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A Fuzzy Logic‐Controlled Classifier for Use in Implantable Cardioverter Defibrillators
Author(s) -
USHER JODIE,
CAMPBELL DUNCAN,
VOHRA JITU,
CAMERON JIM
Publication year - 1999
Publication title -
pacing and clinical electrophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.686
H-Index - 101
eISSN - 1540-8159
pISSN - 0147-8389
DOI - 10.1111/j.1540-8159.1999.tb00329.x
Subject(s) - medicine , fuzzy logic , fuzzy inference system , adaptive neuro fuzzy inference system , rhythm , heart rhythm , classifier (uml) , artificial intelligence , machine learning , fuzzy control system , computer science , cardiology
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.