基于经验模态分解和贝叶斯决策理论的心室颤动检测

Detection of ventricular fibrillation using empirical mode decomposition and Bayes decision theory.

作者信息

Abdullah Arafat Muhammad, Sieed Jubair, Kamrul Hasan Md

机构信息

Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.

出版信息

Comput Biol Med. 2009 Nov;39(11):1051-7. doi: 10.1016/j.compbiomed.2009.08.007. Epub 2009 Sep 16.

DOI:10.1016/j.compbiomed.2009.08.007

PMID:19762011

Abstract

Ventricular fibrillation (VF) is the most serious variety of arrhythmia which requires quick and accurate detection to save lives. In this paper, we propose an empirical mode decomposition (EMD) based algorithm for VF detection. The intrinsic mode functions (IMFs) of VF are orthogonal whereas the lower order IMFs of normal sinus rhythm (NSR) are not. The orthogonality indices derived from the first three consecutive intrinsic mode functions (IMFs) of NSR and VF are used for their discrimination. The proposed technique is applied to the MIT-BIH arrhythmia database. The accuracy of detection of VF is 99.70% for a window length of 3s. This early estimate of VF may be useful in emergency cases where defibrillators are to be applied. Comparative results with the existing methods in terms of quality parameters and integrated receiver operating characteristic (IROC) are presented.

摘要

心室颤动（VF）是最严重的心律失常类型，需要快速准确的检测以挽救生命。在本文中，我们提出了一种基于经验模态分解（EMD）的心室颤动检测算法。心室颤动的本征模函数（IMF）是正交的，而正常窦性心律（NSR）的低阶本征模函数则不是。从正常窦性心律和心室颤动的前三个连续本征模函数（IMF）导出的正交性指标用于它们的判别。所提出的技术应用于麻省理工学院 - 贝斯以色列女执事医疗中心（MIT - BIH）心律失常数据库。对于3秒的窗口长度，心室颤动的检测准确率为99.70%。这种对心室颤动的早期估计在需要应用除颤器的紧急情况下可能会有用。给出了与现有方法在质量参数和综合接收器操作特性（IROC）方面的比较结果。