Cyclic system reconfiguration for adaptive separation of lung and heart sounds

This paper introduces an improved methodology of signal separation for removing noise and separating heart and lung sounds. Unlike traditional noise cancellation and signal separation methods that rely on frequency band separation or statistical independence to achieve signal separation, this methodology utilizes the unique feature of time-split stages in breath and cardiac cycles to simplify the blind source separation problem and enhance algorithm performance. By employing a multi-sensor system, the method performs time-split channel identification, adaptive signal separation, and noise cancellation, with recursion from cycle to cycle. Since no frequency separation or statistical independence is required, this method can provide a robust and reliable capability of signal extraction for enhancing accuracy and reliability of medical diagnosis

[1]  H. Pasterkamp,et al.  Respiratory sounds. Advances beyond the stethoscope. , 1997, American journal of respiratory and critical care medicine.

[2]  P. A. Ramamoorthy,et al.  Reduction of Heart Sounds from Lung Sounds by Adaptive Filterng , 1986, IEEE Transactions on Biomedical Engineering.

[3]  P. Khargonekar,et al.  Non-Euclidian metrics and the robust stabilization of systems with parameter uncertainty , 1985 .

[4]  M Nissan,et al.  Spectral characteristics of chest wall breath sounds in normal subjects. , 1995, Thorax.

[5]  L. Y. Wang,et al.  Local-global double algebras for slow H/sup infinity / adaptation. I. Inversion and stability , 1991 .

[6]  Bruce A. Francis,et al.  Feedback Control Theory , 1992 .

[7]  Le Yi Wang,et al.  Persistent identification of systems with unmodeled dynamics and exogenous disturbances , 2000, IEEE Trans. Autom. Control..

[8]  N Gavriely,et al.  Spectral characteristics of normal breath sounds. , 1981, Journal of applied physiology: respiratory, environmental and exercise physiology.

[9]  L.J. Hadjileontiadis,et al.  Adaptive reduction of heart sounds from lung sounds using fourth-order statistics , 1997, IEEE Transactions on Biomedical Engineering.

[10]  Le Yi Wang,et al.  Local–global double algebras for slow H∞ adaptation; the case of l2 disturbances , 1989 .

[11]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[12]  ZhengHAN,et al.  Time-shared channel identification for adaptive noise cancellation in breath sound extraction , 2004 .

[13]  S. Lehrer.  Understanding Lung Sounds , 2002 .

[14]  Zahra Moussavi,et al.  An overview of heart-noise reduction of lung sound using wavelet transform based filter , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).