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.

翻译：我们提出了一种新颖的基于学习方法，在多个基于光电容积脉搏波（PPG）信号提取的心率估计基准测试中达到了当前最优性能。我们将心率的演变视为离散时间随机过程，并采用隐马尔可夫模型对其进行建模。通过训练后的神经网络，我们为给定PPG信号窗口推导出可能心率值的分布。利用置信传播算法，我们整合心率变化的统计分布，在时间上下文中优化这些估计结果。由此获得一个量化的心率可能值概率分布，该分布能够捕捉具有意义且校准良好的内在预测不确定性。通过三个不同交叉验证实验，我们在八个公开数据集上证明了本方法的鲁棒性。