Detecting Ventricular Fibrillation by Fast Algorithm of Dynamic Sample Entropy

摘要：

A key component in automatic external defibrillator (AED) is to discriminate ventricular fibrillation (VF) from non VF by means of appropriate detection algorithms, which should have a high detection quality, be easily implementable, and work in real time. In this study, we presented a novel and computationally simple algorithm for VF detection using sample entropy (SampEn) method. To evaluate the accuracy of the proposed algorithm, we analyzed the complete data sets from Creighton University Ventricular Tachyarrhythmia database (CUDB). We compared the sensitivity, specificity, positive prediction, accuracy and the area under its receiver operating characteristic curve (ROC) of the new algorithm with several earlier VF detection algorithms. The new algorithm proves to be well suited for short data sets analysis, which can reach an elegant balance between detection time and accuracy.

作者: Haiyan Li Wenguang Han Chao Hu Max Q.-H. Meng

作者单位: CAS/CUHK Shenzhen Institute of Advanced Integration,Key Lab for Biomedical Informatics & Health Eng. CAS/CUHK Shenzhen Institute of Advanced Integration,Key Lab for Biomedical Information & Health Eng. Department of Electronic Engineering,The Chinese University of HongKong.Shatin,N.T.HongKong

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Robotics and Biomimetics(2009 IEEE 机器人与仿生技术国际会议 ROBIO 2009)

会议地点: 桂林

会议语种:英文

页码: 1105-1110

在线出版日期: 2009-12-19（万方平台首次上网日期，不代表论文的发表时间）