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KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate From a Smartwatch.

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This study introduces KID-PPG, a novel deep learning model that improves heart rate extraction from photoplethysmography (PPG) signals by integrating expert knowledge. The model significantly enhances accuracy, even with motion artifacts, for better biomedical applications.

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Area of Science:

Biomedical Engineering
Signal Processing
Artificial Intelligence

Background:

Photoplethysmography (PPG) signal analysis for heart rate extraction faces challenges from motion artifacts and signal degradation.
Current deep learning models often neglect valuable domain knowledge from medical and signal processing fields.

Purpose of the Study:

To address limitations in deep learning for PPG signal analysis, specifically motion artifact removal, degradation assessment, and physiologically plausible analysis.
To develop a knowledge-informed deep learning model for more accurate heart rate extraction.

Main Methods:

Proposed KID-PPG, a knowledge-informed deep learning model integrating expert knowledge.
Utilized adaptive linear filtering, deep probabilistic inference, and data augmentation.
Evaluated the model on the PPGDalia dataset.

Main Results:

Achieved an average mean absolute error of 2.85 beats per minute in heart rate extraction.
Outperformed existing reproducible methods on the PPGDalia dataset.
Demonstrated significant performance improvement in heart rate tracking.

Conclusions:

Incorporating prior expert knowledge into deep learning models enhances PPG signal analysis.
KID-PPG shows promise for improving heart rate tracking accuracy in various biomedical applications.
This approach offers a pathway for integrating existing knowledge into AI models for healthcare.