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Auscultation provides a rich diversity of information to diagnosecardiovascular and respiratory diseases. However, sound auscultation ischallenging due to noise. In this study, a modified version of the affinenon-negative matrix factorization (NMF) approach is proposed to blindlyseparate lung and heart sounds recorded by a digital stethoscope. This methodapplies a novel NMF algorithm, which embodies a parallel structure ofmultilayer units on the input signal, to find a proper estimation of sourcesignals. Another key innovation is the use of the periodic property of thesignals which improves accuracy compared to previous works. The method istested on 100 cases. Each case consists of two synthesized mixtures of realmeasurements. The effect of different parameters is discussed, and the resultsare compared to other current methods. Results demonstrate improvements in thesource-to-distortion ratio (SDR), source-to-interference ratio (SIR), andsource-to-artifacts ratio (SAR) of heart and lung sounds, respectively.

A New Non-Negative Matrix Factorization Approach for Blind Source  Separation of Cardiovascular and Respiratory Sound Based on the Periodicity  of Heart and Lung Function