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Performance of an automatic arrhythmia classification algorithm: comparison to the ALTITUDE electrophysiologist panel

Deepa Mahajan1, Yanting Dong, Leslie A Saxon

  • 1Boston Scientific, St. Paul, Minnesota.

Pacing and Clinical Electrophysiology : PACE
|February 18, 2014
PubMed

Insights

An automated algorithm accurately adjudicates implantable cardioverter defibrillator (ICD) arrhythmia episodes, matching expert electrophysiologist performance. This technology offers potential for efficient analysis of clinical ICD data.

Area of Science:

  • Cardiology
  • Medical Devices
  • Machine Learning

Background:

  • Adjudicating implantable cardioverter defibrillator (ICD) treated arrhythmia episodes is time-consuming.
  • Manual review of thousands of ICD episodes is often not performed due to labor intensity.

Purpose of the Study:

  • To evaluate an automated classification algorithm for adjudicating ICD-treated arrhythmia episodes.
  • To assess the algorithm's performance against expert electrophysiologist consensus.

Main Methods:

  • A machine learning algorithm was developed using 776 arrhythmia episodes.
  • Validation involved 131 dual-chamber ICD shock episodes adjudicated by electrophysiologists (EPs).
  • Algorithm performance was compared to three-EP consensus, with and without atrial lead information.

Main Results:

  • The algorithm's overall agreement with expert consensus was comparable to three-EP consensus (95% vs 94% with atrial EGM, 91% vs 90% without).
  • No significant difference in accurate adjudication was found between the algorithm and EP consensus (OR 1.02).

Conclusions:

  • The automated algorithm demonstrates performance comparable to an expert panel for ICD arrhythmia episode adjudication.
  • This algorithm shows potential for automated analysis of clinical ICD episodes and electrogram (EGM) adjudication for research and quality assessment.
Abstract

Keywords:
CRTICDcomputingdatabasedefibrillatorselectrocardiogram

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