A Computerized Method for Automatic Detection of Schizophrenia Using EEG Signals

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 28; no. 11; pp. 2390 - 2400
• Main Authors: Siuly, Siuly, Khare, Smith K., Bajaj, Varun, Wang, Hua, Zhang, Yanchun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; classification; Classifiers; decision tree; Decomposition; Diagnosis; EEG; Electroencephalogram; Electroencephalography; Electronic mail; empirical mode decomposition; ensemble bagged tree; Feature extraction; Frequency modulation; Interviews; K-nearest neighbor; Mental disorders; Schizophrenia; Statistical analysis; support vector machine; Support vector machines; Time-frequency analysis
• DOI: 10.1109/TNSRE.2020.3022715
• ISSN: 1534-4320; 1558-0210

Diagnosis of schizophrenia (SZ) is traditionally performed through patient's interviews by a skilled psychiatrist. This process is time-consuming, burdensome, subject to error and bias. Hence the aim of this study is to develop an automatic SZ identification scheme using electroencephalogram (EEG) signals that can eradicate the aforementioned problems and support clinicians and researchers. This study introduces a methodology design involving empirical mode decomposition (EMD) technique for diagnosis of SZ from EEG signals to perfectly handle the behavior of non-stationary and nonlinear EEG signals. In this study, each EEG signal is decomposed into intrinsic mode functions (IMFs) by the EMD algorithm and then twenty-two statistical characteristics/features are calculated from these IMFs. Among them, five features are selected as significant feature applying Kruskal Wallis test. The performance of the obtained feature set is tested through several renowned classifierson a SZ EEG dataset. Among the considered classifiers, theensemble bagged tree performed as the best classifier producing 93.21% correct classification rate for SZ, with an overall accuracy of 89.59% for IMF 2. These results indicate that EEG signals discriminate SZ patients from healthy control (HC) subjects efficiently and have the potential to become a tool for the psychiatrist to support the positive diagnosis of SZ.