  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.

Quantitative Methods