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Neural Computing and Applications

Erschienen in:

01.12.2005

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

verfasst von: Nurettin Acır

Erschienen in: Neural Computing and Applications | Ausgabe 4/2005

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Abstract

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.

Vorheriger Artikel Feedforward neural network for blind equalization with PSK signals

Nächster Artikel Revisiting squared-error and cross-entropy functions for training neural network classifiers

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Titel: Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm
verfasst von: Nurettin Acır
Publikationsdatum: 01.12.2005
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 4/2005
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-005-0466-z

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