Schizophrenia Detection using Extreme Gradient Boosting with Enhanced Features on PYNQ-Z2

Schizophrenia is a long-term brain complication that impairs speech, behavior, mood, and cognitive function. A psychiatrist’s manual patient examination is highly subjective and labor-intensive. An automated classification tool is developed using 16-channel electroencephalogram measurements on the PYNQ-Z2 platform to diagnose schizophrenia patients. The linear discriminate analysis (LDA)-based feature extraction technique is incorporated in the extreme gradient boosting (XGBoost) to achieve promising classification accuracy. The LDA handles the multicollinearity (correlation between features) in the measurements. The XGBoost provides a more direct route to the minimum error with faster convergence and has built-in support for handling missing values, making it robust toward real-world data. A comprehensive study with state-of-the-art classification techniques is performed. The efficacy of the proposed approach is evaluated concerning accuracy, sensitivity, precision, specificity, and F1 score. The proposed method is found to outperform various state-of-the-art techniques. Furthermore, we implement our algorithms on the PYNQ-Z2 board, substantiating that the proposed method is hardware-compatible.