Abstract
Computer-aided diagnostic systems (CAD) have been extensively used in medical applications to identify neurological abnormalities. It enhances diagnostic accuracy, decreases misdiagnosis rates, and provides reliable decision support. The proposed manuscript has introduced an automated CAD system that uses EEG signals to detect depression. The proposed automated CAD system is designed to work with a reduced set of electrodes. This manuscript introduces a method for electrode ranking based on the Fisher score. Electrodes are selected based on the threshold criteria. EEG is spatio-temporal data. The spatial and temporal self-attention mechanism is used to extract spatial and temporal features in the spatial and temporal domain. The proposed automated CAD system reduces the requirements of electrodes by an order of magnitude without compromising the performance of the model. The proposed CAD system has a higher degree of automation. It reduces the cost of the scan, experimental time, and complexity of the system.
Published Version
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