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        Wavelet-Based Kernel Construction for Heart Disease Classification

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        Author
        Nguyen, Thanh-Nghia
        Nguyen, Thanh-Hai
        Nguyen, Manh-Hung
        Livatino, Salvatore
        Attention
        2299/21708
        Abstract
        Heart disease classification plays an important role in clinical diagnoses. The performance improvement of an Electrocardiogram classifier is therefore of great relevance, but it is a challenging task too. This paper proposes a novel classification algorithm using the kernel method. A kernel is constructed based on wavelet coefficients of heartbeat signals for a classifier with high performance. In particular, a wavelet packet decomposition algorithm is applied to heartbeat signals to obtain the Approximation and Detail coefficients, which are used to calculate the parameters of the kernel. A principal component analysis algorithm with the wavelet-based kernel is employed to choose the main features of the heartbeat signals for the input of the classifier. In addition, a neural network with three hidden layers in the classifier is utilized for classifying five types of heart disease. The electrocardiogram signals in nine patients obtained from the MIT-BIH database are used to test the proposed classifier. In order to evaluate the performance of the classifier, a multi-class confusion matrix is applied to produce the performance indexes, including the Accuracy, Recall, Precision, and F1 score. The experimental results show that the proposed method gives good results for the classification of the five mentioned types of heart disease.
        Publication date
        2019-09
        Published in
        AEEE Advances in Electrical and Electronic Engineering
        Published version
        https://doi.org/10.15598/aeee.v17i3.3270
        License
        http://creativecommons.org/licenses/by/4.0/
        Other links
        http://hdl.handle.net/2299/21708
        Relations
        School of Physics, Engineering & Computer Science
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