Schizophrenia detection from electroencephalogram signals using image encoding and wrapper-based deep feature selection approach - Utathya Aich; Arghyasree Saha; Marcin Woźniak; Muhammad Fazal Ijaz; Pawan Kumar Singh (Scientific Reports)
Abstract Schizophrenia is a persistent and serious mental illness that leads to distortions in cognition, perception, emotions, speech, self-awareness, and actions. Affecting about 1% of people worldwide, schizophrenia usually emerges in late adolescence or early adulthood. It is characterized by symptoms like hallucinations, delusions, disorganized speech, and cognitive impairments. Despite significant research efforts, the exact cause of schizophrenia is still not fully understood, highlighting the need for continuous investigation into new diagnostic and treatment methods. The electroencephalogram (EEG), which measures brain electrical activity using scalp electrodes, is crucial in schizophrenia research due to its ability to detect subtle brain activity changes due to high temporal information and provide valuable insights into brain function. Many methods have been proposed to identify schizophrenia for diagnosis. Different machine learning and deep learning models have been used to improve the detection of schizophrenia. Through transfer learning using deep learning models, relevant features are selected automatically, outperforming traditional methods in accuracy and speed. Our paper introduces a three-stage framework for detection of schizophrenia from EEG signals. An image encoding method has been used to encode EEG signals to scalogram images to get both spatial and temporal information of the time series data. Using these images in the second step, two pre-trained deep learning models are implemented using transfer learning to extract features for the detection of schizophrenia. In the third step, a newly developed Average subtraction wrapper-based feature selection method has been proposed to lower the number of irrelevant features. The proposed framework has been tested on two datasets. The first (M.S.U) dataset is from M.V. Lomonosov Moscow State University which contains EEG data of 84 individuals where 45 individuals are with schizophrenia symptoms and the rest are 39 individuals are healthy. The second RepOD dataset contains EEG data of 28 individuals where both schizophrenic and healthy individuals are equal in number. Our framework achieved 99.67% and 99.97% accuracy on the first and second dataset, respectively. On both the datasets, our proposed framework outperformed state of the art results.