The SE and multiscale sample entropy (MSE) are both methods of complexity measuring. Recent years have witnessed advances in the application of different kinds of entropy, including transfer entropy , causation entropy , permutation entropy , fuzzy entropy , approximate entropy and sample entropy (SE) .Īn overview of entropy shows that it quantifies the complexity of a system by calculating its uncertainty. Its application has been seen in a wide range of fields and industries, such as economy ,, medicine ,, , environment , and transportation . As a measure of the system’s internal chaos, entropy has gradually become an effective method in the research of dynamics and nonlinear signal processing. Experimental results demonstrate that the two proposed frameworks are effective in monitoring human health and in assessing physical status through EEG signals.Ī growing body of literature has been undertaken on nonlinear time series ,, ,, , and prior studies have been concerned about correlation, causality and time series prediction. Furthermore, this paper introduces the surrogate data test and the proposed methods have the ability to detect the nonlinearity and synchronization in simulations and in real-world experiments. It is possible to extract the characteristics of sleep EEG signals and divide the subjects’ sleep stages using this method. It shows robustness in artificial data and decreases the appearance of undefined entropy to a certain extent. In the meantime, we propose an improved horizontal visibility graph-based multiscale cross-sample entropy method to measure the synchronization between two time series. Using a novel data processing algorithm that combines frequency bands with decomposition, feature vectors are constructed for Electroencephalography (EEG) signals through the proposed entropy calculation algorithm, and different classes of subjects are categorized based on K-Nearest Neighbors, Support Vector Machine, and Artificial Neural Network. We apply the modified multiscale sample entropy to the diagnosis of epilepsy. The modified multiscale sample entropy has been proven to be robust on two artificial time series, and is capable of reducing the undefined entropy generated as a result of the increase in scale. This paper presents an innovative multiscale sample entropy for measuring the complexity of time series based on the horizontal visibility graph. As a crucial method of the feature extraction, the complexity measurement has a wide range of applications in the field of nonlinear time series research.
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