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Classification of ventricular arrhythmias using empirical mode decomposition and machine learning algorithms

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Abstract

Ventricular arrhythmias such as ventricular tachycardia (VT) and ventricular fibrillation (VF) are the main life-threatening arrhythmias which have to be detected accurately by designing automated system. In this work, we propose a novel method based on ensemble empirical mode decomposition to decompose the ECG signal and classified with decision tree classifier and support vector machine (SVM) for discriminating the VT/VF conditions using informative ranked features. Total fifty-seven records of ECG signals from Creighton University Ventricular Tachyarrhythmia Database (CUDB) and the MIT-BIH Malignant Ventricular Arrhythmia Database (VFDB) database of PhysioNet were taken for evaluation. We obtained the sensitivity of 97.74%, specificity of 99% and accuracy of 98.69% in C4.5 classifier, whereas the accuracy of 90.52% was achieved with SVM classifier. These results indicate that the C4.5 algorithm is a superior approach for identification of cardiac arrhythmia class. The proposed system is an effective method that may be used to assist in decision support system in clinical practice for accurate recognition of ventricular arrhythmias.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, ITER, SOA University, Bhubaneswar, India

    Monalisa Mohanty

  2. Department of Mathematics, Silicon Institute of Technology, Bhubaneswar, India

    Manasa Dash

  3. Department of Electronics & Telecom Engineering, IIIT Bhubaneswar, Bhubaneswar, India

    Pradyut Biswal

  4. School of Electronics Engineering, KIIT Deemed To Be University, Bhubaneswar, India

    Sukanta Sabut

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  1. Monalisa Mohanty
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  2. Manasa Dash
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  4. Sukanta Sabut
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Correspondence to Sukanta Sabut.

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Mohanty, M., Dash, M., Biswal, P. et al. Classification of ventricular arrhythmias using empirical mode decomposition and machine learning algorithms. Prog Artif Intell 10, 489–504 (2021). https://doi.org/10.1007/s13748-021-00250-6

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