BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation
Valentin Bieri*, Paul Streli*, Berken Utku Demirel, and Christian Holz. BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation. In Conference on Uncertainty in Artificial Intelligence (UAI) 2023.
* these authors contributed equally to this work.
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.