Abstract
The rapid growth of wavelets and machine learning algorithms in the field of medical sciences like cardiology have encouraged researchers for fast and effective classification of heart irregularities. Electrocardiogram (ECG) is a non-linear pattern which confines critical information regarding cardiac functions. It has to be analyzed and examined profoundly for equitable diagnosis. In this paper, Dual Tree Complex Wavelet Transform (DTCWT) and Linear Discriminant Analysis (LDA) are used as a hybrid pair for ECG feature extraction. The Support Vector Machine (SVM) and Extreme Learning Machine (ELM) are effectually employed as classifiers to distinguish ECG as a normal and abnormal class. The simulated outcome shows that linear behavior of SVM and ELM results in better ECG analysis as compared to radial basis function (RBF). In addition, the performance comparison illustrates that SVM outperforms ELM in terms of accuracy while ELM clearly outperforms SVM in the time taken for training and testing of MIT/BIH dataset.
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Singh, R., Rajpal, N., Mehta, R. (2020). Dynamic ECG Classification Using Shift-Invariant DTCWT and Discriminant Analysis. In: Singh, P., Panigrahi, B., Suryadevara, N., Sharma, S., Singh, A. (eds) Proceedings of ICETIT 2019. Lecture Notes in Electrical Engineering, vol 605. Springer, Cham. https://doi.org/10.1007/978-3-030-30577-2_43
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DOI: https://doi.org/10.1007/978-3-030-30577-2_43
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