Tripathy Rajesh K, Zamora-Mendez Alejandro, de la O Serna José A, Paternina Mario R Arrieta, Arrieta Juan G, Naik Ganesh R
Faculty of Engineering and Technology (ITER), Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, India.
Electrical Engineering Faculty, Universidad Michoacana de San Nicolas de Hidalgo, Morelia, Mexico.
Front Physiol. 2018 Jun 13;9:722. doi: 10.3389/fphys.2018.00722. eCollection 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.
从心电图（ECG）中准确检测和分类危及生命的室性心律失常事件，如心室颤动（VF）和快速室性心动过速（VT），对于患者监测和除颤治疗来说是一个具有挑战性的问题。本文介绍了一种用于检测和分类危及生命的室性心律失常事件的新方法。使用数字泰勒 - 傅里叶变换（DTFT）将ECG信号分解为各种振荡模式。从ECG信号的模式泰勒 - 傅里叶系数中评估幅度特征和一种新的相位特征，即相位差（PD）。采用具有线性和径向基函数（RBF）核的最小二乘支持向量机（LS - SVM）分类器来检测和分类VT与VF、非电击与电击以及VF与非VF心律失常事件。对于非VF和VF事件的分类，使用所提出方法获得的准确率、灵敏度和特异性值分别为89.81%、86.38%和93.97%。与现有最先进特征的性能比较证明了该方法的优势。
