Analysis of respiratory mechanomyographic signals by means of the empirical mode decomposition

The study of the mechanomyographic (MMG) signals of respiratory muscles is a promising technique in order to evaluate the respiratory muscles effort. A critical point in MMG studies is the selection of the cut-off frequency in order to separate the low frequency (LF) component (basically due to gross movement of the muscle or of the body) and the high frequency (HF) component (related with the vibration of the muscle fibres during contraction). In this study, we propose to use the Empirical Mode Decomposition method in order to analyze the Intrinsic Mode Functions of MMG signals of the diaphragm muscle, acquired by means of a capacitive accelerometer applied on the costal wall. The method was tested on an animal model, with two incremental respiratory protocols performed by two non anesthetized mongrel dogs. The proposed EMD based method seems to be a useful tool to eliminate the low frequency component of MMG signals. The obtained correlation coefficients between respiratory and MMG parameters were higher than the ones obtained with a Wavelet multiresolution decomposition method utilized in a previous work