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

TL;DR

This paper introduces a Bayesian state-space model for heart rate dynamics that treats heart rate as a hidden stochastic process, enabling more accurate and informative estimation of autonomic outflow from noisy heartbeat data.

Contribution

It presents a novel generative Bayesian framework that models heart rate as a hidden process, improving the analysis of autonomic nervous system activity from physiological signals.

Findings

Recapitulates linear properties of traditional heart rate estimators

Provides better discrimination of dynamical complexity across physiological states

Offers a posterior distribution for heart rate dynamics rather than a point estimate

Abstract

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 paradigm shift in how we 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.