Related Experiment Video
Updated: Jan 9, 2026

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
Heartbeat Detection from Ballistocardiogram Signals Using a Transformer Network
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Longitudinal monitoring of heart rate (HR) and heart rate variability (HRV) can aid in tracking cardiovascular diseases (CVDs), sleep quality, and sleep disorders, and reflect autonomic nervous system activity, stress levels, and overall well-being. These metrics are valuable in both clinical and everyday settings. In this paper, we present a transformer network aimed primarily at detecting the precise timing of heart beats from predicted electrocardiogram (ECG) signals that are derived from input Ballistocardiogram (BCG) signals. We compared the performance of segment-based and subject-based models across three datasets: a dataset with 46 young, healthy subjects, a dataset with 28 older adults, and a combined dataset. The segment-based model demonstrated superior performance, with correlation coefficients of 0.97 for both, HR and mean heart beat interval (MHBI) when compared to ground truth ECG. This non-invasive method offers significant potential for long-term, in home HR and HRV monitoring, aiding in the early indication and prevention of cardiovascular issues.
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