  Modern wearable devices are embedded with a range of noninvasive biomarker
sensors that hold promise for improving detection and treatment of disease. One
such sensor is the single-lead electrocardiogram (ECG) which measures
electrical signals in the heart. The benefits of the sheer volume of ECG
measurements with rich longitudinal structure made possible by wearables come
at the price of potentially noisier measurements compared to clinical ECGs,
e.g., due to movement. In this work, we develop a statistical model to simulate
a structured noise process in ECGs derived from a wearable sensor, design a
beat-to-beat representation that is conducive for analyzing variation, and
devise a factor analysis-based method to denoise the ECG. We study synthetic
data generated using a realistic ECG simulator and a structured noise model. At
varying levels of signal-to-noise, we quantitatively measure an upper bound on
performance and compare estimates from linear and non-linear models. Finally,
we apply our method to a set of ECGs collected by wearables in a mobile health
study.
