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Related Experiment Videos

Implantable cardioverter defibrillator electrogram recognition with a multilayer perceptron

S Farrugia1, H Yee, P Nickolls

  • 1Department of Electrical Engineering, University of Sydney, Australia.

Pacing and Clinical Electrophysiology : PACE
|January 1, 1993
PubMed
Summary
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New artificial neural networks can better classify arrhythmias using waveform morphology and heart rate, improving implantable cardioverter defibrillator (ICD) therapy. This approach surpasses traditional rate-based systems for optimal arrhythmia recognition.

Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Artificial Intelligence in Medicine

Background:

  • Implantable cardioverter defibrillators (ICDs) require advanced arrhythmia recognition for optimal therapy delivery.
  • Current rate-based systems are insufficient for the growing complexity of pacing and shock modalities.
  • Power and space constraints in implantable devices necessitate efficient classification techniques.

Purpose of the Study:

  • To develop and evaluate an artificial neural network (ANN) approach for classifying arrhythmias.
  • To incorporate waveform morphology alongside heart rate for improved arrhythmia detection.
  • To ensure the developed technique is compatible with the constraints of implantable devices.

Main Methods:

  • An artificial neural network was designed for arrhythmia classification.

Related Experiment Videos

  • The ANN utilizes easily extractable features characterizing ventricular intracardiac electrogram (EGM) waveform morphology and heart rate.
  • Simulations were conducted to compare the ANN's performance against existing methods.

    • Main Results:

    • The artificial neural network demonstrated superior performance compared to a rate-based classification scheme.
    • The ANN effectively utilized waveform morphology and heart rate features for accurate arrhythmia identification.
    • The proposed method shows promise for enhancing the diagnostic capabilities of implantable devices.

    Conclusions:

    • Artificial neural networks offer a viable solution for advanced arrhythmia classification in ICDs.
    • Integrating waveform morphology with heart rate improves diagnostic accuracy beyond rate-based systems.
    • This ANN-based approach meets the stringent power and space requirements for implantable cardioverter defibrillators.