Model of arrhythmia classification based on fuzzy subordination degree and support vector machine

Objective:To discuss the arrhythmia classification method based on the wave's characteristics by using fuzzy subordination degree and support vector machine(SVM) technology.Methods:The electrocardiosignal of MIT-BIH arrhythmia standard database was pre-processed,and QRS waves were identified and located.Electrocardiosignal was clustered by making use of the similarity of electrocardiosignal waves and having QRS waves as the center.The characteristic parameters were Abstracted from electrocardiosignal and fuzzified to build arrhythmia characteristic parameter set.The model of arrhythmia classification was established using the technology of SVM.Results:The classification performance which was assessed by the MIT-BIH arrhythmia database reached a total accuracy of 97.2%.Conclusions:This algorithm has a high accuracy of classification to the electrocardiosignal of MIT-BIH arrhythmia standard database and is quite practicable.