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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
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Heartbeat Detection from Ballistocardiogram Signals Using a Transformer Network.

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    Summary
    This summary is machine-generated.

    This study introduces a transformer network to precisely detect heartbeats from Ballistocardiogram (BCG) signals, enabling accurate heart rate (HR) and heart rate variability (HRV) monitoring for cardiovascular health.

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    Area of Science:

    • Biomedical Engineering
    • Cardiovascular Physiology
    • Signal Processing

    Background:

    • Longitudinal monitoring of heart rate (HR) and heart rate variability (HRV) is crucial for assessing cardiovascular diseases (CVDs), sleep quality, and autonomic nervous system activity.
    • Current methods for HR/HRV monitoring may be invasive or lack long-term applicability in everyday settings.

    Purpose of the Study:

    • To develop and evaluate a transformer network for precise heartbeat timing detection from Ballistocardiogram (BCG) signals.
    • To assess the performance of segment-based versus subject-based models for HR and HRV estimation using BCG data.

    Main Methods:

    • A transformer network was designed to predict electrocardiogram (ECG) signals from input BCG signals for heartbeat detection.
    • Performance was evaluated using segment-based and subject-based models across three datasets: young adults, older adults, and a combined group.
    • Correlation coefficients against ground truth ECG were calculated for HR and mean heart beat interval (MHBI).

    Main Results:

    • The segment-based model achieved superior performance compared to the subject-based model.
    • Correlation coefficients of 0.97 were obtained for both HR and MHBI using the segment-based model against ground truth ECG.
    • The model demonstrated effectiveness across different age groups.

    Conclusions:

    • A non-invasive transformer network approach using BCG signals can accurately detect heartbeat timing for HR and HRV monitoring.
    • This method holds significant potential for long-term, at-home monitoring to aid in the early detection and prevention of cardiovascular issues.
    • The segment-based approach is recommended for enhanced accuracy in HR/HRV estimation from BCG.