Granger Causality And Information Transfer In Physiological Systems Basic Research And Applications

Granger causality and information transfer in physiological systems: Basic research and applications

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 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.

Nonlinearity in Living Systems: Theoretical and Practical Perspectives on Metrics of Physiological Signal Complexity

The biological basis of physiological signals is incredibly complex. While many types of research certainly appreciate molecular, cellular and systems approach to unravel overall biological complexity, in the recent decades the interest for mathematical and computational characterization of structural and functional basis underlying biological phenomena gain wide popularity among scientists. Nowadays, we witnessed wide range applications of nonlinear quantitative analysis that produced measures such as fractal dimension, power-law scaling, Hurst exponent, Lyapunov exponent, approximate entropy, sample entropy, Lempel–Ziv complexity, as well as other metrics for predictions of onset and progression of many pathological conditions, especially in the central nervous systems (CNS). In this Research Topic, we seek to bring together the recent practical and theoretical advances in the development and application of nonlinear methods or narrower fractal-based methods for characterizing the complex physiological systems at multiple levels of the organization. We will discuss the use of various complexity measures and appropriate parameters for characterizing the variety of physiological signals up to the systems level. There are multiple aims in this topic. The recent advancement in the application of nonlinear methods for both normal and pathological physiological conditions is the first. The second aim is to emphasize the more recent successful attempt to apply these methods across animal species. Finally, a comprehensive understanding of advantages and disadvantages of each method, especially between its mathematical assumptions and real-world applicability, can help to find out what is at stake regarding the above aims and to direct us toward the more fruitful application of nonlinear measures and statistics in physiology and biology in general.