Empirical mode decomposition applied to laser Doppler flowmetry signals : diagnosis approach.

ABSTRACT

Empirical Mode Decomposition (EMD) is a recently introduced tool for decomposing signals into so-called intrinsic mode functions (IMF). These IMF represent the data by means of oscillating waves with local zero mean. In some sense the decomposition can be compared with a time-varying filter bank, i.e., signals are decomposed using band limited filters with band widths that vary in time. The main attribute of EMD compared to other time-frequency tools is that it does not use any predetermined filters or transforms. It is therefore a self-contained method that preserves the physical properties in the separate IMF, explaining why it has been successfully applied in many engineering fields. This method is applied here on laser Doppler flowmetry signals and particularly on the hyperemia signals. Two interested hyperemia parameters are the maximum perfusion value and the corresponding time instant of appearance. Accurate values parameters are determined from the fifth IMF component. Computing these parameters allows us to improve diagnosis of some pathologies as peripheral arterial occlusive diseases.