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Evaluation of a Multi-Axes Multi-Channel Heartbeat Detection Algorithm in Ballistocardiography.

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Area of Science:

  • Biomedical Engineering
  • Physiological Signal Processing
  • Wearable Health Technology

Background:

  • Ballistocardiography (BCG) offers a non-invasive method for monitoring cardiovascular activity.
  • Speech interference can significantly degrade the quality of BCG signals, impacting accuracy.
  • Estimating heart rate variability (HRV) parameters requires robust signal processing techniques.

Purpose of the Study:

  • To evaluate the efficacy of multi-axis ballistocardiographic (BCG) sensors for estimating heartbeats and HRV parameters.
  • To investigate the impact of speech interference on BCG signal quality and accuracy.
  • To develop and assess an adaptive signal processing approach for improved BCG analysis.

Main Methods:

  • Utilized multiple multi-axis ballistocardiographic (BCG) sensors to capture cardiac signals.
  • Introduced controlled speech interference during BCG recordings.
  • Implemented an adaptive signal processing technique to detect and interpolate J-peaks in disturbed signal segments.

Main Results:

  • The adaptive approach demonstrated superior accuracy in heartbeat estimation compared to single BCG channels.
  • Interpolation of J-peaks in disturbed signal parts significantly improved the reliability of HRV parameter calculations.
  • Multi-axis BCG sensors provided a richer signal dataset for analysis, even under interference.

Conclusions:

  • An adaptive signal processing strategy enhances the robustness of BCG-based cardiac monitoring in the presence of speech interference.
  • Multi-axis BCG sensor arrays, combined with adaptive algorithms, show significant potential for accurate, non-invasive heart rate and HRV assessment.
  • This method offers a promising solution for continuous cardiac monitoring in real-world environments where signal disturbances are common.