An Intelligent System for Early Detection of Schizophrenia Using EEG-Based Deep Learning Approach

SZ (Schizophrenia) is a multifaceted neurological disorder that demands a timely and precise diagnosis to facilitate optimal treatment outcomes. This research introduces a DL-driven framework tailored for classifying SZ through EEG signal analysis, underpinned by advanced preprocessing and feature extraction strategies. To enhance EEG signal clarity,ICA is first applied to eliminate artifacts. Subsequently, CWT is used to extract vital features across both temporal and frequency domains, ensuring that essential data is retained for classification. Three state-of-the-art DL models—TCN, 1DCNN, and LSTM—are implemented to perform classification. Of these, the TCN model, when combined with ICA and CWT, delivers the most robust results. The framework is evaluated using data from the Kaggle MLSP 2014 Schizophrenia Classification Challenge, where the TCN consistently outperforms 1DCNN and LSTM in metrics such as accuracy, precision, and recall. This underscores TCN's ability to capture temporal dynamics in EEG signals effectively.Overall, the proposed model stands out as a compelling and efficient solution for automatic schizophrenia detection, providing a promising decision-support tool for clinical applications by merging efficient preprocessing, informative feature extraction, and powerful DL methodologies.