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A low-complexity intracardiac electrogram compression algorithm

R J Coggins1, M A Jabri

  • 1Department of Electrical Engineering, University of Sydney, NSW, Australia. richardc@sedal.usyd.edu.au

IEEE Transactions on Bio-Medical Engineering
|January 27, 1999
PubMed
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This study presents a novel data-compression algorithm for implantable cardioverter defibrillators (ICDs). The algorithm optimizes signal recording for low power and high reliability, adapting to patient variations for effective arrhythmia detection.

Area of Science:

  • Biomedical Engineering
  • Cardiovascular Technology
  • Signal Processing

Background:

  • Implantable cardioverter defibrillators (ICDs) manage life-threatening heart arrhythmias like bradycardia, ventricular tachycardia (VT), and ventricular fibrillation (VF).
  • Design constraints for ICDs include power consumption, reliability, and size, impacting signal recording capabilities.
  • Existing solutions often rely on patient-specific data or lack adaptability.

Purpose of the Study:

  • To develop and evaluate a data-compression algorithm for ICDs that enhances signal recording capabilities.
  • To optimize the algorithm for low power consumption and high reliability.
  • To create a patient-independent compression method adaptable to intracardiac electrogram (ICEG) variations.

Main Methods:

Related Experiment Videos

  • An adaptive scalar quantization algorithm was developed, adjusting to ICEG amplitude and phase variations.
  • The algorithm was tested against other patient-independent compression methods using VT arrhythmia data from 146 patients.
  • Performance was evaluated based on data compression rates and root mean square distortion at a 250-Hz sample rate.

    • Main Results:

    • The proposed algorithm achieved an average of 3.5 bits/sample at 5% root mean square distortion, closely approaching the Shannon lower bound.
    • It demonstrated effective compression without relying on patient-specific morphology.
    • At higher distortion levels, alternative methods like vector quantization and Karhunen-Loeve Transform showed superiority but with increased computational complexity.

    Conclusions:

    • The developed data-compression algorithm offers an efficient solution for enhancing ICD signal recording capabilities.
    • Its adaptive nature and low power consumption make it suitable for implantable devices.
    • The algorithm provides a reliable method for managing cardiac arrhythmia data within critical design constraints.