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


CONCLUSIONS
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.
Usher
J
J
Department of Electronic Engineering, Biomedical Engineering, La Trobe University, Melbourne, Victoria, Australia.
Campbell
D
D
Vohra
J
J
Cameron
J
J
D001145:Arrhythmias, Cardiac / Q000503:physiopathology / Q000628:therapy*; D017147:Defibrillators, Implantable; D016002:Discriminant Analysis; D004594:Electrophysiology / Q000295:instrumentation*; D004867:Equipment Design; D006339:Heart Rate; D006801:Humans; D008838:Microcomputers; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted