Accurate extraction of heart rate from photoplethysmography (PPG) signals
remains challenging due to motion artifacts and signal degradation. Although
deep learning methods trained as a data-driven inference problem offer
promising solutions, they often underutilize existing knowledge from the
medical and signal processing community. In this paper, we address three
shortcomings of deep learning models: motion artifact removal, degradation
assessment, and physiologically plausible analysis of the PPG signal. We
propose KID-PPG, a knowledge-informed deep learning model that integrates
expert knowledge through adaptive linear filtering, deep probabilistic
inference, and data augmentation. We evaluate KID-PPG on the PPGDalia dataset,
achieving an average mean absolute error of 2.85 beats per minute, surpassing
existing reproducible methods. Our results demonstrate a significant
performance improvement in heart rate tracking through the incorporation of
prior knowledge into deep learning models. This approach shows promise in
enhancing various biomedical applications by incorporating existing expert
knowledge in deep learning models.

我们提出了 KID-PPG，这是一个知识引导的深度学习模型，通过自适应线性滤波、深度概率推断和数据增强，整合了专家知识，解决了深度学习模型中的三个问题：去除运动伪迹、降低信号降解，以及对光电脉动信号进行生理上合理的分析。在 PPGDalia 数据集上评估 KID-PPG，得到平均绝对误差为 2.85 次 / 分钟，超过现有可复现方法。我们的结果表明，通过将先前的专家知识整合到深度学习模型中，可以显著提高心率跟踪性能。该方法展示了在深度学习模型中整合现有专家知识，提升各种生物医学应用的潜力。