Abstract

Dynamic physiological noise is a fundamental component of neural systems, which are characterized by intrinsic complex nonlinear dynamics. While informative, this noise can bias quantitative assessments using experimental data. Moreover, estimating this noise is challenging without specific knowledge of the system’s behavior. This talk introduces the concept of informative randomness in complex systems and showcases model-free methods for estimating physiological noise using nonlinear entropy profiles, applicable without assumptions about system dynamics. The methodological framework is applied to experimental cardiovascular and brain data collected throughout aging, including EEG and fMRI series.

Biography

Gaetano Valenza, M.Eng., Ph.D., 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, and is official reviewer of more than sixty international scientific journals and research funding agencies.