Key result
Improved symbolic transfer entropy algorithm better captures EEG-ECG coupling dynamics during sleep and wake stages.
An improved symbolic transfer entropy algorithm provides better analysis of the dynamic coupling between ECG and EEG signals, which may be useful for clinical testing of non-stationary time series.
May refine EEG-ECG coupling analysis in sleep; leaves open clinical validation in patient cohorts.
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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Sha et al. (2013) studied this question. Improved symbolic transfer entropy algorithm vs. Traditional symbolic dynamics analysis method was evaluated on Coupling significance between electroencephalogram and electrocardiogram. An improved symbolic transfer entropy algorithm using dynamic adaptive segmentation better captures the dynamic information and complexity of EEG and ECG coupling during waking and sleeping stages.
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