Open AccessCoupling analysis of electrocardiogram and electroencephalogram based on improved symbolic transfer entropy
Author(s) -
Wu Sha,
Jin Li,
Mingli Zhang,
Jun Wang
Publication year - 2013
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.62.238701
Subject(s) - computer science , transfer entropy , entropy (arrow of time) , segmentation , symbolic data analysis , coupling (piping) , electroencephalography , pattern recognition (psychology) , transfer function , symbolic dynamics , time series , artificial intelligence , algorithm , principle of maximum entropy , theoretical computer science , machine learning , mathematics , physics , thermodynamics , electrical engineering , engineering , mechanical engineering , psychology , psychiatry , pure mathematics
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.