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Smart watches and other wearable devices are equipped withphotoplethysmography (PPG) sensors for monitoring heart rate and other aspectsof cardiovascular health. However, PPG signals collected from such devices aresusceptible to corruption from noise and motion artifacts, which cause errorsin heart rate estimation. Typical denoising approaches filter or reconstructthe signal in ways that eliminate much of the morphological information, evenfrom the clean parts of the signal that would be useful to preserve. In thiswork, we develop an algorithm for denoising PPG signals that reconstructs thecorrupted parts of the signal, while preserving the clean parts of the PPGsignal. Our novel framework relies on self-supervised training, where weleverage a large database of clean PPG signals to train a denoisingautoencoder. As we show, our reconstructed signals provide better estimates ofheart 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 ouralgorithm denoises PPG signals in a way that can improve downstream analysis ofmany different health metrics from wearable devices.

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