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%...