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A Novel Feature Enhancement Technique for ECG Arrhythmia Classification Using Discrete Anamorphic Stretch Transform

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Abstract

In this paper, one-dimensional Discrete Anamorphic Stretch Transform is proposed as an additional pre-processor for the feature extraction of the ECG signal using discrete wavelet transform in order to enhance the arrhythmia classification accuracy. Three DAST kernels: linear, sublinear, and superlinear kernels are proposed for enhancing the morphological features of the QRS complex. Its effectiveness is evaluated using two classifiers: feed-forward-based neural network and support vector machine with radial basis function. The MIT–BIH arrhythmia database and the generic cardiac beat classes such as normal (N), supraventricular ectopic (S), ventricular ectopic (V), fusion (F) and unknown beat (Q) are used for evaluating the proposed pre-processor. The training and testing of the classifier follow an inter-patient as well as intra-patient procedures. The classifier with SVM_RBF and the proposed pre-processor using DAST result in an increase in the average accuracy, sensitivity, specificity, positive predictivity, F-score and overall accuracy by 1.29%, 15.63%, 3.7%, 35.7%, 20.66%, and 2.796%, respectively, compared to that without DAST. The percentage improvement in the above performance metrics using ANN Classifier with DAST is 2.99%, 27.73%, 6.83%, 64.27%, 31.53% and 6.48%, respectively, compared to that without DAST. The morphological features obtained using DAST and DWT are also combined with RR-interval features. The combined feature set is found to have better classification accuracy than that using only morphological features. The accuracy of the proposed classifier is also found to be improved compared to many of the standard ECG classifiers reported in the literature.

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Data Availability

The data used to support the findings of this study are publically available and cited in [50].

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    R. Thilagavathy & B. Venkataramani

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Thilagavathy, R., Venkataramani, B. A Novel Feature Enhancement Technique for ECG Arrhythmia Classification Using Discrete Anamorphic Stretch Transform. Circuits Syst Signal Process 42, 277–306 (2023). https://doi.org/10.1007/s00034-022-02120-5

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