ECG beat classification using particle swarm optimization and radial basis function neural network

被引：124

作者：

Koruerek, Mehmet ^{[1
]}

Dogan, Berat ^{[1
]}

机构：

[1] Istanbul Tech Univ, Dept Elect & Commun Engn, Fac Elect & Elect Engn, TR-34469 Istanbul, Turkey

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2010年 / 37卷 / 12期

关键词：

ECG; Particle swarm optimization; Radial basis function neural networks; Arrhythmia classification;

D O I：

10.1016/j.eswa.2010.04.087

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a method for electrocardiogram (ECG) beat classification based on particle swarm optimization (PSO) and radial basis function neural network (RBFNN). Six types of beats including Normal Beat, Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Atrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f) are obtained from the MIT-BIH arrhythmia database. Four morphological features are extracted from each beat after the preprocessing of the selected records. For classification stage of the extracted features, a RBFNN structure which is evolved by particle swarm optimization is used. Several experiments are performed over the test set and it is observed that the proposed method classifies ECG beats with a smaller size of network without making any concessions on the classification performance. (C) 2010 Elsevier Ltd. All rights reserved.

引用

下载

页码：7563 / 7569

页数：7

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