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 t...

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Bibliographic Details
Published in:Schizophrenia research 2016-10, Vol.176 (2-3), p.314-319
Main Authors: Shim, Miseon, Hwang, Han-Jeong, Kim, Do-Won, Lee, Seung-Hwan, Im, Chang-Hwan
Format: Article
Language:English
Subjects:
Adult
Auditory Perception - physiology
Cognition - physiology
Computer-aided diagnosis
Diagnosis, Computer-Assisted
Electroencephalography - methods
Event-related potential (ERP)
Evoked Potentials
Female
Humans
Machine Learning
Male
Neuropsychological Tests
Psychiatric Status Rating Scales
Psychiatry
Schizophrenia
Schizophrenia - classification
Schizophrenia - diagnosis
Schizophrenia - physiopathology
Signal Processing, Computer-Assisted
Source-level features
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Summary: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.
ISSN:0920-9964
1573-2509
DOI:10.1016/j.schres.2016.05.007