Paper Info
Title
Extracting Fuzzy Rules for Detecting Ventricular Arrhythmias Based on NEWFM
Abstract
In the heart disease, the important problem of ECG arrhythmia is to discriminate ventricular arrhythmias from normal cardiac rhythm. This paper presents novel method based on the neural network with weighted fuzzy membership functions (NEWFM) for the discrimination of ventricular tachycardia (VT) and ventricular fibrillation (VF) from normal sinus rhythm (NSR). This paper uses two pre-processes, the Haar wavelet function and extraction feature method are carried out in order. By using these methods, six features can be generated, which are the input data of NEWFM. NEWFM classifies NSR and VT/VF beats by the trained bounded sum of weighted fuzzy membership functions (BSWFMs) using six input features from the Creighton University Ventricular Tachyarrhythmia Data Base (CUDB). The results are better than Amann's phase space reconstruction (PSR) algorithm, accuracy and specificity rates of 90.4% and 93.3%, respectively.
Year
DOI
Venue
2009
10.1007/978-3-642-01307-2_73
PAKDD
Keywords
Field
DocType
input feature,novel method,input data,normal cardiac rhythm,ventricular tachycardia,ventricular fibrillation,extraction feature method,normal sinus rhythm,detecting ventricular,extracting fuzzy rules,discriminate ventricular arrhythmias,weighted fuzzy membership function,neural network,wavelet transform,fuzzy neural network
Data mining,Pattern recognition,Ventricular fibrillation,Computer science,Fuzzy logic,Normal Sinus Rhythm,Normal cardiac rhythm,Ventricular tachycardia,Artificial intelligence,Haar wavelet,Artificial neural network,Wavelet transform
Conference
Citations 
PageRank 
References 
2
0.41
6
Authors
3
Name
Order
Citations
PageRank
Dongkun Shin1122667.83
Sang-hong Lee27211.96
Joon S. Lim39912.15