Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea; Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea.
Department of Psychiatry, University of Missouri-Kansas City, Kansas City, MO, USA.
Prog Neuropsychopharmacol Biol Psychiatry. 2020 Aug 30;102:109960. doi: 10.1016/j.pnpbp.2020.109960. Epub 2020 May 3.
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.
最近，用于创伤后应激障碍（PTSD）诊断的客观和自动化方法引起了越来越多的关注。然而，以前使用静息态脑电图（EEG）进行基于机器学习的 PTSD 诊断的研究报告的准确率低至 60%。在这里，首次使用基于黎曼几何的分类器——Fisher 测地线均值最小距离（FgMDM）进行 PTSD 分类。使用 39 名健康个体和 42 名 PTSD 患者的闭眼静息态 EEG 数据进行分析。根据 Destrieux 图谱将 EEG 源活动划分为 148 个皮质区域，并评估每个个体的协方差。使用 30 个预处理 EEG epochs 计算源活动。此外，还将 FgMDM 方法应用于每个 EEG 源协方差，以构建分类器。为了进行比较，还测试了采用源频带功率和网络特征作为特征候选的线性判别分析（LDA）、支持向量机（SVM）和随机森林（RF）分类器。FgMDM 分类器的平均分类准确率为 75.24±0.80%。相比之下，LDA、SVM 和 RF 分类器的最大准确率分别为 66.54±2.99%、61.11±2.98%和 60.99±2.19%。我们的研究表明，通过采用基于黎曼几何的 FgMDM 框架，静息态 EEG 对 PTSD 的诊断准确性可以显著提高，这是一种基于黎曼几何的分类器。
