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Heartbeats Arrhythmia Classification Using Quadratic Loss Multi-Class Support Vector Machines


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DOI: https://doi.org/10.15866/irecos.v11i1.8280

Abstract


The support vector machines (SVM) are a powerful approach which has been applied in many difficult applications. However, they are principally designed for binary classification problems, and usually their extension for multiclass problems involves only bi-class SVM. In this study, a recently developed machine named the quadratic loss multi-class SVM (M-SVM2), which considers all classes simultaneously, is proposed to classify five different arrhythmia, in addition to normal beat. The M-SVM2 is compared with both decomposition methods involving 2-norm binary SVM (l2-SVM) based on 1-against-1, and 1-against-all approach. The proposed model achieved an average accuracy of (99.73%), which was better compared to the other implemented classifiers. On the other hand, the study showed that post-processing the outputs of the M-SVM2 in terms of probability can significantly improve the classification decision. This results have shown the effectiveness of the proposed approach to enhance the performance of Electrocardiogram (EEG) classification methods.
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Keywords


Multi-Class Support Vector Machines (M-SVM); Quadratic Loss M-SVM (MSVM2); Electrocardiogram (ECG); Discrete Wavelet Transform (DWT); MIT-BIH Database

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References


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