Heart Sound Cancellation Based on Multiscale Products and Linear Prediction

This paper presents a novel method for Heart Sound (HS) cancellation from Lung Sound (LS) records. The method uses the multiscale product of the wavelet coefficients of the original signal to detect HS segments. Once the HS segments are identified, the method removes them from the wavelet coefficients at every level and estimate the created gaps by either an autoregressive or moving average model. It is shown that if the segment to be predicted is stationary, a final record with no audible artifacts such as clicks can be reconstructed using this approach. The results were promising for HS removal from LS without hampering the main components of the LS. The results were confirmed both qualitatively by listening to the reconstructed signal and quantitatively by spectral analysis.