Mondal, A.Saxena, I.Tang, H.Banerjee, P.2026-02-052018IEEE Journal of Biomedical and Health Informatics, 2018, 22, 3, pp. 775-78421682194https://doi.org/10.1109/JBHI.2017.2667685https://idr.nitk.ac.in/handle/123456789/25161The main difficulty encountered in interpretation of cardiac sound is interference of noise. The contaminated noise obscures the relevant information, which are useful for recognition of heart diseases. The unwanted signals are produced mainly by lungs and surrounding environment. In this paper, a novel heart sound denoising technique has been introduced based on a combined framework of wavelet packet transform and singular value decomposition (SVD). The most informative node of the wavelet tree is selected on the criteria of mutual information measurement. Next, the coefficient corresponding to the selected node is processed by the SVD technique to suppress noisy component from heart sound signal. To justify the efficacy of the proposed technique, several experiments have been conducted with heart sound dataset, including normal and pathological cases at different signal to noise ratios. The significance of the method is validated by statistical analysis of the results. The biological information preserved in denoised heart sound signal is evaluated by the k-means clustering algorithm. The overall results show that the proposed method is superior than the baseline methods. © 2013 IEEE.Acoustic noiseBioinformaticsCardiologyEigenvalues and eigenfunctionsHeartK-means clusteringSignal to noise ratioSingular value decompositionWavelet analysisWavelet decompositionEigen-valueHeart soundsLung soundsthresholdWavelet packet transform(WPT)Biomedical signal processingalgorithmArticledecision treeentropyFourier transformationheart soundinformation processingnoise reductionnonhumansignal noise ratiosimulationsingular value decompositionwavelet packet transformheart auscultationhumanphysiologyreproducibilitysignal processingAlgorithmsHeart AuscultationHeart SoundsHumansReproducibility of ResultsSignal Processing, Computer-AssistedSignal-To-Noise Ratio