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Coupling analysis of electrocardiogram and electroencephalogram based on improved symbolic transfer entropy

Wu Sha Li Jin Zhang Ming-Li Wang Jun

Coupling analysis of electrocardiogram and electroencephalogram based on improved symbolic transfer entropy

Wu Sha, Li Jin, Zhang Ming-Li, Wang Jun
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  • PDF Downloads:  588
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  • Received Date:  20 June 2013
  • Accepted Date:  19 August 2013
  • Published Online:  05 December 2013

Coupling analysis of electrocardiogram and electroencephalogram based on improved symbolic transfer entropy

  • 1. Image Processing and Image Communications Key Lab., Nanjing Univ. of Posts & Telecomm., Nanjing 210003, China;
  • 2. College of Physics and Information Technology, Shaanxi Normal University, Xi’an 710062, China;
  • 3. Department of Gastroenterology, The No. 1 Hospital of Xi’an, Fen Rd 30, the South Street, Xi’an 710002, China
Fund Project:  Project supported by the National Natural Science Foundation of China (Grant Nos. 61271082, 61201029, 61102094), and the Natural Science Foundation of Jiangsu Province (Grant Nos. BK2011759, BK2011565).

Abstract: Exploration of the coupling relationship in dynamical system has always been a hot topic of many scholars at home and abroad, the traditional symbolic dynamics analysis method may lead to the results from the serious effect of non-stationary time series. This paper employs coarse graining extraction based on research of original transfer entropy. Through theoretical and experimental analysis, we find that the results of transfer entropy have different distribution trend under different extraction conditions in the coupling analysis of electroencephalogram and electrocardiogram. We choose the best effect of signal data extraction method and apply it to the later application analysis. Furthermore, this paper proposes improvement on the method of time series symbolization, using dynamic adaptive segmentation method. The experimental results show that the whether waking period or sleeping stage, coupling between electroencephalogram and electrocardiogram is more significant when using improved symbolic transfer entropy algorithm. It is also better to capture the dynamic information of the signal and the change of complexity of system dynamics, which is more conductive to clinical testing in practical application and has a better effect on the analysis of non-stationary time series.

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