Physiological Noise and Informative Randomness in Neural and Cardiovascular Systems

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.

• 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.