SCZ-SCAN: An automated Schizophrenia detection system from electroencephalogram signals

Abstract

Schizophrenia (SCZ) is a severe neurological and physiological syndrome that perverts a patient’s perception of reality. SCZ exhibits several symptoms, including hallucinations, delusions, aberrant behavior, and thinking. It affects their professional, academic, personal, and social lives. Neurologists use a variety of verbal and visual tests to determine SCZ. However, these methods are laborious, time-consuming, superficial, and vulnerable to mistakes. Therefore, it is necessary to create an automated model for SCZ detection. Convolutional neural networks have swiftly established themselves in the field of mental health care due to the growth of deep learning in recent decades. Electroencephalogram (EEG) data records the variations in the neural dynamics of human memory. Using EEG data, this study proposes an automatic SCZ detection method using separable convolution attention network (SCZ-SCAN). The proposed network employs depth-wise separable convolution and attention networks on high-level and low-level to aggregate characteristics of 2-D scalogram images acquired from the continuous wavelet transform. The depth-wise separable convolutions help to create a lightweight framework, while attention techniques concentrate on significant features and reduce futile computations by removing the transmission of irrelevant features. The proposed approach has an average classification accuracy of 99% and 95% on the IBIB-PAN and EEG data from the basic sensory task in SZ dataset. Moreover, statistical hypothesis testing is performed using Wilcoxon’s Rank-Sum test to signify the model performance and it proves that SCZ-SCAN is statistically efficient to nine cutting-edge methods.Experimental results show that the PSFAN statistically defeats 11 contemporary methods, proving its effectiveness for medical industrial applications.