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Journal articles
Turning Tangent Empirical Mode Decomposition: A Framework for Mono- and Multivariate Signals.
Abstract : A novel Empirical Mode Decomposition (EMD) algorithm, called 2T-EMD, for both mono- and multivariate signals is proposed in this paper. It differs from the other approaches by its computational lightness and its algorithmic simplicity. The method is essentially based on a redefinition of the signal mean envelope, computed thanks to new characteristic points, which offers the possibility to decompose multivariate signals without any projection. The scope of application of the novel algorithm is specified, and a comparison of the 2T-EMD technique with classical methods is performed on various simulated mono- and multivariate signals. The monovariate behaviour of the proposed method on noisy signals is then validated by decomposing a fractional Gaussian noise and an application to real life EEG data is finally presented.
Keywords :
Analysis of non-linear and non-stationary signals
Computational complexity
EEG denoising
Extrema and barycenters of oscillation
Filter bank structure
Frequency modulation
Hurst exponent estimation
Interpolation
Intrinsic Mode functions
Mono- and Multivariate Empirical Mode Decomposition
Oscillators
Robustness
Spline
Time varying representation
Cited literature [22 references]
https://www.hal.inserm.fr/inserm-00550936
Contributor : Lotfi Senhadji <>
Submitted on : Friday, December 31, 2010 - 6:14:52 PM
Last modification on : Tuesday, July 3, 2018 - 10:58:04 AM
Long-term archiving on: : Friday, April 1, 2011 - 2:51:34 AM
Contributor : Lotfi Senhadji <>
Submitted on : Friday, December 31, 2010 - 6:14:52 PM
Last modification on : Tuesday, July 3, 2018 - 10:58:04 AM
Long-term archiving on: : Friday, April 1, 2011 - 2:51:34 AM
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