Extracting Fuzzy Rules for Detecting Ventricular Arrhythmias Based on NEWFM

Authors:
Dong-Kun Shin;Sang-Hong Lee;Joon S. Lim
Affiliations:
Division of Computer, Sahmyook University, Seoul, Korea 139-742;Division of Software, Kyungwon University, Sungnam, Korea 461-701;Division of Software, Kyungwon University, Sungnam, Korea 461-701
Venue:
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Year:
2009

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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.