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Robust PPG Peak Detection Using Dilated Convolutional Neural Networks
Kianoosh Kazemi1, Juho Laitala1, Iman Azimi1,2,3
1Department of Computing, Faculty of Technology, University of Turku, 20014 Turku, Finland.
Sensors (Basel, Switzerland)
|August 26, 2022
Summary
This study introduces a robust peak detection algorithm for photoplethysmogram (PPG) signals, enhancing accuracy even with significant noise. The convolutional neural network (CNN) based method outperforms existing techniques for reliable heart rate monitoring.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Machine Learning
Background:
- Accurate photoplethysmogram (PPG) signal peak determination is crucial for physiological monitoring, including heart rate calculation.
- Conventional peak detection methods struggle with low signal-to-noise ratio (SNR) PPG data, often corrupted by noise and motion artifacts.
- Existing algorithms are insufficient for robustly analyzing PPG signals in real-world, free-living conditions.
Purpose of the Study:
- To enhance the noise-resiliency of PPG signal analysis.
- To propose and validate a robust peak detection algorithm for noisy PPG signals, including those with motion artifacts.
- To improve the accuracy of physiological parameter extraction from low-SNR PPG data.
Main Methods:
- Development of a novel peak detection algorithm utilizing convolutional neural networks (CNNs) with dilated convolutions.
- Training and evaluation using a smartwatch-collected PPG dataset from a home-based health monitoring application.
- Implementation of a data generator to create synthetic noisy PPG data for comprehensive model assessment across various SNRs (0-45 dB).
Main Results:
- The proposed CNN-based algorithm significantly outperforms conventional adaptive threshold, transform-based, and existing machine learning methods.
- Achieved overall precision, recall, and F1-score of 82%, 80%, and 81%, respectively, across all tested SNR ranges.
- Demonstrated superior performance compared to state-of-the-art methods, which yielded a best of 78% precision, 80% recall, and 79% F1-score.
Conclusions:
- The developed algorithm provides accurate PPG peak detection, even in the presence of substantial noise and motion artifacts.
- This robust method enhances the reliability of PPG signal analysis for home-based health monitoring applications.
- The findings support the use of advanced deep learning techniques for improving physiological signal processing in challenging environments.
