DEVELOPMENT OF AN XAI-BASED COMPUTER-AIDED DIAGNOSTIC SYSTEM FOR DRUG-NAÏ VE MALE MDD PATIENTS

Abstract Background Recently, deep learning-based computer-aided diagnostic (CAD) systems have been actively developed to assist in the accurate diagnosis of patients with major depressive disorder (MDD). While deep learning- based CAD systems utilizing electroencephalography (EEG) data have emerged as promising diagnostic tools, challenges such as data transparency and neurophysiological interpretability persist. Aims & Objectives The present study aims to facilitate precise diagnosis and investigation of the neurophysiological characteristics inherent to patients with Major Depressive Disorder (MDD) by utilizing explainable artificial intelligence (XAI) technology. Also, we integrate XAI into EEG-based CAD systems to increasing its practicality in terms of the number of EEG channels. Method To achieve this objective, resting-state EEG data were collected from 40 male MDD patients and 41 sex- matched healthy controls (HCs). The EEG data were band-pass filtered from 1 to 55 Hz. Subsequently, independent component analysis and common average reference techniques were applied to remove various external artifacts, such as eye blinks and electrocardiogram. After that, the data were downsampled to 200 Hz to reduce the computational cost and divided into approximately 3-minute segments to ensure uniform length across all subjects. A shallow ConvNet model developed specifically for EEG data analysis, based on the convolution neural network (CNN) algorithm, was used to classify between MDD patients and HCs [1]. To prevent overfitting and improve the generalization of the diagnostic system, a leave-one-out cross-validation approach was employed. Furthermore, the relevance scores computed using the layer-wise relevance propagation (LRP) method guided the channel selection process [2]. Results The diagnostic performance of the proposed CAD system was evaluated based on the number of selected EEG channels. The results showed a classification accuracy of 100.00% when distinguishing between MDD patients and HCs using all 62 channels. In addition, for MDD patients, higher relevance scores were observed in the prefrontal and occipital lobes of the right hemisphere. In contrast, HCs showed higher relevance scores in the prefrontal and occipital lobes of the left hemisphere, which differed from MDD patients. Notably, when employing the XAI-based channel selection algorithm, a substantial accuracy of 92.59% was achieved with only 5 channels, primarily located in the occipital lobe. The selected channels notably aligned with areas that exhibited the most discernible power spectral density (PSD) features between MDD patients and HCs. Discussion & Conclusion In the present study, we developed a deep-learning-based CAD system to assist in accurately diagnosing patients with Major Depressive Disorder (MDD). We achieved a high diagnostic performance of 100.00% without the hand-crafted feature extraction. It was found from the results that only 5 channels were sufficient to diagnose MDD patients with a high diagnostic accuracy of 92.59%. Furthermore, the proposed CAD system enabled the investigation of unique neurophysiological characteristics of MDD patients. In summary, our proposed deep learning-based CAD system not only provides high diagnostic accuracy but also offers improved practicality in terms of the number of channels used. References [1]Schirrmeister, R. T. et al, 2017, ‘Deep learning with convolutional neural networks for EEG decoding and visualization’, Human Brain Mapping, vol. 38, pp. 5391-5420 [2]Binder, A. et al, 2016, ‘Layer-wise relevance propagation for neural networks with local renormalization layers’, Artificial Neural Networks and Machine Learning, vol. 9887, pp. 63-71