%0 Journal Article
%T Detection of Ventricular Fibrillation Using Random Forest Classifier
%A Anurag Verma
%A Xiaodai Dong
%J Journal of Biomedical Science and Engineering
%P 259-268
%@ 1937-688X
%D 2016
%I Scientific Research Publishing
%R 10.4236/jbise.2016.95019
%X 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.
%K Machine Learning
%K Random Forests (RF)
%K Ventricular Fibrillation (VF) Detection
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=65626