A fuzzy logic-controlled classifier for use in implantable cardioverter defibrillators

Jodie Usher, Duncan Campbell, Jitu Vohra, Jim Cameron, James Cameron

Research output: Contribution to journalArticleResearchpeer-review

28 Citations (Scopus)

Abstract

Purpose: Implantable cardioverters defibrillators (ICDs) are increasingly used in the management of life-threatening arrhythmias. Correct recognition of a treatable arrhythmia is crucial to this application. However, the computational power of microprocessors currently used in ICDs limits the range of traditional algorithms available for this application. Methods: Classification based on fuzzy inference systems (FIS) were trained to recognize different cardiac rhythms (AF, VF, SVT, VT) from the Ann Arbor Electrogram Library. The FIS used were designed using adaptive-network-based fuzzy inference methods to optimize the classification procedure. Only computational techniques suitable for ICD design were used. Results: After pretraining with the ANFIS correct rhythm classification was observed for the rhythms studied. Conclusion: In this preliminary study, successful rhythm classification was demonstrated using fuzzy logic techniques. In view of the computational efficiency this may have application in ICD design.

Original languageEnglish
Pages (from-to)183-186
Number of pages4
JournalPACE - Pacing and Clinical Electrophysiology
Volume22
Issue number1 II
DOIs
Publication statusPublished - 1999
Externally publishedYes

Keywords

  • Fuzzy logic
  • Implantable cardioverter defibrillator
  • Rhythm classification

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