BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation (2023-06-13T00:00:00.000000Z)

TL;DR

A novel learning-based method that achieves state-of-the-art performance on several heart rate estimation benchmarks extracted from photoplethysmography signals (PPG) by incorporating the statistical distribution of heart rate changes into a hidden Markov model.

Abstract

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.

Authors

B. U. Demirel

3 papers

Valentin Bieri

1 papers

Paul Streli

1 papers

TL;DR

Abstract

Authors

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