CALSczNet: Convolution Neural Network with Attention and LSTM for the Detection of Schizophrenia Using EEG Signals

Abstract

Schizophrenia (SZ) is a serious psychological disorder that affects nearly 1% of the global population. The progression of SZ disorder causes severe brain damage; its early diagnosis is essential to limit adverse effects. Electroencephalography (EEG) is commonly used for SZ detection, but its manual screening is laborious, time-consuming, and subjective. Automatic methods based on machine learning have been introduced to overcome these issues, but their performance is not satisfactory due to the non-stationary nature of EEG signals. To enhance the detection performance, a novel deep learning-based method is introduced, namely, CALSczNet. It uses temporal and spatial convolutions to learn temporal and spatial patterns from EEG trials, uses Temporal Attention (TA) and Local Attention (LA) to adaptively and dynamically attend to salient features to tackle the non-stationarity of EEG signals, and finally, it employs Long Short-Term Memory (LSTM) to work out the long-range dependencies of temporal features to learn the discriminative features. The method was evaluated on the benchmark public-domain Kaggle dataset of the basic sensory tasks using 10-fold cross-validation. It outperforms the state-of-the-art methods on all conditions with 98.6% accuracy, 98.65% sensitivity, 98.72% specificity, 98.72% precision, and an F1-score of 98.65%. Furthermore, this study suggested that the EEG signal of the subject performing either simultaneous motor and auditory tasks or only auditory tasks provides higher discriminative features to detect SZ in patients. Finally, it is a robust, effective, and reliable method that will assist psychiatrists in detecting SZ at an early stage and provide suitable and timely treatment.