Key result
A novel algorithm using modified variational mode decomposition and support vector machine achieved 99.00% accuracy, 97.36% sensitivity, and 99.16% specificity for shockable rhythm diagnosis.
A novel algorithm using modified variational mode decomposition and support vector machine achieved high accuracy, sensitivity, and specificity for detecting shockable rhythms in automated external defibrillators.
May aid automated defibrillator decisions; leaves open prospective clinical validation before practice change.
Sudden cardiac arrests are mainly caused by ventricular fibrillation and ventricular tachycardia, which are known as shockable (SH) rhythms and properly curable by electronic defibrillators. In this paper, we propose a novel algorithm to decide whether an electrocardiogram (ECG) signal is SH or nonshockable (NSH). The algorithm selects 20 features from both the preprocessed ECG and its NSH signal using a modified variational mode decomposition technique, and uses the support vector machine for the binary SH/NSH classification. The 20 features are identified as an efficient set of the most informative, among 54 candidate features, by comparing the balanced error rate of each combination, based on two layers of feature selection. This feature set is validated with the evaluation data in the public database using a fivefolds cross validation procedure. The proposed algorithm results in accuracy of 99.00%, sensitivity of 97.36%, and specificity of 99.16%.
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Nguyen et al. (2017) studied Sudden cardiac arrest. Modified variational mode decomposition technique and support vector machine algorithm was evaluated on Binary shockable/nonshockable rhythm classification accuracy. A novel algorithm using modified variational mode decomposition and support vector machine achieved 99.00% accuracy, 97.36% sensitivity, and 99.16% specificity for shockable rhythm diagnosis.
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