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

TL;DR

This paper introduces a self-supervised denoising algorithm for PPG signals from wearables that improves heart rate and HRV estimation by reconstructing corrupted signals while preserving morphological details.

Contribution

The paper presents a novel self-supervised autoencoder framework that effectively denoises PPG signals, enhancing the accuracy of heart rate and HRV measurements from wearable devices.

Findings

Better heart rate estimation than existing methods

Significant improvement in HRV analysis accuracy

Effective preservation of signal morphology during denoising

Abstract

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.