Detection of Life Threatening Ventricular Arrhythmia Using Digital Taylor Fourier Transform - Tripathy, Rajesh K.; Zamora-Mendez, Alejandro; de la O Serna, José A.; Paternina, Mario R. Arrieta; Arrieta, Juan G.; Naik, Ganesh R. (Frontiers in Physiology, 2018)
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.