Using wavelet transform and fuzzy neural network for VPC detection from the Holter ECG

IEEE Transactions on Bio-medical Engineering
Liang-Yu ShyuWeichih Hu

Abstract

A novel method for detecting ventricular premature contraction (VPC) from the Holter system is proposed using wavelet transform (WT) and fuzzy neural network (FNN). The basic ideal and major advantage of this method is to reuse information that is used during QRS detection, a necessary step for most ECG classification algorithm, for VPC detection. To reduce the influence of different artifacts, the filter bank property of quadratic spline WT is explored. The QRS duration in scale three and the area under the QRS complex in scale four are selected as the characteristic features. It is found that the R wave amplitude has a marked influence on the computation of proposed characteristic features. Thus, it is necessary to normalize these features. This normalization process can reduce the effect of alternating R wave amplitude and achieve reliable VPC detection. After normalization and excluding the left bundle branch block beats, the accuracies for VPC classification using FNN is 99.79%. Features that are extracted using quadratic spline wavelet were used successfully by previous investigators for QRS detection. In this study, using the same wavelet, it is demonstrated that the proposed feature extraction method from different WT s...Continue Reading

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Citations

Dec 9, 2010·Annals of Biomedical Engineering·Ataollah Ebrahim Zadeh, Ali Khazaee
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Aug 30, 2012·IEEE Transactions on Bio-medical Engineering·Li SunShaozi Li
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