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.

翻译：现代可磨损装置嵌入一系列非侵入性生物标志传感器,这些传感器有望改进对疾病的检测和治疗,其中一种传感器是单铅心电图(ECG),用来测量心脏中的电信号。由于磨损装置而能够进行丰富的纵向结构的ECG测量,其数量之大,其好处是,与临床ECG相比,(例如,由于移动),其潜在隐隐性测量值的代价是不同的。在这项工作中,我们开发了一个统计模型,用于模拟ECG中结构化的噪音过程,该过程来自磨损感传感器,设计了一种有利于分析变异的节拍对拍的表示法,并设计了一种基于要素的分析方法,以锁定ECG。我们用现实的ECG模拟器和结构化的噪音模型来研究合成数据。在信号到噪音的不同层面上,我们用定量测量性能的上限,并比较线性和非线性模型的估计值。最后,我们将我们的方法应用于移动健康研究中用磨损器收集的一套ECG。