Paper Info
Title
Finding features for real-time premature ventricular contraction detection using a fuzzy neural network system
Abstract
Fuzzy neural networks (FNNs) have been successfully applied to generate predictive rules for medical or diagnostic data. This brief presents an approach to detect premature ventricular contractions (PVCs) using the neural network with weighted fuzzy membership functions (NEWFMs). The NEWFM classifies normal and PVC beats by the trained bounded sum of weighted fuzzy membership functions (BSWFMs) using wavelet transformed coefficients from the MIT-BIH PVC database. The eight generalized coefficients, locally related to the time signal, are extracted by the nonoverlap area distribution measurement method. The eight generalized coefficients are used for the three PVC data sets with reliable accuracy rates of 99.80%, 99.21%, and 98.78%, respectively, which means that the selected features are less dependent on the data sets. It is shown that the locations of the eight features are not only around the QRS complex that represents ventricular depolarization in the electrocardiogram (ECG) containing a Q wave, an R wave, and an S wave, but also the QR segment from the Q wave to the R wave has more discriminate information than the RS segment from the R wave to the S wave. The BSWFMs of the eight features trained by NEWFM are shown visually, which makes the features explicitly interpretable. Since each BSWFM combines multiple weighted fuzzy membership functions into one using the bounded sum, the eight small-sized BSWFMs can realize real-time PVC detection in a mobile environment.
Year
DOI
Venue
2009
10.1109/TNN.2008.2012031
IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council
Keywords
Field
DocType
real-time premature ventricular contraction,fuzzy neural network,multiple weighted fuzzy membership,fuzzy neural network system,mit-bih pvc database,real-time pvc detection,generalized coefficient,r wave,pvc data set,weighted fuzzy membership function,q wave,data mining,real time,neural network,feature selection,neural networks,heart rate variability,real time systems,time measurement,algorithms,fuzzy logic,wavelet transforms,time signal,feature extraction,wavelet transform
Weight function,Computer science,QRS complex,Artificial intelligence,Fuzzy control system,Artificial neural network,Wavelet,Wavelet transform,Pattern recognition,Fuzzy logic,Algorithm,Membership function,Machine learning
Journal
Volume
Issue
ISSN
20
3
1941-0093
Citations 
PageRank 
References 
35
2.54
14
Authors
1
Name
Order
Citations
PageRank
Joon S. Lim19912.15