title: Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features.
abstract: Recently, an increasing number of researchers have endeavored to develop practical tools for diagnosing patients with schizophrenia using machine learning techniques applied to EEG biomarkers. Although a number of studies showed that source-level EEG features can potentially be applied to the differential diagnosis of schizophrenia, most studies have used only sensor-level EEG features such as ERP peak amplitude and power spectrum for machine learning-based diagnosis of schizophrenia. In this study, we used both sensor-level and source-level features extracted from EEG signals recorded during an auditory oddball task for the classification of patients with schizophrenia and healthy controls. EEG signals were recorded from 34 patients with schizophrenia and 34 healthy controls while each subject was asked to attend to oddball tones. Our results demonstrated higher classification accuracy when source-level features were used together with sensor-level features, compared to when only sensor-level features were used. In addition, the selected sensor-level features were mostly found in the frontal area, and the selected source-level features were mostly extracted from the temporal area, which coincide well with the well-known pathological region of cognitive processing in patients with schizophrenia. Our results suggest that our approach would be a promising tool for the computer-aided diagnosis of schizophrenia.
authors.affiliation: Department of Biomedical Engineering, Hanyang University, Seoul, South Korea.
authors.forename: Miseon
authors.initials: M
authors.lastname: Shim
authors.affiliation: Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, South Korea.
authors.forename: Han-Jeong
authors.initials: HJ
authors.lastname: Hwang
authors.affiliation: Berlin Institute of Technology, Machine Learning Group, Marchstrasse 23, Berlin 10587, Germany.
authors.forename: Do-Won
authors.initials: DW
authors.lastname: Kim
authors.affiliation: Psychiatry Department, Ilsan Paik Hospital, Inje University, Goyang, South Korea.
authors.forename: Seung-Hwan
authors.initials: SH
authors.lastname: Lee
authors.affiliation: Department of Biomedical Engineering, Hanyang University, Seoul, South Korea. Electronic address: ich@hanyang.ac.kr.
authors.forename: Chang-Hwan
authors.initials: CH
authors.lastname: Im
mesh_terms: D000328:Adult; D001307:Auditory Perception / Q000502:physiology; D003071:Cognition / Q000502:physiology; D003936:Diagnosis, Computer-Assisted; D004569:Electroencephalography / Q000379:methods; D005071:Evoked Potentials; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D009483:Neuropsychological Tests; D011569:Psychiatric Status Rating Scales; D012559:Schizophrenia / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology*; D012815:Signal Processing, Computer-Assisted
keywords: Computer-aided diagnosis; Event-related potential (ERP); Machine learning; Schizophrenia; Source-level features