An Efficient Correlated Gamma Wave-Based Lightweight Model for Schizophrenia Detection

Subodh Rajpopat, Aswini Kumar Padhi, Sunil Kumar

Abstract: Schizophrenia is a serious mental disorder that affects a person's ability to think, feel, and behave clearly. Schizophrenia detection using Electroencephalography is an en-grossing area of neuroscience. There are several researches reported on designing effective models which can detect schizophre-nia accurately, however, existing models are either complex or use an extensive number of waves to process for detecting schizophre-nia accurately. Addressing the issues mentioned above, this work presents a lightweight Schizophrenia detection model with the help of Gamma wave extraction from the electroencephalogram brain waves. The proposed model has approximately 12500 parameters with an accuracy of 99.5 % which is much lighter than the previous state-of-the-art models that contain nearly 11 million parameters. We also presented a detailed comparative result analysis among all the state-of-the-art models with the proposed model which shows the effectiveness of our proposed model.