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    Area of Science:

    • Biomedical Engineering
    • Cardiovascular Monitoring
    • Signal Processing

    Background:

    • Seismocardiography (SCG) offers noninvasive cardiac monitoring, ideal for wearables.
    • Motion artifacts in SCG signals, especially during exercise, compromise data reliability.
    • Accurate heart rate estimation in dynamic conditions remains a challenge for SCG.

    Purpose of the Study:

    • To comparatively evaluate various denoising algorithms for SCG signals.
    • To assess the impact of denoising on heart rate estimation accuracy during dynamic activities.
    • To identify optimal signal processing techniques for robust SCG-based monitoring.

    Main Methods:

    • Investigated seven denoising methods: EMD, EEMD, CEEMD, VMD, Savitzky-Golay, moving average, and wavelet decomposition.
    • Employed four heart rate estimation approaches: peak detection, enveloping, and Teager-Kaiser energy operator.
    • Collected SCG data from 20 participants during rest and stepping exercise using a wearable patch.

    Main Results:

    • Variational Mode Decomposition (VMD) and Savitzky-Golay filtering, coupled with enveloping, yielded the best performance.
    • These methods reduced heart rate estimation errors (MAPE and RMSE) by up to 38% during exercise compared to unprocessed signals.
    • Demonstrated significant improvement in SCG signal quality and heart rate accuracy in dynamic conditions.

    Conclusions:

    • Advanced denoising techniques are crucial for reliable SCG-based heart rate monitoring in ambulatory settings.
    • VMD and Savitzky-Golay filtering show promise for enhancing SCG applications during physical activity.
    • Improved SCG signal processing supports continuous, accurate cardiovascular assessment in real-world scenarios.