Separating lung sound (LS) in heart sound (HS)-interfered recordings has been of interest to doctors and researchers in the last two decades. Many
algorithms have been developed to solve this question, one of them
is based on the empirical mode decomposition (EMD). Due to the
notorious mode mixing issue in the standard EMD, in this study we survey
LS extraction based on EMD extensions, including ensemble EMD
(EEMD), multivariate EMD (M-EMD), and noise assisted M-EMD
(NAM-EMD). The algorithm for LS extraction is composed of heart sound (HS) segmentation, LS separation, and components reconstruction. The performance evaluation by auditory, visual, and numerical analyses reveals that NAM-EMD with brown noise as noise assisted data is the most suitable method for LS separation algorithm among the standard EMD and other EMD extensions.

Separating lung sound (LS) in heart sound (HS)-interfered recordings has been of interest to doctors and researchers in the last two decades. Many
algorithms have been developed to solve this question, one of them
is based on the empirical mode decomposition (EMD). Due to the
notorious mode mixing issue in the standard EMD, in this study we survey
LS extraction based on EMD extensions, including ensemble EMD
(EEMD), multivariate EMD (M-EMD), and noise assisted M-EMD
(NAM-EMD). The algorithm for LS extraction is composed of heart sound (HS) segmentation, LS separation, and components reconstruction. The performance evaluation by auditory, visual, and numerical analyses reveals that NAM-EMD with brown noise as noise assisted data is the most suitable method for LS separation algorithm among the standard EMD and other EMD extensions.

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