A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation From Wearables

Smartwatches and other wearable devices are equipped with photoplethysmography (PPG) sensors for monitoring heart rate and other aspects of cardiovascular health. However, PPG signals collected from such devices are susceptible to corruption from noise and motion artifacts, resulting in inaccuracies during heart rate estimation. Conventional denoising methods filter or reconstruct signals in ways that eliminate morphological information, even from the clean segments of the signal that should ideally be preserved. In this work, we develop an algorithm for denoising PPG signals that reconstructs the corrupted parts of the signal, while preserving the clean parts of the PPG signal. Our novel framework relies on self-supervised training, where we leverage a large database of clean PPG signals to train a denoising autoencoder. As we show, our reconstructed signals provide better estimates of heart rate from PPG signals than the leading heart rate estimation methods. Further experiments show improvement in heart rate variability (HRV) estimation from PPG signals using our algorithm. We conclude that our algorithm denoises PPG signals in a way that can improve downstream analysis of health metrics from wearable devices.