Akademik Veri Yönetim Sistemi
Machine Learning: Science and Technology, cilt.6, sa.2, 2025 (SCI-Expanded)
Accurate classification of major depressive disorder (MDD) remains a significant challenge, particularly because of the confounding effect of medications. This study bridges this gap by focusing on the classification of drug-naïve individuals diagnosed with MDD and healthy controls (HCs) using electroencephalogram (EEG) data recorded during emotional processing tasks. This study involved 14 HCs and 14 drug-naïve individuals diagnosed with MDD (aged 18-31, 12+ years of education, 12 F/2 M). The participants were presented with positive, neutral, and negative images collected from the International Affective Picture System. The mean power amplitudes of event-related potentials (ERP), including the P200, P300, early, middle, and late components of the late positive potential (LPP), were computed, along with band power features, and used as features for classifiers. A support vector machine model was employed for classification to evaluate the individual contributions of ERP components and band power features and explore the combined effects of ERP components and band power features within themselves. The alpha band power achieved the highest individual classification accuracy among the band power features for negative stimuli (92.86%). The late LPP component was the most discriminative ERP component for positive stimuli, yielding an accuracy rate of 89.29%. Combined analysis of the band power features exhibited high accuracy for both positive and negative stimuli (92.86% each). When the ERP components were combined, the classifier achieved the highest accuracy of 89.29% for both negative and neutral stimuli. Our findings suggest that alpha band power and LPP responses to negative and positive stimuli, respectively, can be used to detect MDD. The comparable performance of individual features to that of the combined feature sets indicates their strength as indicators of emotional processing in MDD. These findings provide valuable insights into the development of more reliable diagnostic tools and treatment monitoring strategies that focus on emotional processing in MDD.