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The prevalence of smart devices has extended cardiac monitoring beyond the hospital setting. It is currently possible to obtain instant Electrocardiogram (ECG) test anywhere by tapping a built-in bio-sensor of a smartwatch with a hand. However, such user participation is infeasible for long-term continuous cardiac monitoring in order to capture the intermittent and asymptomatic abnormalities of the heart that short-term ECG tests often miss. In this paper, we present a computational solution for automated and continuous cardiac monitoring. A neural network is designed to jointly infer ECG and diagnose cardiovascular diseases (CVDs) from photoplethysmogram (PPG). PPG measures the variations of blood volume driven by heartbeats, and the signal can be sensed at the wrist or finger via an optical sensor. To minimize the memory consumption on mobile devices, we devise a model compression scheme for the proposed architecture. For higher trustworthiness and transparency, this study also addresses the problem of model interpretation. We analyze the latent connection between PPG and ECG as well as the CVDs-related features of PPG learned by the neural network, aiming at obtaining clinical insights from data. The quantitative comparison with prior methods on a benchmark dataset shows that our algorithm can make more accurate ECG inference. It achieves an average $F_1$ score of 0.96 in diagnosing major CVDs.
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document_parses/pdf_json/698d9ab950b4c251df6956a79c4f9a34459e1593.json
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A Lightweight Neural Network for Inferring ECG and Diagnosing Cardiovascular Diseases from PPG
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