A Self-Supervised Algorithm for Denoising Photoplethysmography Signals
  for Heart Rate Estimation from Wearables
Pranay Jain Cheng Ding Cynthia Rudin Xiao Hu
Smart watches 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, which cause errors
in heart rate estimation. Typical denoising approaches filter or reconstruct
the signal in ways that eliminate much of the morphological information, even
from the clean parts of the signal that would be useful to preserve. 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 significant 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
many different health metrics from wearable devices.