文献类型：期刊文献

中文题名：Approach of Complex Networks for the Determination of Brain Death

英文题名：Approach of Complex Networks for the Determination of Brain Death

作者：Sun Wei-Gang[1,2];Cao Jian-Ting[1,3];Wang Ru-Bin[1]

机构：[1]E China Univ Sci & Technol, Sch Informat Sci & Engn, Sch Sci, Inst Cognit Neurodynam, Shanghai 200237, Peoples R China;[2]Hangzhou Dianzi Univ, Sch Sci, Hangzhou 310018, Peoples R China;[3]Saitama Inst Technol, Dept Robot, Saitama 3690293, Japan

年份：2011

卷号：28

期号：6

中文期刊名：Chinese Physics Letters

外文期刊名：CHINESE PHYSICS LETTERS

收录：CSTPCD;;EI(收录号：20220711662408);Scopus;WOS:【SCI-EXPANDED(收录号:WOS:000291243400090)】；CSCD:【CSCD2011_2012】；

基金：Supported by the National Natural Science Foundation of China under Grant Nos 10672057 and 10872068, the Fundamental Research Funds for the Central Universities and Japan Society for the Promotion of Science (22560425).

语种：英文

中文关键词：quantities;irreversible;insight;

外文关键词：Brain - Electrophysiology - Electroencephalography

摘要：In clinical practice,brain death is the irreversible end of all brain activity.Compared to current statistical methods for the determination of brain death,we focus on the approach of complex networks for real-world electroencephalography in its determination.Brain functional networks constructed by correlation analysis are derived,and statistical network quantities used for distinguishing the patients in coma or brain death state,such as average strength,clustering coefficient and average path length,are calculated.Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state.Our findings might provide valuable insights on the determination of brain death.
In clinical practice, brain death is the irreversible end of all brain activity. Compared to current statistical methods for the determination of brain death, we focus on the approach of complex networks for real-world electroencephalography in its determination. Brain functional networks constructed by correlation analysis are derived, and statistical network quantities used for distinguishing the patients in coma or brain death state, such as average strength, clustering coefficient and average path length, are calculated. Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state. Our findings might provide valuable insights on the determination of brain death.