Adaptive Window-Based SPWVD for Detection of Schizophrenia Using EEG

Schizophrenia (SZ) is a complex psychiatric disorder that affects 1% of the global population. It disrupts thought processes, perceptions, emotional responsiveness, and social interactions. Researchers have used electroencephalogram (EEG) signals as clinical indicators for a variety of neurological and psychiatric conditions, including SZ. These signals record the electrical activity of the brain. This study uses a novel time–frequency analysis and convolutional neural networks (CNNs) to detect SZ using EEG recorded from 24 SZ patients and 24 healthy controls (HCs). Initially, the Hilbert transform was used to convert EEG signals into analytic representations, which improves time–frequency resolution. After that, the proposed adaptive window-based smoothed pseudo Wigner–Ville distribution (AD-SPWVD) is applied to extract instantaneous frequency and amplitude variations from the modified signals. The AD-SPWVD coefficient values at the outputs provide a detailed depiction of neuronal dynamics, which serve as inputs to the CNN model for detecting SZ. Results show that combining the Hilbert transform and AD-SPWVD significantly improves feature extraction from EEG data, yielding informative and discriminative features. The proposed model achieved a classification accuracy of 97%, demonstrating its effectiveness in identifying neural patterns associated with SZ. This work contributes to the development of real-time, noninvasive, and automated tools that support clinicians in the early diagnosis, detection, and continuous monitoring of SZ in practical healthcare settings. Future research will validate these findings in larger cohorts and explore their generalizability across diverse clinical conditions.