BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation
This work addresses robust heart rate monitoring for healthcare applications, representing an incremental improvement with novel uncertainty handling.
The paper tackles heart rate estimation from PPG signals by introducing a learning-based method that models heart rate evolution as a hidden Markov process and uses belief propagation to refine estimates with uncertainty quantification, achieving state-of-the-art performance on eight public datasets across three cross-validation experiments.
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.