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Related Experiment Videos

Detection of ventricular tachycardia using scanning correlation analysis.

B M Steinhaus1, R T Wells, S E Greenhut

  • 1Telectronics Pacing Systems, Englewood, Colorado 80112.

Pacing and Clinical Electrophysiology : PACE
|December 1, 1990
PubMed
Summary

Temporal data compression enhances cross-correlation analysis for distinguishing normal sinus rhythm from ventricular tachycardia. This computationally efficient method enables reliable detection of ventricular tachycardia using implantable devices.

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Cross-correlation is an accurate method for distinguishing normal sinus rhythm (NSR) from ventricular arrhythmias.
  • High computational demands of cross-correlation have limited the development of implantable devices.
  • Ventricular tachycardia (VT) detection requires efficient and accurate algorithms for real-time analysis.

Purpose of the Study:

  • To investigate the efficacy of temporal data compression prior to cross-correlation for reducing computational load.
  • To assess the feasibility of developing a computationally efficient and reliable VT detection algorithm.

Main Methods:

  • Intracardiac electrograms (unipolar and bipolar) from 23 patients with NSR and VT were analyzed.
  • Data were filtered (1-11 Hz), digitized (250 samples/sec), and temporally compressed to 50 samples/sec.

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  • Data compression involved saving samples with maximum excursion from the last saved sample; average squared correlation coefficients (r2) were computed.

    • Main Results:

    • Significant separation in r2 values was observed between NSR and VT in both unipolar (0.93 vs 0.20) and bipolar (0.91 vs 0.17) configurations.
    • Template lengths of 80% of the intrinsic interval yielded optimal r2 separation.
    • Narrower templates or high-pass filtering at 3 Hz degraded r2 separation, while data compression had negligible effects on accuracy.

    Conclusions:

    • Temporal data compression significantly reduces computational requirements for cross-correlation analysis.
    • The computationally efficient cross-correlation method is a reliable detector for ventricular tachycardia.
    • This approach holds promise for the development of implantable devices for arrhythmia detection.