| Autor: |
Rajesh K. Tripathy, Alejandro Zamora-Mendez, José A. de la O Serna, Mario R. Arrieta Paternina, Juan G. Arrieta, Ganesh R. Naik |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
|
| Zdroj: |
Frontiers in Physiology, Vol 9 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
1664-042X |
| DOI: |
10.3389/fphys.2018.00722 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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