Li, H. (2009) Detecting Ventricular Fibrillation by Fast Algorithm of Dynamic Sample Entropy. Proceedings of the IEEE International Conference on Robotics and Biomimetics, Guilin, 19-23 December 2009, 1105-1110.
http://dx.doi.org/10.1109/robio.2009.5420764

has been cited by the following article:

• TITLE: Detection of Ventricular Fibrillation Using Random Forest Classifier

  AUTHORS: Anurag Verma, Xiaodai Dong

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

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.9 No.5, April 19, 2016

  ABSTRACT: 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.