Inferring ECG from PPG for Continuous Cardiac Monitoring Using Lightweight Neural Network

9 Dec 2020  ·  Yuenan Li, Xin Tian, Qiang Zhu, Min Wu ·

This paper presents a computational solution for continuous cardiac monitoring. While some smartwatches now allow users to obtain a 30-second ECG test by tapping a built-in bio-sensor, these short-term ECG tests often miss intermittent and asymptomatic abnormalities of the heart. It is also infeasible to expect persistently active user participation for long-term continuous cardiac monitoring in order to capture these and other types of abnormalities of the heart. To alleviate the need for continuous user attention and active participation, a lightweight neural network is designed to infer electrocardiogram (ECG) from the photoplethysmogram (PPG) signal sensed at the skin surface by a wearable optical sensor. To increase the utility of reconstructed ECG signals for screening cardiovascular diseases (CVDs), a diagnosis-oriented training strategy is developed to encourage the neural network to capture the pathological features of ECG. Model interpretation can be leveraged to obtain insights from data-driven models, for example, to reveal some associations between CVDs and ECG/PPG and to demonstrate how the neural network copes with motion artifacts in the ambulatory application. The experimental results on three datasets demonstrate the feasibility of inferring ECG from PPG, achieving a high fidelity of ECG reconstruction with only about 40K parameters.

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