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

• Published in: Progress in neuro-psychopharmacology & biological psychiatry Vol. 102; p. 109960
• Main Authors: Kim, Yong-Wook, Kim, Sungkean, Shim, Miseon, Jin, Min Jin, Jeon, Hyeonjin, Lee, Seung-Hwan, Im, Chang-Hwan
• Format: Journal Article
• Language: English
• Published: England Elsevier Inc 30.08.2020
• Subjects: Classification; Computer-aided diagnosis; Machine learning; Post-traumatic stress disorder; Riemannian geometry; PSD; DSM-V; ERP; qEEG; BE; LDA; BCI; SVM; AUC; PCA; Riemannian geometry; PTSD; RF; FFT; Post-traumatic stress disorder; Machine learning; Classification; HC; SCM; Computer-aided diagnosis; CAPS; PET; FgMDM
• ISSN: 0278-5846; 1878-4216; 1878-4216
• DOI: 10.1016/j.pnpbp.2020.109960

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 employed to calculate source activities. In addition, the FgMDM approach was applied to each EEG source covariance 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. •Resting EEG from PTSD and healthy subjects could be successfully classified using Riemannian-based classifier.•A Riemannian-based classifier showed better performance than conventional classifiers.•Employment of EEG source imaging and FgMDM further elevated the classification performance.