Automated detection of schizophrenia using optimal wavelet-based \(l_1\) norm features extracted from single-channel EEG

Schizophrenia (SZ) is a mental disorder, which affects the ability of human thinking, memory, and way of living. Manual screening of SZ patients is tedious, laborious and prone to human errors. Hence, we developed a computer-aided diagnosis (CAD) system to diagnose SZ patients accurately using single-channel electroencephalogram (EEG) signals. The EEG signals are nonlinear and non-stationary. Hence, we have used wavelet-based features to capture the hidden non-stationary nature present in the signal. First, the EEG signals are subjected to the the wavelet decomposition through six iterations, which yields seven sub-bands. The \(l_1\) norm is computed for each sub-band. The extracted norm features are disseminated to various classification algorithms. We have obtained the highest accuracy of 99.21% and 97.2% using K-nearest neighbor classifiers with ten-fold and leave-one-subject-out cross-validations. The developed single-channel EEG wavelet-based CAD model can help the clinicians to confirm the outcome of their manual screening and obtain an accurate diagnosis.