Automatic detection of mode mixing in empirical mode decomposition using non-stationarity detection: application to selecting IMFs of interest and denoising

Abstract

Empirical mode decomposition splits a signal into several intrinsic mode functions (IMF). An algorithm for the automatic selection of the modes containing the signal of interest was recently proposed. This algorithm is based on statistical analysis describing the noise repartition between IMFs. This algorithm uses an estimate of the signal noise content from the energy of the first IMF, which is supposed to contain a specific part of the total noise and to contain noise only. Mode mixing can give rise to an over-estimation of the noise in the signal. This can lead to more IMFs to be considered as containing only noise and to be erroneously discarded before reconstruction. We propose to use mode mixing detection based on a stationarity test applied to the first IMF. In case of mode mixing, we propose to correct the noise estimation by extracting from the first IMF the part corresponding to the signal of interest. The results obtained with synthetic signals as well as with real mechanical and biomedical signals demonstrate a good performance of the approach proposed here. The first two modes do lose some of their IMF properties in the process. We offer some comments on how these properties can be recovered if needed.