Contactless Seismocardiography via Deep Learning Radars

Ha, Unsoo, Salah Assana, and Fadel Adib. "Contactless seismocardiography via deep learning radars." Proceedings of the 26th Annual International Conference on Mobile Computing and Networking. 2020.


The seismocardiogram (SCG) is a recording of a human heart's mechanical activity. It captures fine-grained cardiovascular events such as the opening and closing of heart valves and the contraction and relaxation of heart chambers. Today, SCG recordings are obtained by strapping an accelerometer at the apex of the heart to measure chest wall vibrations. These recordings can be used to diagnose and monitor various cardiovascular conditions including myocardial infarction (heart attack), coronary heart disease, and ischemia.

This paper introduces RF-SCG, a system that can capture SCG recordings without requiring any contact with the human body. The system operates by analyzing the reflections of millimeter-wave radar signals off the human body. RF-SCG can reconstruct the SCG waveform, and it can time 5 cardiovascular events within individual heartbeats with high accuracy. Our design is based on a hybrid architecture that combines signal processing with deep learning. The pipeline includes a 4D Cardiac Beamformer that can focus on the reflections of the human heart and a deep learning pipeline (RF-to-SCG Translator) that can transform these reflections into SCG waveforms. Empirical evaluation with 40,000 heartbeats from 21 healthy subjects demonstrates RF-SCG's ability to robustly time five key cardiovascular events (aortic valve opening, aortic valve closing, mitral valve opening, mitral valve closing, and isovolumetric contraction) with a median error between 0.26%-1.29%.

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