Key result
The proposed wavelet decomposition algorithm with adaptive threshold achieved 100% sensitivity for ventricular fibrillation and 97% sensitivity for ventricular tachycardia.
A wavelet decomposition algorithm with adaptive threshold effectively detects and classifies ventricular arrhythmias from ECG signals with high sensitivity and specificity.
May aid automated VF/VT detection in ECG analysis; leaves open clinical integration pending prospective trials.
In this paper an algorithm has been proposed to detect and classify the cardiac arrhythmia from a normal Electro Cardio Graphic (ECG) signal based on wavelet decomposition with adaptive threshold.The MIT -BIH arrhythmia and malignant ventricular arrhythmia database has been utilized for evaluating the algorithm.The performance of the algorithm is compared with some existing algorithms in terms of signal duration time (episode length), sensitivity, specificity and positive selectivity.The analysis shows that the proposed algorithm gives satisfactory results.
No takes yet. Share an insight, caveat, or question.
Ilankumaran et al. (2011) studied Ventricular Arrhythmias. Wavelet decomposition with adaptive threshold algorithm vs. Existing algorithms (e.g., complexity measure, Pan et al.) was evaluated on Sensitivity, specificity, and positive selectivity for arrhythmia detection. The proposed wavelet decomposition algorithm with adaptive threshold achieved 100% sensitivity for ventricular fibrillation and 97% sensitivity for ventricular tachycardia.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: