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Complexity Variability Assessment of Nonlinear Time-Varying Cardiovascular Control
Scientific reports
Short Title: Sci.Rep.
Format: Journal Article
Publication Date: Nov 30, 2016
Pages: 42779
Sources ID: 30631
Notes: LR: 20180803; GR: F32 AT006092/AT/NCCIH NIH HHS/United States; JID: 101563288; 2016/03/21 00:00 [received]; 2016/12/30 00:00 [accepted]; 2017/02/21 06:00 [entrez]; 2017/02/22 06:00 [pubmed]; 2017/02/22 06:00 [medline]; epublish
Visibility: Public (group default)
Abstract: (Show)
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.