2023-07-02-bieri23a.md

abstract	openreview	title	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
We present a novel learning-based method that achieves state-of-the-art performance on several heart rate estimation benchmarks extracted from photoplethysmography signals (PPG). We consider the evolution of the heart rate in the context of a discrete-time stochastic process that we represent as a hidden Markov model. We derive a distribution over possible heart rate values for a given PPG signal window through a trained neural network. Using belief propagation, we incorporate the statistical distribution of heart rate changes to refine these estimates in a temporal context. From this, we obtain a quantized probability distribution over the range of possible heart rate values that captures a meaningful and well-calibrated estimate of the inherent predictive uncertainty. We show the robustness of our method on eight public datasets with three different cross-validation experiments.	_U6zqe5OXe	BeliefPPG: Uncertainty-aware heart rate estimation from PPG signals via belief propagation	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	bieri23a	0	{BeliefPPG}: Uncertainty-aware heart rate estimation from {PPG} signals via belief propagation	173	183	173-183	173	false	Bieri, Valentin and Streli, Paul and Demirel, Berken Utku and Holz, Christian	given family Valentin Bieri given family Paul Streli given family Berken Utku Demirel given family Christian Holz	2023-07-02		Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence	216	inproceedings	date-parts 2023 7 2	https://proceedings.mlr.press/v216/bieri23a/bieri23a.pdf	label link Supplementary PDF https://proceedings.mlr.press/v216/bieri23a/bieri23a-supp.pdf