Detection of Life Threatening Ventricular Arrhythmia Using Digital Taylor Fourier Transform

Rajesh K. Tripathy, Alejandro Zamora-Mendez, José A. de la O Serna, Mario R.Arrieta Paternina, Juan G. Arrieta, Ganesh R. Naik

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Accurate detection and classification of life-threatening ventricular arrhythmia episodes such as ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) from electrocardiogram (ECG) is a challenging problem for patient monitoring and defibrillation therapy. This paper introduces a novel method for detection and classification of life-threatening ventricular arrhythmia episodes. The ECG signal is decomposed into various oscillatory modes using digital Taylor-Fourier transform (DTFT). The magnitude feature and a novel phase feature namely the phase difference (PD) are evaluated from the mode Taylor-Fourier coefficients of ECG signal. The least square support vector machine (LS-SVM) classifier with linear and radial basis function (RBF) kernels is employed for detection and classification of VT vs. VF, non-shock vs. shock and VF vs. non-VF arrhythmia episodes. The accuracy, sensitivity, and specificity values obtained using the proposed method are 89.81, 86.38, and 93.97%, respectively for the classification of Non-VF and VF episodes. Comparison with the performance of the state-of-the-art features demonstrate the advantages of the proposition.

Original languageEnglish
Article number722
Number of pages12
JournalFrontiers in Physiology
Volume9
Issue numberJUN
DOIs
Publication statusPublished - 13 Jun 2018
Externally publishedYes

Bibliographical note

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Keywords

  • Classifier performance
  • Life threatening arrhythmia
  • LSSVM
  • Magnitude and phase features
  • Radial basis function kernel
  • Taylor-Fourier transform

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    Tripathy, R. K., Zamora-Mendez, A., de la O Serna, J. A., Paternina, M. R. A., Arrieta, J. G., & Naik, G. R. (2018). Detection of Life Threatening Ventricular Arrhythmia Using Digital Taylor Fourier Transform. Frontiers in Physiology, 9(JUN), [722]. https://doi.org/10.3389/fphys.2018.00722