Automated detection of schizophrenia using nonlinear signal processing methods

• Published in: Artificial intelligence in medicine Vol. 100; p. 101698
• Main Authors: Jahmunah, V., Lih Oh, Shu, Rajinikanth, V., Ciaccio, Edward J., Hao Cheong, Kang, Arunkumar, N., Acharya, U. Rajendra
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.09.2019
• Subjects: EEG signal; Non-linear feature extraction; Performance evaluation and validation; Schizophrenia; Series splitting; SVM classifier; Schizophrenia; Series splitting; Performance evaluation and validation; EEG signal; Non-linear feature extraction; SVM classifier
• ISSN: 0933-3657; 1873-2860; 1873-2860
• DOI: 10.1016/j.artmed.2019.07.006

•Automated detection of schizophrenia is proposed.•Nonlinear features are extracted from EEG signals.•Obtained classification accuracy of 92.91% using SVM classifier.•Developed model is kept in the cloud for fast and immediate diagnosis.•Patient will be informed of the diagnosis after confirmation by the clinician. Examination of the brain’s condition with the Electroencephalogram (EEG) can be helpful to predict abnormality and cerebral activities. The purpose of this study was to develop an Automated Diagnostic Tool (ADT) to investigate and classify the EEG signal patterns into normal and schizophrenia classes. The ADT implements a sequence of events, such as EEG series splitting, non-linear features mining, t-test assisted feature selection, classification and validation. The proposed ADT is employed to evaluate a 19-channel EEG signal collected from normal and schizophrenia class volunteers. A dataset was created by splitting the raw 19-channel EEG into a sequence of 6250 sample points, which was helpful to produce 1142 features of normal and schizophrenia class patterns. Non-linear feature extraction was then implemented to mine 157 features from each EEG pattern, from which 14 of the principal features were identified based on significance. Finally, a signal classification practice with Decision-Tree (DT), Linear-Discriminant analysis (LD), k-Nearest-Neighbour (KNN), Probabilistic-Neural-Network (PNN), and Support-Vector-Machine (SVM) with various kernels was implemented. The experimental outcome showed that the SVM with Radial-Basis-Function (SVM-RBF) offered a superior average performance value of 92.91% on the considered EEG dataset, as compared to other classifiers implemented in this work.