Key result
A decision tree (C4.5) classifier using DWT-based time-frequency features achieved an average accuracy of 99.23% for detecting ventricular arrhythmias, outperforming an SVM classifier (92.43%).
Why the study?
Ventricular tachycardia and ventricular fibrillation are life-threatening arrhythmias requiring emergency treatment, and early automatic detection in ECG signals is crucial for successful defibrillation.
Absolute Event Rate: 99.23% vs 92.43%
A DWT-based feature extraction combined with a decision tree classifier provides highly accurate automated detection of ventricular tachycardia and fibrillation from ECG signals.
Supports automated ECG detection of ventricular arrhythmias; hypothesis-generating and requires prospective clinical validation.
Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the life-threatening ventricular arrhythmias that require treatment in an emergency. Detection of VT and VF at an early stage is crucial for achieving the success of the defibrillation treatment. Hence an automatic system using computer-aided diagnosis tool is helpful in detecting the ventricular arrhythmias in electrocardiogram (ECG) signal. In this paper, a discrete wavelet transform (DWT) was used to denoise and decompose the ECG signals into different consecutive frequency bands to reduce noise. The methodology was tested using ECG data from standard CU ventricular tachyarrhythmia database (CUDB) and MIT-BIH malignant ventricular ectopy database (VFDB) datasets of PhysioNet databases. A set of time-frequency features consists of temporal, spectral, and statistical were extracted and ranked by the correlation attribute evaluation with ranker search method in order to improve the accuracy of detection. The ranked features were classified for VT and VF conditions using support vector machine (SVM) and decision tree (C4.5) classifier. The proposed DWT based features yielded the average sensitivity of 98%, specificity of 99.32%, and accuracy of 99.23% using a decision tree (C4.5) classifier. These results were better than the SVM classifier having an average accuracy of 92.43%. The obtained results prove that using DWT based time-frequency features with decision tree (C4.5) classifier can be one of the best choices for clinicians for precise detection of ventricular arrhythmias.
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Mohanty et al. (2018) studied Ventricular tachycardia and ventricular fibrillation. DWT based time-frequency features with decision tree (C4.5) classifier vs. Support vector machine (SVM) classifier was evaluated on Accuracy of detecting VT and VF. A decision tree (C4.5) classifier using DWT-based time-frequency features achieved an average accuracy of 99.23% for detecting ventricular arrhythmias, outperforming an SVM classifier (92.43%).
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