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Symbolic transfer entropy-based premature signal analysis 被引量:2

Symbolic transfer entropy-based premature signal analysis
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摘要 In this paper, we use symbolic transfer entropy to study the coupling strength between premature signals. Numerical experiments show that three types of signal couplings are in the same direction. Among them, normal signal coupling is the strongest, followed by that of premature ventricular contractions, and that of atrial premature beats is the weakest. The T test shows that the entropies of the three signals are distinct. Symbolic transfer entropy requires less data, can distinguish the three types of signals and has very good computational efficiency. In this paper, we use symbolic transfer entropy to study the coupling strength between premature signals. Numerical experiments show that three types of signal couplings are in the same direction. Among them, normal signal coupling is the strongest, followed by that of premature ventricular contractions, and that of atrial premature beats is the weakest. The T test shows that the entropies of the three signals are distinct. Symbolic transfer entropy requires less data, can distinguish the three types of signals and has very good computational efficiency.
作者 Wang Jun Yu Zheng-Feng 王俊;余正锋(Image Processing and Image Communications Key Laboratory,School of Geography and Biological Information,Nanjing University of Posts&Telecommunications,Nanjing 210003,China)
出处 《Chinese Physics B》 SCIE EI CAS CSCD 2012年第1期535-538,共4页 中国物理B(英文版)
基金 Project supported by the Jiangsu Province Science Foundation,China(Grant No.BK2011759)
关键词 premature signal symbolic transfer entropy signal coupling premature signal, symbolic transfer entropy, signal coupling
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参考文献11

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同被引文献14

  • 1Bian Chunhua,Qin Chang,Ma Qianli D. Y,Shen Qinghong.Modified permutation-entropy analysis of heartbeat dynamics[].Physical Review E Statistical Nonlinear and Soft Matter Physics.2012
  • 2Wen-Liang Hung,Miin-Shen Yang.On the J-divergence of intuitionistic fuzzy sets with its application to pattern recognition[].Journal of Information Science.2008
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  • 8吴东宇,刘霖,宋玖骏,袁英,李广庆,蔡刿,宋为群,王茂斌.脑电非线性分析评价卒中患者的意识障碍[J].中国脑血管病杂志,2008,5(9):385-389. 被引量:8
  • 9沈韡,王俊.基于符号相对熵的心电信号时间不可逆性分析[J].物理学报,2011,60(11):744-747. 被引量:2
  • 10张梅,崔超,马千里,干宗良,王俊.基于符号化部分互信息熵的多参数生物电信号的耦合分析[J].物理学报,2013,62(6):491-495. 被引量:4

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