the developed resting-state EEG-based CAD system using the channel reduction approach.

Keywords: Channel reduction; Classification; Drug-naïve; Female major depressive disorder; Machine-learning; Resting-state EEG.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Depressive Disorder, Major* / diagnosis
Diagnosis, Computer-Assisted
Electroencephalography
Female
Humans
Machine Learning
Support Vector Machine