A Hybrid Approach for ECG Classification Based on Particle Swarm Optimization and Support Vector Machine

被引：0
|
作者：
Kopiec, Dawid [1 ]
Martyna, Jerzy [1 ]
机构：
[1] Jagiellonian Univ, Inst Comp Sci, PL-30348 Krakow, Poland
来源：
HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, PART I | 2011年 / 6678卷
关键词：
SYSTEM;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we describe a hybrid framework based on Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) for the ECG signal classification. By means of a specially prepared preprocessing method we extracted the most significant features from the 12-lead ECG recording from the standard ECG database. In order to reduce the dimension of the input data a particle swarm optimization (PSO) was used. The numerical results indicated that the presented classifier achieved 94.16% recognition rate.
引用
收藏
页码：329 / 337
页数：9
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