Simplifying Depression Diagnosis: Single-Channel EEG and Deep Learning Approaches

Abstract

Major depressive disorder (MDD) or depression is a chronic mental illness that significantly impacts individuals' well-being and is often diagnosed at advanced stages, increasing the risk of suicide. Current diagnostic practices, which rely heavily on subjective assessments and patient self-reports, are often hindered by challenges such as under-reporting and the failure to detect early, subtle symptoms. Early detection of MDD is crucial and requires monitoring vital signs in daily living conditions. The electroencephalogram (EEG) is a valuable tool for monitoring brain activity, providing critical information on MDD and its underlying neurological mechanisms. While traditional EEG systems typically involve multiple channels for recording, making them impractical for home-based monitoring, wearable sensors can effectively capture single-channel EEG data. However, generating meaningful features from these data poses challenges due to the need for specialized domain knowledge and significant computational power, which can hinder real-time processing. To address these issues, our study focuses on developing a deep learning model for the binary classification of MDD using single-channel EEG data. We focused on specific channels from various brain regions such as central, frontal, occipital, temporal, and parietal. Our study found that the channels Fp1, F8 and Cz achieved an impressive accuracy of 90% when analyzed using a Convolutional Neural Network (CNN) with leave-one-subject-out cross-validation on a public dataset. Our study highlights the potential of utilizing single-channel EEG data for reliable MDD diagnosis, providing a less intrusive and more convenient wearable solution for mental health assessment.