Nonlinear Mode Decomposition: a new noise-robust, adaptive decomposition method
For researchers in signal processing and applied fields (geophysics, finance, life sciences), NMD provides a more noise-robust and adaptive decomposition tool.
The paper introduces Nonlinear Mode Decomposition (NMD), a new adaptive method that decomposes signals into physically meaningful oscillations while removing noise, demonstrating qualitative and quantitative superiority over existing methods like EEMD and ICA on simulated and real signals.
We introduce a new adaptive decomposition tool, which we refer to as Nonlinear Mode Decomposition (NMD). It decomposes a given signal into a set of physically meaningful oscillations for any waveform, simultaneously removing the noise. NMD is based on the powerful combination of time-frequency analysis techniques - which together with the adaptive choice of their parameters make it extremely noise-robust - and surrogate data tests, used to identify interdependent oscillations and to distinguish deterministic from random activity. We illustrate the application of NMD to both simulated and real signals, and demonstrate its qualitative and quantitative superiority over the other existing approaches, such as (ensemble) empirical mode decomposition, Karhunen-Loeve expansion and independent component analysis. We point out that NMD is likely to be applicable and useful in many different areas of research, such as geophysics, finance, and the life sciences. The necessary MATLAB codes for running NMD are freely available at http://www.physics.lancs.ac.uk/research/nbmphysics/diats/nmd/.