Article citationsMore>>

Gao, J.B., Sultan, H., Hu, J. and Tung, W.W. (2010) Denoising nonlinear time series by adaptive filtering and wavelet shrinkage: A comparison. IEEE Signal Processing Letters, 17, 237-240. doi:10.1109/LSP.2009.2037773

has been cited by the following article:

• TITLE: A new method of lung sounds filtering using modulated least mean square—Adaptive noise cancellation

  AUTHORS: Noman Qaid Al-Naggar

  KEYWORDS: Lung Sound; Filtering of Lung Sound; Least Mean Squares Algorithm; Adaptive Noise Cancelling

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.6 No.9, August 29, 2013

  ABSTRACT: Advanced processing of lung sound (LS) recording is a significant means to separate heart sounds (HS) and combined low frequency noise from instruments (NI), with saving its characteristics. This paper proposes a new method of LS filtering which separates HS and NI simultaneously. It focuses on the application of least mean squares (LMS) algorithm with adaptive noise cancelling (ANC) technique. The second step of the new method is to modulate the reference input r1(n) of LMS-ANC to acquiesce combining HS and NI signals. The obtained signal is removed from primary signal (original lung sound recording-LS). The original signal is recorded from subjects and derived HS from it and it is modified by a band pass filter. NI is simulated by generating approximately periodic white gaussian noise (WGN) signal. The LMS-ANC designed algorithm is controlled in order to determine the optimum values of the order L and the coefficient convergence μ. The output results are measured using power special density (PSD), which has shown the effectiveness of our suggested method. The result also has shown visual difference PSD (to) normal and abnormal LS recording. The results show that the method is a good technique for heart sound and noise reduction from lung sounds recordings simultaneously with saving LS characteristics.