Author(s)

Anurag Verma¹, Xiaodai Dong²

¹Department of Electronics & Electrical Communication Engineering, Indian Institute of Technology-Kharagpur, Kharagpur, India.
²Department of Electrical and Computer Engineering, University of Victoria, Victoria, Canada.

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.

Machine Learning, Random Forests (RF), Ventricular Fibrillation (VF) Detection

Verma, A. and Dong, X. (2016) Detection of Ventricular Fibrillation Using Random Forest Classifier. Journal of Biomedical Science and Engineering, 9, 259-268. doi: 10.4236/jbise.2016.95019.