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Titlebook: Complex Dynamics in Physiological Systems: From Heart to Brain; Syamal K. Dana (Dr.),Prodyot K. Roy (Dr.),Jürgen K Conference proceedings

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發(fā)表于 2025-3-21 17:44:09 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Complex Dynamics in Physiological Systems: From Heart to Brain
編輯Syamal K. Dana (Dr.),Prodyot K. Roy (Dr.),Jürgen K
視頻videohttp://file.papertrans.cn/232/231428/231428.mp4
概述Covers topics on nonlinear dynamics of heart.Covers topics on diagnosis of sudden cardiac death.Covers topics on prediction of epilepsy seizures.Covers topics on behavioural psychology
叢書名稱Understanding Complex Systems
圖書封面Titlebook: Complex Dynamics in Physiological Systems: From Heart to Brain; Syamal K. Dana (Dr.),Prodyot K. Roy (Dr.),Jürgen K Conference proceedings
描述.Nonlinear dynamics has become an important field of research in recent years in many areas of the natural sciences. In particular, it has potential applications in biology and medicine; nonlinear data analysis has helped to detect the progress of cardiac disease, physiological disorders, for example episodes of epilepsy, and others. This book focuses on the current trends of research concerning the prediction of sudden cardiac death and the onset of epileptic seizures, using the nonlinear analysis based on ECG and EEG data. Topics covered include the analysis of cardiac models and neural models. The book is a collection of recent research papers by leading physicists, mathematicians, cardiologists and neurobiologists who are actively involved in using the concepts of nonlinear dynamics to explore the functional behaviours of heart and brain under normal and pathological conditions. This collection is intended for students in physics, mathematics and medical sciences, and researchers in interdisciplinary areas of physics and biology..
出版日期Conference proceedings 2009
關(guān)鍵詞Master Patient Index; STATISTICA; cardiovascular; cells; dynamics; heart rate; linear optimization; neurons
版次1
doihttps://doi.org/10.1007/978-1-4020-9143-8
isbn_softcover978-90-481-8079-0
isbn_ebook978-1-4020-9143-8Series ISSN 1860-0832 Series E-ISSN 1860-0840
issn_series 1860-0832
copyrightSpringer Science+Business Media B.V. 2009
The information of publication is updating

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Direction of Information Flow Between Heart Rate, Blood Pressure and Breathingrtant signals: blood pressure variability (BPV), heart rate variability (HRV) and breathing are taken as an example. Below we introduce a novel method of analysis of physiological signals based on local linear cross-correlation. Nonstationarity of both signals is not a problem for this new method. I
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Suppression of Turbulent Dynamics in Models of Cardiac Tissue by Weak Local Excitationsresolve the problem of suppressing the fibrillative activity of the heart by a low-voltage local electrical forcing. Such a low-energy defibrillation has a great advantage in comparison with other widespread methods since it, in particular, does not require the knowledge of the frequency of re-entra
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發(fā)表于 2025-3-22 20:35:28 | 只看該作者
Synchronization Phenomena in Networks of Oscillatory and Excitable Luo-Rudy Cellsllatory cells and mixtures of oscillatory and excitable cells. Individual cell dynamics is described by a modified Luo-Rudy model with depolarizing current. We focus on the transition from incoherent behavior to global synchronization via cluster synchronization regimes as coupling strength is incre
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Statistical physics of human heart rate in health and diseaseenchmark in biophysics, consistently defying full explanation. In our recent work, heart rate regulation by the autonomic nervous system has been shown to display remarkable fundamental properties of scale-invariance of extreme value statistics [1] in healthy heart rate fluctuations, ubiquitously ob
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Application of Empirical Mode Decomposition to Cardiorespiratory Synchronizationatory synchronization is reviewed. In the scheme, an experimental respiratory signal is decomposed into a set of intrinsic mode functions (IMFs), and one of these IMFs is selected as a respiratory rhythm to construct the cardiorespiratory synchrogram incorporating with heartbeat data. The analysis o
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