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Improved Sparse Adaptive Algorithms for Accurate Non-contact Heartbeat Detection Using Time-Window-Variation

Chen Ye, Kentaroh Toyoda, Tomoaki Ohtsuki

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |November 17, 2018
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    Summary
    This summary is machine-generated.

    This study improves heartbeat detection using Doppler radar by adapting the sparse penalty and time window in the zero-attracting sign least-mean-square (ZA-SLMS) algorithm. The enhanced method offers more accurate heart rate estimation, especially under varying noise conditions.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Radar Systems

    Background:

    • Doppler radar signals can reconstruct heartbeat spectrum using sparse adaptive algorithms like zero-attracting sign least-mean-square (ZA-SLMS).
    • Constant regularization parameters in ZA-SLMS struggle with varying noise levels caused by body motion, impacting accurate heartbeat spectrum acquisition.
    • Accurate heart rate (HR) estimation is crucial for non-invasive monitoring.

    Purpose of the Study:

    • To enhance the accuracy and stability of heartbeat detection and heart rate estimation from Doppler radar signals.
    • To address the limitations of constant regularization parameters in sparse adaptive algorithms under dynamic noise conditions.

    Main Methods:

    • Introduced an adaptive regularization parameter (AREPA) that adjusts the sparse penalty based on the standard deviation of radar data.
    • Incorporated a time-window-variation (TWV) technique to improve the stability of heartbeat detection by leveraging the stability of spectral peak positions.
    • Validated the improved ZA-SLMS algorithm against experimental data from five subjects.

    Main Results:

    • The proposed improved ZA-SLMS algorithm with AREPA and TWV demonstrates reliable improvement in heart rate estimation error compared to the standard ZA-SLMS algorithm.
    • The adaptive approach effectively handles varying noise strengths caused by different body motions.
    • Enhanced stability in heartbeat detection was observed.

    Conclusions:

    • The proposed adaptive ZA-SLMS algorithm with AREPA and TWV significantly improves the accuracy and reliability of heart rate estimation from Doppler radar signals.
    • This enhanced algorithm offers a more robust solution for non-invasive cardiac monitoring, particularly in environments with fluctuating noise levels.