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Recent research is revealing how cognitive processes are supported by acomplex interplay between the brain and the rest of the body, which can beinvestigated by the analysis of physiological features such as breathingrhythms, heart rate, and skin conductance. Heart rate dynamics are ofparticular interest as they provide a way to track the sympathetic andparasympathetic outflow from the autonomic nervous system, which is known toplay a key role in modulating attention, memory, decision-making, and emotionalprocessing. However, extracting useful information from heartbeats about theautonomic outflow is still challenging due to the noisy estimates that resultfrom standard signal-processing methods. To advance this state of affairs, wepropose a paradigm shift in how we conceptualise and model heart rate: insteadof being a mere summary of the observed inter-beat intervals, we introduce amodelling framework that views heart rate as a hidden stochastic process thatdrives the observed heartbeats. Moreover, by leveraging the rich literature ofstate-space modelling and Bayesian inference, our proposed framework delivers adescription of heart rate dynamics that is not a point estimate but a posteriordistribution of a generative model. We illustrate the capabilities of ourmethod by showing that it recapitulates linear properties of conventional heartrate estimators, while exhibiting a better discriminative power for metrics ofdynamical complexity compared across different physiological states.

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