Key result
The digital Taylor-Fourier transform method with an LS-SVM classifier achieved 89.81% accuracy, 86.38% sensitivity, and 93.97% specificity for classifying Non-VF and VF episodes.
Why the study?
Does digital Taylor-Fourier transform with LS-SVM improve detection and classification of life-threatening ventricular arrhythmias in ECG signals?
Does digital Taylor-Fourier transform with LS-SVM improve detection and classification of life-threatening ventricular arrhythmias in ECG signals?
The digital Taylor-Fourier transform combined with an LS-SVM classifier provides high accuracy, sensitivity, and specificity for detecting and classifying life-threatening ventricular arrhythmias from ECG signals.
May enhance VF/VT detection in monitoring; hypothesis-generating until prospectively validated.
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.
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Tripathy et al. (2018) studied Life-threatening ventricular arrhythmia (Ventricular Fibrillation and Ventricular Tachycardia) (n=57). Digital Taylor-Fourier transform (DTFT) and LS-SVM classifier vs. State-of-the-art features (TCI, VFF, SPEC, CPLX, PSR) was evaluated on Accuracy for classification of Non-VF and VF episodes. The digital Taylor-Fourier transform method with an LS-SVM classifier achieved 89.81% accuracy, 86.38% sensitivity, and 93.97% specificity for classifying Non-VF and VF episodes.
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