BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation

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Jun 14, 2023
Valentin Bieri, Paul Streli, Berken Utku Demirel, Christian Holz

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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.

* Conference on Uncertainty in Artificial Intelligence (UAI) 2023. The first two authors contributed equally  
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