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• W2970733329 startingPage "849" @default.
• W2970733329 abstract "Measuring the complexity of time series provides an important indicator for characteristic analysis of nonlinear systems. The permutation entropy (PE) is widely used, but it still needs to be modified. In this paper, the PE algorithm is improved by introducing the concept of the network, and the network PE (NPE) is proposed. The connections are established based on both the patterns and weights of the reconstructed vectors. The complexity of different chaotic systems is analyzed. As with the PE algorithm, the NPE algorithm-based analysis results are also reliable for chaotic systems. Finally, the NPE is applied to estimate the complexity of EEG signals of normal healthy persons and epileptic patients. It is shown that the normal healthy persons have the largest NPE values, while the EEG signals of epileptic patients are lower during both seizure-free intervals and seizure activity. Hence, NPE could be used as an alternative to PE for the nonlinear characteristics of chaotic systems and EEG signal-based physiological and biomedical analysis." @default.
• W2970733329 created "2019-09-05" @default.
• W2970733329 creator A5018654135 @default.
• W2970733329 creator A5038554077 @default.
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• W2970733329 date "2019-08-30" @default.
• W2970733329 modified "2023-10-16" @default.
• W2970733329 title "Design of a Network Permutation Entropy and Its Applications for Chaotic Time Series and EEG Signals" @default.
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• W2970733329 doi "https://doi.org/10.3390/e21090849" @default.
• W2970733329 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7515378" @default.
• W2970733329 hasPublicationYear "2019" @default.
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