KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate from a Smartwatch
This work addresses motion artifacts and signal degradation in heart rate tracking for biomedical applications, representing an incremental improvement by combining existing knowledge with deep learning.
The paper tackled the challenge of accurately extracting heart rate from PPG signals by integrating expert knowledge into a deep learning model, achieving an average mean absolute error of 2.85 beats per minute on the PPGDalia dataset.
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.