Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm

Küçük Resim Yok

Tarih

2005

Yazarlar

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

SPRINGER

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

In this paper, we present a new system for the classification of electrocardiogram ( ECG) beats by using a fast least square support vector machine (LSSVM). Five feature extraction methods are comparatively examined in the 15-dimensional feature space. The dimension of the each feature set is reduced by using dynamic programming based on divergence analysis. After the preprocessing of ECG data, six types of ECG beats obtained from the MIT-BIH database are classified with an accuracy of 95.2% by the proposed fast LSSVM algorithm together with discrete cosine transform. Experimental results show that not only the fast LSSVM is faster than the standard LSSVM algorithm, but also it gives better classification performance than the standard backpropagation multilayer perceptron network.

Açıklama

Anahtar Kelimeler

LSSVM, ECG beat classification, feature selection, dynamic programming, backpropagation MLP

Kaynak

NEURAL COMPUTING & APPLICATIONS

WoS Q Değeri

Q4

Scopus Q Değeri

Q1

Cilt

14

Sayı

4

Künye