Abstract
An Electrocardiography (ECG) signals, which represent the electrical activities of a human heart and provide data on its function and diseases, are crucial for the diagnosis of cardiac disorders and the identification of arrhythmias. The empirical mode decomposition (EMD) is extensively used for the classification of heart signal, although there are limitations such as noise sensitivity and sampling. In this paper, variational mode decomposition (VMD) is adopted for the extraction of features based on distinct intrinsic mode functions (IMFs). The features are fed to support vector machine (SVM) classifier with radial basis function (RBF) kernel. The VMD algorithm was implemented in Matlab and the SVM was trained with 80% data. A test accuracy of 95% was obtained from the classifier which classifies the data into normal or abnormal. This work can be applied for precise diagnosis of arrhythmia.
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Gautam, S., Shanmughasundaram, R. (2023). Classification of Heart Signal Using Variational Mode Decomposition. In: Kaiser, M.S., Xie, J., Rathore, V.S. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2022). Lecture Notes in Networks and Systems, vol 615. Springer, Singapore. https://doi.org/10.1007/978-981-19-9304-6_62
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DOI: https://doi.org/10.1007/978-981-19-9304-6_62
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