AUTHOR=Tripathy Rajesh K. , Zamora-Mendez Alejandro , de la O Serna José A. , Paternina Mario R. Arrieta , Arrieta Juan G. , Naik Ganesh R. TITLE=Detection of Life Threatening Ventricular Arrhythmia Using Digital Taylor Fourier Transform JOURNAL=Frontiers in Physiology VOLUME=9 YEAR=2018 URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2018.00722 DOI=10.3389/fphys.2018.00722 ISSN=1664-042X 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.