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dc.contributor.editorCastiglioni, Paolo
dc.contributor.editorFaes, Luca
dc.contributor.editorValenza, Gaetano
dc.date.accessioned2021-05-01T15:09:28Z
dc.date.available2021-05-01T15:09:28Z
dc.date.issued2021
dc.identifierONIX_20210501_9783039433681_173
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/68427
dc.description.abstractComplexity 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.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: generalen_US
dc.subject.classificationthema EDItEUR::P Mathematics and Scienceen_US
dc.subject.otherautonomic nervous function
dc.subject.otherheart rate variability (HRV)
dc.subject.otherbaroreflex sensitivity (BRS)
dc.subject.otherphoto-plethysmo-graphy (PPG)
dc.subject.otherdigital volume pulse (DVP)
dc.subject.otherpercussion entropy index (PEI)
dc.subject.otherheart rate variability
dc.subject.otherposture
dc.subject.otherentropy
dc.subject.othercomplexity
dc.subject.othercognitive task
dc.subject.othersample entropy
dc.subject.otherbrain functional networks
dc.subject.otherdynamic functional connectivity
dc.subject.otherstatic functional connectivity
dc.subject.otherK-means clustering algorithm
dc.subject.otherfragmentation
dc.subject.otheraging in human population
dc.subject.otherfactor analysis
dc.subject.othersupport vector machines classification
dc.subject.otherSampen
dc.subject.othercross-entropy
dc.subject.otherautonomic nervous system
dc.subject.otherheart rate
dc.subject.otherblood pressure
dc.subject.otherhypobaric hypoxia
dc.subject.otherrehabilitation medicine
dc.subject.otherlabor
dc.subject.otherfetal heart rate
dc.subject.otherdata compression
dc.subject.othercomplexity analysis
dc.subject.othernonlinear analysis
dc.subject.otherpreterm
dc.subject.otherAlzheimer’s disease
dc.subject.otherbrain signals
dc.subject.othersingle-channel analysis
dc.subject.otherbiomarker
dc.subject.otherrefined composite multiscale entropy
dc.subject.othercentral autonomic network
dc.subject.otherinterconnectivity
dc.subject.otherECG
dc.subject.otherectopic beat
dc.subject.otherbaroreflex
dc.subject.otherself-organized criticality
dc.subject.othervasovagal syncope
dc.subject.otherZipf’s law
dc.subject.othermultifractality
dc.subject.othermultiscale complexity
dc.subject.otherdetrended fluctuation analysis
dc.subject.otherself-similarity
dc.subject.othersEMG
dc.subject.otherapproximate entropy
dc.subject.otherfuzzy entropy
dc.subject.otherfractal dimension
dc.subject.otherrecurrence quantification analysis
dc.subject.othercorrelation dimension
dc.subject.otherlargest Lyapunov exponent
dc.subject.othertime series analysis
dc.subject.otherrelative consistency
dc.subject.otherevent-related de/synchronization
dc.subject.othermotor imagery
dc.subject.othervector quantization
dc.subject.otherinformation dynamics
dc.subject.otherpartial information decomposition
dc.subject.otherconditional transfer entropy
dc.subject.othernetwork physiology
dc.subject.othermultivariate time series analysis
dc.subject.otherState–space models
dc.subject.othervector autoregressive model
dc.subject.otherpenalized regression techniques
dc.subject.otherlinear prediction
dc.subject.otherfNIRS
dc.subject.otherbrain dynamics
dc.subject.othermental arithmetics
dc.subject.othermultiscale
dc.subject.othercardiovascular system
dc.subject.otherbrain
dc.subject.otherinformation flow
dc.titleAssessing Complexity in Physiological Systems through Biomedical Signals Analysis
dc.typebook
oapen.identifier.doi10.3390/books978-3-03943-369-8
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783039433681
oapen.relation.isbn9783039433698
oapen.pages296
oapen.place.publicationBasel, Switzerland


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