Key result
A random forest classifier using 17 ECG features achieved an accuracy of 97.17%, sensitivity of 95.17%, and specificity of 97.32% for detecting ventricular fibrillation using an 8-second overlapping window.
Why the study?
Does a random forest classifier using ECG features accurately detect ventricular fibrillation?
Does a random forest classifier using ECG features accurately detect ventricular fibrillation?
A random forest classifier using 17 ECG features achieved high accuracy (97.17%) in detecting ventricular fibrillation, outperforming recent methods with lower complexity.
Supports ML-based VF detection development; leaves open prospective clinical validation before any practice integration.
Early warning and detection of ventricular fibrillation is crucial to the successful treatment of this life-threatening condition. In this paper, a ventricular fibrillation classification algorithm using a machine learning method, random forest, is proposed. A total of 17 previously defined ECG feature metrics were extracted from fixed length segments of the echocardiogram (ECG). Three annotated public domain ECG databases (Creighton University Ventricular Tachycardia database, MIT-BIH Arrhythmia Database and MIT-BIH Malignant Ventricular Arrhythmia Database) were used for evaluation of the proposed method. Window sizes 3 s, 5 s and 8 s for overlapping and non-overlapping segmentation methodologies were tested. An accuracy (Acc) of 97.17%, sensitivity (Se) of 95.17% and specificity (Sp) of 97.32% were obtained with 8 s window size for overlapping segments. The results were benchmarked against recent reported results and were found to outper-form them with lower complexity.
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Verma et al. (2016) studied Ventricular Fibrillation (n=95). Random Forest Classifier vs. Previous algorithms was evaluated on Ventricular fibrillation detection accuracy. A random forest classifier using 17 ECG features achieved an accuracy of 97.17%, sensitivity of 95.17%, and specificity of 97.32% for detecting ventricular fibrillation using an 8-second overlapping window.
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