EEG-based schizophrenia detection: integrating discrete wavelet transform and deep learning

Millions of people worldwide are afflicted with the psychological disease Schizophrenia (SZ). Symptoms of SZ include delusions, hallucinations, disoriented speech, and confused thinking. This disorder is manually diagnosed by a skilled medical practitioner. Nowadays, machine learning and deep learning techniques based on electroencephalogram (EEG) signals have been proposed to support medical practitioners. This paper proposes a deep learning system and a wavelet transform-based computer-aided detection method for detecting SZ disorder. The proposed technique aims to present a highly accurate EEG signal-based SZ detection technique. In this work, we first separate the EEG signal into sub-bands and extract the features for each sub-band using the Discrete Wavelet Transform (DWT). We have explored different mother wavelets and decomposition levels for the DWT setting; it is found that the Daubechies (db4) wavelet with 7-level decomposition performs the best for SZ detection. After obtaining the gathered features, the multilayer perceptron neural network (MLP) applies them to differentiate between SZ patients and healthy controls (HC). We validate our proposed automated SZ detection method using two publicly available datasets, Dataset-1 (DS1) with 81 records (32-HC and 49-SZ) and Dataset-2 (DS2) with 28 records (14-HC and 14-SZ), respectively. Compared with previous work, our proposed model surpasses the state-of-the-art technique for SZ detection. Our classification accuracy has increased, achieving an accuracy of 99.61% and 99.12% for DS1 and DS2. Our proposed method for identifying SZ using EEG signals is more reliable and accurate and is ready to support physicians in diagnosing SZ.