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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 618743, 6 pages
http://dx.doi.org/10.1155/2013/618743
Research Article

Analyzing EEG of Quasi-Brain-Death Based on Dynamic Sample Entropy Measures

1East China University of Science and Technology, Meilong Road 130, Shanghai 200237, China
2Saitama Institute of Technology, 1690 Fusaiji, Fukaya-shi, Saitama 369-0293, Japan
3Brain Science Institute, RIKEN, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan

Received 22 August 2013; Revised 27 November 2013; Accepted 27 November 2013

Academic Editor: Jinde Cao

Copyright © 2013 Li Ni et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

To give a more definite criterion using electroencephalograph (EEG) approach on brain death determination is vital for both reducing the risks and preventing medical misdiagnosis. This paper presents several novel adaptive computable entropy methods based on approximate entropy (ApEn) and sample entropy (SampEn) to monitor the varying symptoms of patients and to determine the brain death. The proposed method is a dynamic extension of the standard ApEn and SampEn by introducing a shifted time window. The main advantages of the developed dynamic approximate entropy (DApEn) and dynamic sample entropy (DSampEn) are for real-time computation and practical use. Results from the analysis of 35 patients (63 recordings) show that the proposed methods can illustrate effectiveness and well performance in evaluating the brain consciousness states.