Physiological Noise and Informative Randomness in Neural and Cardiovascular Systems
Description
Noise, often viewed as a disturbance in biomedical signal analysis, plays a crucial and informative role in the dynamics of complex physiological systems, particularly in neurocardiovascular and neural networks. Recent developments underscore noise, or stochasticity, as fundamental to physiological processes, from information transfer in the nervous system - across scales from neurons to the brain - to cardiovascular, respiratory, motor, and sensory functions.
This book offers readers a comprehensive perspective on physiological noise, redefining it not as an artifact, as traditionally viewed, but as an inherent component of physiological systems. It explores noise phenomenology within the neuro-cardiovascular system across multiple scales and presents the primary mathematical methods for estimating intrinsic noise.
Building on foundational approaches, the book introduces recent noise estimation techniques and details the validation of these stochastic component estimates using synthetic data and their applications to real physiological systems. The methods ultimately position noise as a promising biomarker for differentiating between various physio-pathologic states.
This book is designed for undergraduate and master's students, as well as PhD students and researchers in biomedical engineering and physiology. Its broad appeal stems from the subject's multidisciplinary nature and the structured progression of concepts, which helps readers build a strong foundation for understanding the key methods presented. Readers will be guided through a rigorous, formal exploration of essential tools, making analyses and results accessible. Illustrative figures and informal explanations of core concepts are included to enhance intuitive understanding.
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Andrea Scarciglia obtained his bacherlor's (2016) and master's degree (2020) in Mathematics. He is currently a post-doctoral PhD fellow at the Bioengineering and Robotics Research Centre "Enrico Piaggio", University of Pisa. His research interests are in the fields of nonlinear dynamical system theory, Information Theory, Noise and Complexity quantification in physiological time series.
Claudio Bonanno is an associate professor of mathematical physics at the Department of Mathematics of the University of Pisa. He has obtained the PhD in Mathematics at the University of Pisa in 2003, and held positions at the Ecole Polytechnique (Paris, France) and the INdAM (Italy). His research interests focus on infinite ergodic theory and its applications to continued fraction algorithms, and on applications of ergodic methods to the study of time series. Since November 2020, he has been in charge of the group "DinAmicI" inside the Italian Mathematical Union.
Gaetano Valenza is currently Associate Professor of Bioengineering at the University of Pisa, Pisa, Italy, and head of the Neuro-Cardiovascular Intelligence Lab. His research interests include statistical and nonlinear biomedical signal and image processing, cardiovascular and neural modeling, physiologically interpretable artificial intelligence systems, and wearable systems for physiological monitoring. Applications of his research include the assessment of autonomic nervous system activity on cardiovascular control, brain-heart interactions, affective computing, assessment of mood and mental/neurological disorders. He is an author of more than 300 international scientific contributions in these fields published in peer-reviewed international journals, conference proceedings, books and book chapters.
Content
Introduction and Key Definitions.- Formal Definition of Noise and Noise Estimation Techniques.- Physiological Noise in NeuroCardiovascular Systems.- Results on Syntethic Data.- Results on Real Physiological Signals.- Discussions.
