PPGnet achieved a mean absolute error of 3.36 ± 4.1 BPM for heart rate estimation on the IEEE SPC 2015 dataset using single-channel wrist PPG without patient-specific training.
Photoplethysmogram (PPG) is increasingly used to provide monitoring of the cardiovascular system under ambulatory conditions. Wearable devices like smartwatches use PPG to allow long-term unobtrusive monitoring of heart rate in free-living conditions. PPG based heart rate measurement is unfortunately highly susceptible to motion artifacts, particularly when measured from the wrist. Traditional machine learning and deep learning approaches rely on tri-axial accelerometer data along with PPG to perform heart rate estimation. The conventional learning based approaches have not addressed the need for device-specific modeling due to differences in hardware design among PPG devices. In this paper, we propose a novel end-to-end deep learning model to perform heart rate estimation using 8-second length input PPG signal. We evaluate the proposed model on the IEEE SPC 2015 dataset, achieving a mean absolute error of 3.36±4.1BPM for HR estimation on 12 subjects without requiring patient-specific training. We also studied the feasibility of applying transfer learning along with sparse retraining from a comprehensive in-house PPG dataset for heart rate estimation across PPG devices with different hardware design.
Shyam et al. (Mon,) conducted a other in Heart rate estimation (n=72). PPGnet (Deep learning model) vs. State-of-the-art methods was evaluated on Mean Absolute Error (MAE) for heart rate estimation on IEEE SPC 2015 dataset. PPGnet achieved a mean absolute error of 3.36 ± 4.1 BPM for heart rate estimation on the IEEE SPC 2015 dataset using single-channel wrist PPG without patient-specific training.