Design of a Network Permutation Entropy and Its Applications for Chaotic Time Series and EEG Signals
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 net...
Main Authors: | Bo Yan, Shaobo He, Kehui Sun |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-08-01
|
Series: | Entropy |
Subjects: | |
Online Access: | https://www.mdpi.com/1099-4300/21/9/849 |
Similar Items
-
A Modified Multivariable Complexity Measure Algorithm and Its Application for Identifying Mental Arithmetic Task
by: Dizhen Ma, et al.
Published: (2021-07-01) -
Complexity Analysis of Physiological Time Series Using a Novel Permutation-Ratio Entropy
by: Yatao Zhang, et al.
Published: (2018-01-01) -
Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures
by: Jing Li, et al.
Published: (2014-05-01) -
Permutation entropy is not an age-independent parameter for EEG-based anesthesia monitoring
by: Darren Hight, et al.
Published: (2023-06-01) -
Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer’s Disease EEG
by: Isabella Palamara, et al.
Published: (2012-07-01)