A comparative study of wavelet families for schizophrenia detection.

Abstract

Schizophrenia (SZ) is a chronic mental disorder, affecting approximately 1% of the global population, it is believed to result from various environmental factors, with psychological factors potentially influencing its onset and progression. Discrete wavelet transform (DWT)-based approaches are effective in SZ detection. In this report, we aim to investigate the effect of wavelet and decomposition levels in SZ detection. In our study, we analyzed the early detection of SZ using DWT across various decomposition levels, ranging from 1 to 5, with different mother wavelets. The electroencephalogram (EEG) signals are processed using DWT, which decomposes them into multiple frequency bands, yielding approximation and detail coefficients at each level. Statistical features are then extracted from these coefficients. The computed feature vector is then fed into a classifier to distinguish between SZ and healthy controls (HC). Our approach achieves the highest classification accuracy of 100% on a publicly available dataset, outperforming existing state-of-the-art methods.