Assessing Complexity in Physiological Systems through Biomedical Signals Analysis
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 comple...
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| Format: | E-Book |
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| Sprache: | Englisch |
| Veröffentlicht: |
Basel, Switzerland
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| ISBN: | 9783039433698, 3039433695, 3039433687, 9783039433681 |
| Online-Zugang: | Volltext |
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| Abstract | 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. |
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| AbstractList | 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. |
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| Editor | Faes, Luca Castiglioni, Paolo Valenza, Gaetano |
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| SubjectTerms | aging in human population Alzheimer’s disease approximate entropy autonomic nervous function autonomic nervous system baroreflex baroreflex sensitivity (BRS) biomarker blood pressure brain brain dynamics brain functional networks brain signals cardiovascular system central autonomic network cognitive task complexity complexity analysis conditional transfer entropy correlation dimension cross-entropy data compression detrended fluctuation analysis digital volume pulse (DVP) dynamic functional connectivity ECG ectopic beat entropy event-related de/synchronization factor analysis fetal heart rate fNIRS fractal dimension fragmentation fuzzy entropy heart rate heart rate variability heart rate variability (HRV) hypobaric hypoxia information dynamics information flow interconnectivity K-means clustering algorithm labor largest Lyapunov exponent linear prediction Mathematics and Science mental arithmetics motor imagery multifractality multiscale multiscale complexity multivariate time series analysis network physiology nonlinear analysis partial information decomposition penalized regression techniques percussion entropy index (PEI) photo-plethysmo-graphy (PPG) posture preterm recurrence quantification analysis Reference, Information and Interdisciplinary subjects refined composite multiscale entropy rehabilitation medicine relative consistency Research and information: general Sampen sample entropy self-organized criticality self-similarity sEMG single-channel analysis State–space models static functional connectivity support vector machines classification time series analysis vasovagal syncope vector autoregressive model vector quantization Zipf’s law |
| Title | Assessing Complexity in Physiological Systems through Biomedical Signals Analysis |
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