  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.
