Objective Sleep stage classification is important to the evaluation of sleep quality, the diagnosis and treatment of sleep disorders. Transfer entropy is a parameter to measure the relevance of two time sequences symbolic transfer entropy(STE), which is based on the technique of symbolization, may be the first application in the study of sleep stage classification. This method overcomes the drawbacks of requirement for the coordination between parameters and the sensitivity to noise contributions. Methods The EEG and ECG signals about the wake stage and the first stage of non-rapid eye movement sleep are extracted from the same people at the same time. After symbolic and space reconstruction, we compute the STE and test by t test with multi-samples. Results The STE of wake stage is larger than that of the first stage of non-rapid eye movement sleep and the difference between the two sleep stages is significant in t test. Brain cells and heart cells continue to be coupling, the STE become smaller as the sleep stage is deepening. Therefore, the experiment results are consistent with the theory. Conclusions The STE about the wake stage and the first stage of non-rapid eye movement sleep reflects the changes of sleep stages and can be applied in sleep stage classification. STE may be a promising tool for the analysis of automatic sleep classification.
|