Non-linear analysis of EEG signals at various sleep stages

By R. Acharya U., Oliver Faust, N. Kannathal, TjiLeng Chua, Swamy Laxminarayan. Comput. Methods Programs Biomed., 2005

425 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/74392208df4fec9fb37a60241fdb973f23d840de

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. J. Nathan Kutz
2. Bingni W. Brunton
3. Joshua L. Proctor
4. Steven L. Brunton
5. Eurika Kaiser

In Molecular Biology

1. J. Nathan Kutz
2. Steven L. Brunton
3. Eurika Kaiser
4. Joshua L. Proctor
5. Bingni W. Brunton
6. Maizar Raissi
7. G. Em Karniadakis
8. Jean-Christophe Loiseau

In Statistical and Nonlinear Physics

1. Steven L. Brunton
2. J. Nathan Kutz
3. Eurika Kaiser
4. Bingni W. Brunton
5. Joshua L. Proctor
6. Maizar Raissi
7. Igor Mezi'c
8. G. Em Karniadakis

Citations in the corpus

• Epileptic EEG classification based on extreme learning machine and nonlinear features. (2011, methodology): “…of physiological time series data Hurst exponent is a measure of the smoothness of a fractal time series based on the asymptotic behavior of the rescaled range of the process (Acharya et al., 2005; Wang et al., 2006), and it can be determined by R / S algorithm which is described in Appendix B”
• Chaos as an intermittently forced linear system (2016, background): “• Chaos is important: Dynamics [10], Lorenz [14] • Takens embedding is profound and important [23] • Connection to Koopman analysis [12] • Connection to identifying dynamics and DMD [4, 13] • Koopman invariant subspace [?] • Time series classics: review [1], book [25] • ERA [11], SSA [3], NLSA [9]”