Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamics.

Autor: Rosas FE; School of Engineering and Informatics, University of Sussex, United Kingdom; Centre for Psychedelic Research, Department of Brain Science, Imperial College London, United Kingdom; Centre for Complexity Science, Imperial College London, London, United Kingdom; Centre for Eudaimonia and Human Flourishing, University of Oxford, United Kingdom. Electronic address: f.rosas@sussex.ac.uk., Candia-Rivera D; Sorbonne Université, Paris Brain Institute (ICM), INRIA, CNRS, INSERM, AP-HP, Hôpital Pitié-Salpêtrière, 75013, Paris, France., Luppi AI; University Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom; Montreal Neurological Institute, McGill University, Montreal, Canada., Guo Y; Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong., Mediano PAM; Department of Computing, Imperial College London, South Kensington, London, United Kingdom; Department of Psychology, University of Cambridge, Cambridge, United Kingdom.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Mar; Vol. 170, pp. 107857. Date of Electronic Publication: 2023 Dec 23.
DOI: 10.1016/j.compbiomed.2023.107857
Abstrakt: Recent research is revealing how cognitive processes are supported by a complex interplay between the brain and the rest of the body, which can be investigated by the analysis of physiological features such as breathing rhythms, heart rate, and skin conductance. Heart rate dynamics are of particular interest as they provide a way to track the sympathetic and parasympathetic outflow from the autonomic nervous system, which is known to play a key role in modulating attention, memory, decision-making, and emotional processing. However, extracting useful information from heartbeats about the autonomic outflow is still challenging due to the noisy estimates that result from standard signal-processing methods. To advance this state of affairs, we propose a novel approach in how to conceptualise and model heart rate: instead of being a mere summary of the observed inter-beat intervals, we introduce a modelling framework that views heart rate as a hidden stochastic process that drives the observed heartbeats. Moreover, by leveraging the rich literature of state-space modelling and Bayesian inference, our proposed framework delivers a description of heart rate dynamics that is not a point estimate but a posterior distribution of a generative model. We illustrate the capabilities of our method by showing that it recapitulates linear properties of conventional heart rate estimators, while exhibiting a better discriminative power for metrics of dynamical complexity compared across different physiological states.
Competing Interests: Declaration of competing interest None Declared
(Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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