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Riemannian classifier enhances the accuracy of machine-learning-based diagnosis of PTSD using resting EEG

Title
Riemannian classifier enhances the accuracy of machine-learning-based diagnosis of PTSD using resting EEG
Author
임창환
Keywords
Post-traumatic stress disorder; Machine learning; Computer-aided diagnosis; Classification; Riemannian geometry
Issue Date
2020-04
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
PROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY, v. 102, page. 1-8
Abstract
Recently, objective and automated methods for the diagnosis of post-traumatic stress disorder (PTSD) have attracted increasing attention. However, previous studies on machine-learning-based diagnosis of PTSD with resting-state electroencephalogram (EEG) have reported poor accuracies of as low as 60%. Here, a Riemannian geometry-based classifier, the Fisher geodesic minimum distance to the mean (FgMDM), was employed for PTSD classification for the first time. Eyes-closed resting-state EEG data of 39 healthy individuals and 42 PTSD patients were used for the analysis. EEG source activities in 148 cortical regions were parcellated based on the Destrieux atlas, and their covariances were evaluated for each individual. Thirty epochs of preprocessed EEG were em- ployed to calculate source activities. In addition, the FgMDM approach was applied to each EEG source cov- ariance to construct the classifier. For a comparison, linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF) classifiers employing source band powers and network features as feature candidates were also tested. The FgMDM classifier showed an average classification accuracy of 75.240.80%. In contrast, the maximum accuracies of LDA, SVM, and RF classifiers were 66.54 ± 2.99%, 61.11 ± 2.98%, and 60.99 ± 2.19%, respectively. Our study demonstrated that the diagnostic accuracy of PTSD with resting-state EEG could be significantly improved by employing the FgMDM framework, which is a type of Riemannian geometry-based classifier.
URI
https://www.sciencedirect.com/science/article/pii/S0278584620302761?via%3Dihubhttps://repository.hanyang.ac.kr/handle/20.500.11754/165397
ISSN
0278-5846; 1878-4216
DOI
10.1016/j.pnpbp.2020.109960
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > ELECTRICAL AND BIOMEDICAL ENGINEERING(전기·생체공학부) > Articles
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