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Related Experiment Videos

A comparative analysis of signal processing methods for motion-based rate responsive pacing

S E Greenhut1, E A Shreve, C P Lau

  • 1Applied Research Division, Telectronics Pacing Systems, Englewood, Colorado 80112, USA.

Pacing and Clinical Electrophysiology : PACE
|August 1, 1996
PubMed
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Comparing motion-sensing filters and algorithms in pacemakers, this study found that the best combination depends on the activity. The 1-4 Hz filter with integration algorithm performed best for walking, while 15 Hz filter with threshold crossing was optimal for cycling.

Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Signal Processing

Background:

  • Rate-responsive pacemakers adjust heart rate based on physiological demand.
  • Motion-sensing pacemakers utilize body acceleration to infer activity levels.
  • Existing comparisons of signal processing methods are often device-specific.

Purpose of the Study:

  • To objectively compare commonly used motion-sensing filters and algorithms for pacemakers.
  • To evaluate the performance of different filter/algorithm combinations across various activities.
  • To identify optimal signal processing strategies for accurate rate response.

Main Methods:

  • Recorded acceleration and ECG signals from 16 subjects during exercise and daily activities.
  • Applied 1-4 Hz and 15-Hz band-pass filters to acceleration data.

Related Experiment Videos

  • Processed filtered data using threshold crossing (TC) and integration (IN) algorithms.
  • Compared acceleration-indicated heart rate to intrinsic heart rate using root mean square difference (RMSd).

    • Main Results:

    • Most filter/algorithm combinations performed similarly across activities.
    • Specific differences noted: 1-4 Hz filter superior for uphill walking, 15 Hz filter for chair rocking.
    • TC algorithm better for bicycling, IN algorithm better for uphill walking and drilling.
    • 1-4 Hz/IN best for uphill walking; 15 Hz/TC best for bicycling.
    • 15 Hz/TC sensitive to high-frequency artifacts; 1-4 Hz/IN sensitive to chair rocking artifact.
    • 1-4 Hz/TC showed least intersubject variability and was insensitive to footwear.

    Conclusions:

    • The optimal filter/algorithm combination for motion-sensing pacemakers is activity-dependent.
    • No single combination is universally superior across all daily living and exercise activities.
    • Consideration of specific activity profiles is crucial for selecting appropriate signal processing methods.