A Pyramidal Spatial-based Feature Attention Network for Schizophrenia Detection using Electroencephalography Signals

Automatic signal classification is utilized in various medical and industrial applications, particularly in schizophrenia (SZ) diagnosis, one of the most prevalent chronic neurological diseases. SZ is a significant mental illness that negatively affects a person’s behavior by causing things like speech impairment and delusions. In this study, electroencephalography (EEG) signals, a non-invasive diagnostic technique, are being investigated to distinguish SZ patients from healthy people by proposing a pyramidal spatial-based feature attention network (PSFAN). The proposed PSFAN consists of dilated convolutions to extract multiscale deep features in a pyramidal fashion from 2-dimensional images converted from 4-sec EEG recordings. Then, each level of the pyramid includes a spatial attention block (SAB) to concentrate on the robust features that can identify SZ patients. Finally, all the SAB feature maps are concatenated and fed into dense layers, followed by a Softmax layer for classification purposes. The performance of the PSFAN is evaluated on two datasets using three experiments, namely the subject-dependent, subject-independent, and cross-dataset. Moreover, statistical hypothesis testing is performed using Wilcoxon’s Rank-Sum test to signify the model performance. Experimental results show that the PSFAN statistically defeats 11 contemporary methods, proving its effectiveness for medical industrial applications. Source code: https://github.com/KarnatiMOHAN/PSFAN-Schizophrenia-Identification-using-EEG-signals.