Using FNN for classification of cardiac arrhythmia

Author

Hou, Jianjun ; Wang, Tao ; Wu, Beiling

Author_Institution

Northern Jiaotong Univ., Beijing, China

Volume

1

fYear

2002

fDate

28-31 Oct. 2002

Firstpage

687

Abstract

A method based on FNN (fuzzy neural network) is developed to create fuzzy membership functions for classification of cardiac arrhythmia in this paper. A FNN of Sugeno type is constructed firstly. The R-R interval and QRS complex are used as the inputs of the FNN. Then the cam delta learning algorithm is used to train the FNN through which the membership functions can be obtained. Fuzzy recognition using these membership functions can classify cardiac arrhythmia. The verification result shows that this method is effective.

Keywords

cardiology; fuzzy neural nets; learning (artificial intelligence); medical computing; pattern classification; FNN training; QRS complex; R-R interval; Sugeno type; cam delta learning algorithm; cardiac arrhythmia classification; fuzzy membership functions; fuzzy neural network; fuzzy recognition; Algorithm design and analysis; Application software; Artificial intelligence; Electrocardiography; Fuzzy neural networks; Fuzzy sets; Monitoring; Resonance; Signal analysis; Signal mapping;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON '02. Proceedings. 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering

Print_ISBN

0-7803-7490-8

Type

conf

DOI

10.1109/TENCON.2002.1181367

Filename

1181367