Application of Hybrid DeepLearning Architectures for Identification of Individuals with Obsessive Compulsive Disorder Based on EEG Data

Abstract

Objective: Obsessive-compulsive disorder (OCD) is a highly common psychiatric disorder. The symptoms of this condition overlap and co-occur with those of other psychiatric illnesses, making diagnosis difficult. The availability of biomarkers could be useful for aiding in diagnosis, although prior neuroimaging studies were unable to provide such biomarkers. Method: In this study, patients with OCD were classified from healthy controls using 2 different hybrid deep learning models: one-dimensional convolutional neural networks (1DCNN) together with long-short term memory (LSTM) and gradient recurrent units (GRU), respectively. Results: Both models exhibited exceptional classification accuracies in cross-validation and external validation phases. The mean classification accuracies in the cross-validation stage were 90.88% and 85.91% for the 1DCNN-LSTM and 1DCNN-GRU models, respectively. The inferior frontal, temporal, and occipital electrodes were predominant in providing discriminative features. Conclusion: Our findings underscore the potential of hybrid deep learning architectures utilizing EEG data to effectively differentiate patients with OCD from healthy controls. This promising approach holds implications for advancing clinical decision-making by offering valuable insights into diagnostic markers for OCD.