A dilated convolutional neural network algorithm for PPG peak detection outperformed existing methods, achieving an overall precision of 82%, recall of 80%, and F1-score of 81%.
Does a dilated CNN algorithm improve PPG peak detection accuracy in noisy signals compared to existing methods?
A dilated CNN-based algorithm improves the accuracy of PPG peak detection in noisy, real-world conditions compared to conventional methods.
Absolute Event Rate: 81% vs 79%
Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate. Conventional methods are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for PPG signals distorted due to noise and motion artifact. Our algorithm is based on convolutional neural networks (CNNs) with dilated convolutions. We train and evaluate the proposed method using a dataset collected via smartwatches under free-living conditions in a home-based health monitoring application. A data generator is also developed to produce noisy PPG data used for model training and evaluation. The method performance is compared against other state-of-the-art methods and is tested with SNRs ranging from 0 to 45 dB. Our method outperforms the existing adaptive threshold, transform-based, and machine learning methods. The proposed method shows overall precision, recall, and F1-score of 82%, 80%, and 81% in all the SNR ranges. In contrast, the best results obtained by the existing methods are 78%, 80%, and 79%. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.
Kazemi et al. (Sat,) conducted a other in PPG signals distorted due to noise and motion artifact. Dilated convolutional neural network (CNN) peak detection algorithm vs. Existing adaptive threshold, transform-based, and machine learning methods was evaluated on Overall F1-score across SNR ranges from 0 to 45 dB. A dilated convolutional neural network algorithm for PPG peak detection outperformed existing methods, achieving an overall precision of 82%, recall of 80%, and F1-score of 81%.