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Titlebook: Complexity and Nonlinearity in Cardiovascular Signals; Riccardo Barbieri,Enzo Pasquale Scilingo,Gaetano V Book 2017 Springer International

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發(fā)表于 2025-3-21 18:20:15 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Complexity and Nonlinearity in Cardiovascular Signals
編輯Riccardo Barbieri,Enzo Pasquale Scilingo,Gaetano V
視頻videohttp://file.papertrans.cn/232/231667/231667.mp4
概述Expertly reviews cutting-edge research, such as recent advances in multiscale entropy and information-theoretic concepts applied to coupled dynamical systems.Comprehensively describes applications of
圖書封面Titlebook: Complexity and Nonlinearity in Cardiovascular Signals; Riccardo Barbieri,Enzo Pasquale Scilingo,Gaetano V Book 2017 Springer International
描述.This book reports on the latest advances in complex and nonlinear cardiovascular physiology aimed at ?obtaining reliable,?effective markers for the assessment of heartbeat, respiratory, and blood pressure dynamics.?The chapters describe in detail methods that have been previously defined in theoretical physics such as entropy, multifractal spectra, and Lyapunov exponents,?contextualized within physiological dynamics of cardiovascular control, including?autonomic nervous system activity. Additionally, the book discusses several application scenarios of these methods. The text critically reviews the current state-of-the-art research in the field that has led to the description?of dedicated experimental protocols and ad-hoc models of complex physiology. This text is ideal for biomedical engineers, physiologists, and neuroscientists.?..This book also:.Expertly reviews cutting-edge research, such as recent advances in measuring complexity, nonlinearity, and information-theoretic concepts applied to coupled dynamical systems..Comprehensively describes applications of analytic technique to clinical scenarios such as heart failure, depression and mental disorders, atrial fibrillation, acu
出版日期Book 2017
關(guān)鍵詞Approximate Entropy; ARFIMA-GARCH Modelling; Atrial Fibrillation; Compression Entropy; Detrended Fluctua
版次1
doihttps://doi.org/10.1007/978-3-319-58709-7
isbn_softcover978-3-319-86458-7
isbn_ebook978-3-319-58709-7
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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發(fā)表于 2025-3-21 23:04:48 | 只看該作者
https://doi.org/10.1007/978-3-642-02490-0 This chapter illustrates the main methods used in literature for assessing the self-similarity of cardiovascular time series, especially focusing on methods based on the popular Detrended Fluctuation Analysis (DFA) algorithm. In particular, it reviews applications of DFA that describe the cardiovas
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https://doi.org/10.1007/978-3-030-51041-1onal analytics are required that are sensitive yet robust enough to adequately describe this complexity. Entropy measures are a natural candidate for this application as they are able to estimate information content or complexity of the heart rate..A number of different approaches have been used, as
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Information Decomposition: A Tool to Dissect Cardiovascular and Cardiorespiratory Complexityll as the interaction between cardiovascular and cardiorespiratory effects. The analysis of head-up and head-down tilt test protocols demonstrates the relevance of information decomposition in dissecting cardiovascular control mechanisms and accept or reject physiological hypotheses about their acti
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發(fā)表于 2025-3-23 01:09:16 | 只看該作者
Self-Similarity and Detrended Fluctuation Analysis of Cardiovascular Signals This chapter illustrates the main methods used in literature for assessing the self-similarity of cardiovascular time series, especially focusing on methods based on the popular Detrended Fluctuation Analysis (DFA) algorithm. In particular, it reviews applications of DFA that describe the cardiovas
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Time-Frequency Analysis of Cardiovascular Signals and Their Dynamic Interactionsmic interactions between two or more non-stationary processes. Time-frequency coherence, phase-delay, phase-locking and partial-spectra are estimators that assess changes in the coupling and phase shift of signals generated by a complex system..This chapter introduces the reader to multivariate time
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