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Layer-Wise Relevance Propagation Approach for Diagnosis of Drug-Naïve Men With Major Depressive Disorder Using
Eun-Gyoung Yi1,2,3, Miseon Shim4, Seung-Hwan Lee3,5
1Department of Electronics and Information Engineering, Korea University, Sejong-si, Republic of Korea.
None:
The advancement of artificial intelligence (AI) tools utilizing electroencephalography (EEG) for diagnosing major depressive disorder (MDD) has shown significant progress. However, the practical implementation of these tools is often impeded by the large amount of EEG data required for training AI models and the lack of explanations for the MDD diagnoses. This study aims to develop an interpretable deep-learning-based computer-aided diagnostic system for diagnosing male MDD patients using explainable AI (XAI) algorithms. The CAD system was designed to facilitate the diagnostic process by using a reduced number of EEG channels and data length while enhancing understanding of the neurophysiological characteristics of male MDD. Resting-state EEG data were collected from 40 male MDD patients (20-63 years) and 41 gender-matched healthy controls (HCs, 19-61 years). A shallow convolutional neural network (CNN; Shallow ConvNet) model was utilized to distinguish between MDD patients and HCs. Relevance scores were extracted by the layer-wise relevance propagation (LRP) method, integrated with the Shallow ConvNet, to interpret the outcomes of the deep-learning-based CAD system. Additionally, changes in diagnostic performance were assessed by progressively reducing the number of channels using an LRP-based channel selection method, as well as EEG data length. Our XAI-based CAD system showed a high diagnostic performance of 100% when using the whole 62 channels with 180-s EEG data. A relatively high diagnostic performance over 90% was retained with only five channels with 60-s EEG data. Neurophysiologically meaningful brain areas, such as fronto-central, centro-parietal, and occipital areas, also revealed significant differences in relevance scores extracted by the LRP-method between the two groups. This study successfully developed a high performance and practical XAI-based CAD system for male MDD patients. Our developed CAD system not only achieves high diagnostic accuracy but also provides meaningful neurophysiological biomarkers for male MDD patients.
