Assessing Complexity in Physiological Systems through Biomedical Signals Analysis

Edited by
March 2021
296 pages
  • ISBN978-3-03943-368-1 (Hardback)
  • ISBN978-3-03943-369-8 (PDF)

This book is a reprint of the Special Issue Assessing Complexity in Physiological Systems through Biomedical Signals Analysis that was published in

Chemistry & Materials Science
Computer Science & Mathematics
Physical Sciences
Complexity is a ubiquitous phenomenon in physiology that allows living systems to adapt to external perturbations. Fractal structures, self-organization, nonlinearity, interactions at different scales, and interconnections among systems through anatomical and functional networks, may originate complexity. Biomedical signals from physiological systems may carry information about the system complexity useful to identify physiological states, monitor health, and predict pathological events. Therefore, complexity analysis of biomedical signals is a rapidly evolving field aimed at extracting information on the physiological systems. This book consists of 16 contributions from authors with a strong scientific background in biomedical signals analysis. It includes reviews on the state-of-the-art of complexity studies in specific medical applications, new methods to improve complexity quantifiers, and novel complexity analyses in physiological or clinical scenarios. It presents a wide spectrum of methods investigating the entropic properties, multifractal structure, self-organized criticality, and information dynamics of biomedical signals touching upon three physiological areas: the cardiovascular system, the central nervous system, the heart-brain interactions. The book is aimed at experienced researchers in signal analysis and presents the latest trends in the complexity methods in physiology and medicine with the hope of inspiring future works advancing this fascinating area of research.
  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
autonomic nervous function; heart rate variability (HRV); baroreflex sensitivity (BRS); photo-plethysmo-graphy (PPG); digital volume pulse (DVP); percussion entropy index (PEI); heart rate variability; posture; entropy; complexity; cognitive task; sample entropy; brain functional networks; complexity; dynamic functional connectivity; static functional connectivity; K-means clustering algorithm; heart rate variability; entropy; fragmentation; aging in human population; factor analysis; support vector machines classification; Sampen; cross-entropy; autonomic nervous system; heart rate; blood pressure; hypobaric hypoxia; rehabilitation medicine; labor; fetal heart rate; entropy; data compression; complexity analysis; nonlinear analysis; preterm; Alzheimer’s disease; complexity; brain signals; single-channel analysis; biomarker; refined composite multiscale entropy; complexity; central autonomic network; heart rate variability; interconnectivity; sample entropy; heart rate variability; ECG; ectopic beat; baroreflex; heart rate variability; self-organized criticality; vasovagal syncope; Zipf’s law; multifractality; multiscale complexity; detrended fluctuation analysis; heart rate; blood pressure; self-similarity; sEMG; approximate entropy; sample entropy; fuzzy entropy; fractal dimension; recurrence quantification analysis; detrended fluctuation analysis; correlation dimension; largest Lyapunov exponent; time series analysis; sample entropy; relative consistency; event-related de/synchronization; entropy; motor imagery; vector quantization; information dynamics; partial information decomposition; entropy; conditional transfer entropy; network physiology; multivariate time series analysis; State–space models; vector autoregressive model; penalized regression techniques; linear prediction; fNIRS; entropy; complexity analysis; nonlinear analysis; brain dynamics; mental arithmetics; motor imagery; entropy; multifractality; multiscale; cardiovascular system; brain; information flow