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非线性时变心血管控制的复杂性变异性评估。

Complexity Variability Assessment of Nonlinear Time-Varying Cardiovascular Control.

机构信息

Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.

Department of Information Engineering and Bioengineering and Robotics Research Centre "E. Piaggio", School of Engineering, University of Pisa, Italy.

出版信息

Sci Rep. 2017 Feb 20;7:42779. doi: 10.1038/srep42779.

DOI:10.1038/srep42779
PMID:28218249
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5316947/
Abstract

The application of complex systems theory to physiology and medicine has provided meaningful information about the nonlinear aspects underlying the dynamics of a wide range of biological processes and their disease-related aberrations. However, no studies have investigated whether meaningful information can be extracted by quantifying second-order moments of time-varying cardiovascular complexity. To this extent, we introduce a novel mathematical framework termed complexity variability, in which the variance of instantaneous Lyapunov spectra estimated over time serves as a reference quantifier. We apply the proposed methodology to four exemplary studies involving disorders which stem from cardiology, neurology and psychiatry: Congestive Heart Failure (CHF), Major Depression Disorder (MDD), Parkinson's Disease (PD), and Post-Traumatic Stress Disorder (PTSD) patients with insomnia under a yoga training regime. We show that complexity assessments derived from simple time-averaging are not able to discern pathology-related changes in autonomic control, and we demonstrate that between-group differences in measures of complexity variability are consistent across pathologies. Pathological states such as CHF, MDD, and PD are associated with an increased complexity variability when compared to healthy controls, whereas wellbeing derived from yoga in PTSD is associated with lower time-variance of complexity.

摘要

复杂系统理论在生理学和医学中的应用为广泛的生物学过程及其与疾病相关的失常的动力学的非线性方面提供了有意义的信息。然而,尚无研究调查通过量化时变心血管复杂性的二阶矩是否可以提取有意义的信息。为此,我们引入了一种称为复杂性变异性的新数学框架,其中通过随时间估计的瞬时 Lyapunov 谱的方差作为参考量化器。我们将所提出的方法应用于四项涉及心脏病学、神经病学和精神病学的疾病的示范研究:充血性心力衰竭 (CHF)、重度抑郁症障碍 (MDD)、帕金森病 (PD) 和创伤后应激障碍 (PTSD) 患者失眠的瑜伽训练方案。我们表明,源自简单时间平均的复杂性评估无法辨别自主控制的病理相关变化,并且我们证明了复杂性变异性度量之间的组间差异在各种病理中是一致的。与健康对照组相比,CHF、MDD 和 PD 等病理状态与复杂性变异性增加相关,而 PTSD 中的瑜伽带来的健康则与复杂性的时变降低相关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5df4/5316947/37317fc19d61/srep42779-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5df4/5316947/3eba3f54bfad/srep42779-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5df4/5316947/20e6b256e9e7/srep42779-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5df4/5316947/dd2f9a7a47ec/srep42779-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5df4/5316947/37317fc19d61/srep42779-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5df4/5316947/3eba3f54bfad/srep42779-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5df4/5316947/20e6b256e9e7/srep42779-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5df4/5316947/dd2f9a7a47ec/srep42779-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5df4/5316947/37317fc19d61/srep42779-f4.jpg

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