%0 Journal Article
%T Turning Tangent Empirical Mode Decomposition: A Framework for Mono- and Multivariate Signals.
%+ Laboratoire Traitement du Signal et de l'Image (LTSI)
%A Fleureau, Julien
%A Nunes, Jean-Claude
%A Kachenoura, Amar
%A Albera, Laurent
%A Senhadji, Lotfi
%< avec comitÃ© de lecture
%@ 1053-587X
%J IEEE Transactions on Signal Processing
%I Institute of Electrical and Electronics Engineers
%V 59
%N 3
%P 1309-1316
%8 2011-03
%D 2011
%R 10.1109/TSP.2010.2097254
%M 22003273
%K Analysis of non-linear and non-stationary signals
%K Computational complexity
%K EEG denoising
%K Extrema and barycenters of oscillation
%K Filter bank structure
%K Frequency modulation
%K Hurst exponent estimation
%K Interpolation
%K Intrinsic Mode functions
%K Mono- and Multivariate Empirical Mode Decomposition
%K Oscillators
%K Robustness
%K Spline
%K Time varying representation
%Z Life Sciences [q-bio]/Bioengineering
%Z Engineering Sciences [physics]/Signal and Image processing
%Z Computer Science [cs]/Signal and Image ProcessingJournal articles
%X 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.
%G English
%2 https://inserm.hal.science/inserm-00550936/document
%2 https://inserm.hal.science/inserm-00550936/file/Turning_Tangent_Empirical_Mode_Decomposition_a_framework_for_mono_and_multivariate_signals.pdf
%L inserm-00550936
%U https://inserm.hal.science/inserm-00550936
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