A hybrid approach for ECG classification based on particle swarm optimization and support vector machine

Authors:
Dawid Kopiec;Jerzy Martyna
Affiliations:
Institute of Computer Science, Jagiellonian University, Cracow, Poland;Institute of Computer Science, Jagiellonian University, Cracow, Poland
Venue:
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Year:
2011

Citing 3
Cited 1

The nature of statistical learning theory

The nature of statistical learning theory
Swarm intelligence

Swarm intelligence
Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms

Pattern Recognition Letters

Discriminant analysis of multivariate time series: Application to diagnosis based on ECG signals

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.