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
