ECG beats classification using multiclass support vector machines with error correcting output codes
Digital Signal Processing
Time-varying biomedical signals analysis with multiclass support vector machines
BIEN '07 Proceedings of the fifth IASTED International Conference: biomedical engineering
Usage of eigenvector methods in implementation of automated diagnostic systems for ECG beats
Digital Signal Processing
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Identification of QRS complexes in 12-lead electrocardiogram
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Combining recurrent neural networks with eigenvector methods for classification of ECG beats
Digital Signal Processing
Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals
Expert Systems with Applications: An International Journal
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IEEE Transactions on Information Technology in Biomedicine
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AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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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.