Separating Doppler ultrasound blood flow and vessel wall signals by improved empirical mode decomposition methods

Doppler ultrasound blood flow signals usually contain vessel wall components.Their accurate separation is of great significance in the blood flow detection.In this paper,two improved Empirical Mode Decomposition (EMD) methods are proposed.Firstly,Doppler ultrasound signals with vessel wall components are decomposed into multi-level Intrinsic Mode Functions (IMFs).Then those IMFs containing both blood flow and vessel wall signals are separated according to their different properties.Finally,wall components of each level IMF are added as the estimation of the vessel wall signal and the blood flow signal is obtained by subtracting the estimated vessel wall signal from the original signal.Experiments on both computer simulated and real human carotid Doppler ultrasound signals are carried out to compare these two methods with the high pass filter,the spatially selective noise filtration algorithm and the original EMD method.It is shown that methods proposed in this paper can separate blood flow and vessel wall signals more accurately within a large range of wall velocities and their mean absolute error are about 52%and 57%lower than that of the high-pass filtering.So methods proposed in this paper may be effectively used to separate Doppler ultrasound blood flow and vessel wall signals.