Tandy School of Computer Science and Department of Mathematics, University of Tulsa, Tulsa, Oklahoma, United States of America.
Mathematics and Computer Science Department, Fontbonne University, Saint Louis, Missouri, United States of America.
PLoS One. 2018 Jul 3;13(7):e0199144. doi: 10.1371/journal.pone.0199144. eCollection 2018.
We employ a time-dependent Hurst analysis to identify EEG signals that differentiate between healthy controls and combat-related PTSD subjects. The Hurst exponents, calculated using a rescaled range analysis, demonstrate a significant differential response between healthy and PTSD samples which may lead to diagnostic applications. To overcome the non-stationarity of EEG data, we apply an appropriate window length wherein the EEG data displays stationary behavior. We then use the Hurst exponents for each channel as hypothesis test statistics to identify differences between PTSD cases and controls. Our study included a cohort of 12 subjects with half healthy controls. The Hurst exponent of the PTSD subjects is found to be significantly smaller than the healthy controls in channel F3. Our results indicate that F3 may be a useful channel for diagnostic applications of Hurst exponents in distinguishing PTSD and healthy subjects.
我们采用时变赫斯特分析来识别 EEG 信号，以区分健康对照者和与战斗相关的 PTSD 患者。通过重标极差分析计算出的赫斯特指数表明，健康组和 PTSD 组之间存在显著的差异反应，这可能为诊断应用提供依据。为了克服 EEG 数据的非平稳性，我们应用适当的窗口长度，使 EEG 数据呈现出平稳的行为。然后，我们使用每个通道的赫斯特指数作为假设检验统计量来识别 PTSD 病例和对照组之间的差异。我们的研究包括了一个由 12 名受试者组成的队列，其中一半是健康对照者。我们发现 PTSD 患者的 F3 通道的赫斯特指数显著小于健康对照组。我们的结果表明，F3 通道可能是一个有用的通道，用于通过赫斯特指数来区分 PTSD 和健康受试者的诊断应用。
