Detection and elimination of Heart sound form Lung sound based on wavelet multi resolution analysis technique and linear prediction

nishi shahnaj haider, sibu thomas


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.


The aim of this proposed work is achieved by  implementing  a VI for Heart Sound (HS) removal from Lung Sound (LS) records using the Advanced Signal Processing Toolkit of LabVIEW 8.6. The method uses the multi- resolution analysis of the wavelet approximation coefficients of the   original signal to detect HS-included segments.Once the HS segments are identified, the method removes them from the wavelet coefficients and estimates the created gaps by using TSA ARMA modeling and prediction.


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