An accurate automated schizophrenia detection using TQWT and statistical moment based feature extraction. (July 2021) - Baygin, Mehmet
Highlights: Automated classification of normal and schizophrenia disease using EEG signals. A new robust statistical machine learning model is presented. TQWT is used to decompose the signal. Proposed method attained 99.12 % classification accuracy. Abstract: Nowadays, abnormal brain activities can be automatically detected and classified by processing EEG signals. In this paper, the classification process of EEG signals collected from healthy and schizophrenic individuals was carried out. For this purpose, firstly, feature extraction from 19-channel EEG signals with healthy and schizophrenia classes was performed using Tunable Q-Factor Wavelet Transform (TQWT) and statistical moment methods. After this process, feature selection was made using the ReliefF method and the extracted features were reduced. In the last stage of the study, these features were classified using the ensemble subspace k-Nearest Neighbor (kNN) method. As a result of the proposed method, performance values ​​for each channel were calculated and 99.12 % accuracy was achieved. When the obtained results are compared with the studies in the literature, it is seen that the method is extremely fast and successful.