Time-shared channel identification for adaptive noise cancellation in breath sound extraction


Noise artifacts are one of the key obstacles in applying continuous monitoring and computer-assisted analysis of lung sounds. Traditional adaptive noise cancellation (ANC) methodologies work reasonably well when signal and noise are stationary and independent. Clinical lung sound auscultation encounters an acoustic environment in which breath sounds are not stationary and often correlate with noise. Consequendy, capability of ANC becomes significantly compromised. This paper introduces a new methodology for extracting authentic lung sounds from noise-corrupted measurements. Unlike traditional noise cancellation methods that rely on either frequency band separation or signal/noise independence to achieve noise reduction, this methodology combines the traditional noise canceling methods with the unique feature of time-split stages in breathing sounds. By employing a multi-sensor system, the method first employs a high-pass filter to eliminate the off-band noise, and then performs time-shared blind identification and noise cancellation with recursion from breathing cycle to cycle. Since no frequency separation or signal/noise independence is required, this method potentially has a robust and reliable capability of noise reduction, complementing the traditional methods.

This is a preview of subscription content, access via your institution.


  1. [1]

    Y. Tie, M. Sahin. Seperation of multi-channel spinal cord recordings using unsupervised adaptive filtering [C] // Proc.of EMBS/BMES Conf., Houston,USA, 2002:23-26.

  2. [2]

    A. Cichocki, R. Unbehauen. Robust neural networks with on-line learning for blind identification and blind separation [J]. IEEE Trans. on Circuit and Systems, 1996, 43 (11): 894 - 906.

    Article  Google Scholar 

  3. [3]

    J. Cardoso, B. H. Laheld. Equivariant adaptive source separation [J]. IEEE Trans. on Signal Processing, 1996, 44(12): 3017 - 3030.

    Article  Google Scholar 

  4. [4]

    S. Li, T. J. Sejnowski. Adaptive separation of mixed broad band sound sources with delays by a bearnforming Herault-Jutten network [J]. IEEE J. of Oceanic Engineering, 1995, 20(1):73 -79.

    Article  Google Scholar 

  5. [5]

    M. J. Alkindi, A. K. Alsamarrie, Th. M. Alanbakee. Performance improvements of adaptive FIR filters using adjusted step size LMS algorithm [C]// HF Radio Systems and Techniques Conf., Nottinghan, UK, 1997:7-10.

  6. [6]

    J. Kim, A. D. Poularikas. Comparison of two proposed methods in adaptive noise canceling [C] // Proc. of the 35 th Southeastern Symposium, Morgantown, USA, 2003:400 - 403.

  7. [7]

    C. Kim, H. Park, Y. Choi, et al. FPGA implementation of ICA algorithm for blind signal separation and adaptive noise canceling [J]. IEEE Trans. on. Network, 2003, 14(5): 1038 - 1046.

    Article  Google Scholar 

  8. [8]

    S. Haykin. Unsupervised Adaptive Filtering [M]. Vol.I and II, New York: John Wiley & Sons, Inc., 2000.

    Google Scholar 

  9. [9]

    G. C. Goodwin, K. S. Sin. Adaptive Filtering Prediction and Control [M]. New York: Prentice Hall, 1984.

    Google Scholar 

  10. [10]

    B. Widrow. Adaptive noise canceling: principle and application [J]. Proc. IEEE, 1975, 63: 1692 - 1716.

    Article  Google Scholar 

  11. [11]

    H. Wang, L. Wang. Continuous intro-operative respiratory auscultation in anesthesia [C] // Proc. of IEEE Sensors 2003, Toronto, 2003.

  12. [12]

    H. Wang, L. Wang. Multi-sensor adaptive heart and Lung sound extraction [C]// Proc. of IEEE Sensors 2003, Toronto, 2003.

  13. [13]

    H. Wang, L. Wang, H. Zheng, et al. Lung sound/noise separation in anesthesia respiratory monitoring [J]. WSEAS Trans. on Systems, 2004, 3(4): 1839- 1844.

    Google Scholar 

  14. [14]

    L. Wang, G. Yin, H. Wang. Nonlinear system identification in medical applications [M] // SYSID 2003, Rotterdam, The Netherlands, August 27-29,2003.

  15. [15]

    L. Wang, G. Yin. Persistent identification of systems with unmodeled dynamics and exogenous disturbances [J]. IEEE Trans. on Automatic Control, 2000, 45(7): 1246- 1256.

    MATH  Article  MathSciNet  Google Scholar 

  16. [16]

    S. Karlin, H. M. Taylor. A First Course in Stochastic Processes [M]. 2nd Ed. New York: Academic Press, 1975.

    Google Scholar 

  17. [17]

    P. Billingsley. Convergence of Probability Measures [M]. New York: John Wiley, 1968.

    Google Scholar 

  18. [18]

    P. Hall, C. C. Heyde. Martingale Limit Theory and Its Application [M]. New York: Academic Press, 1980.

    Google Scholar 

  19. [19]

    A. Dembo, O. Zeitouni. Large Deviations Techniques and Applications [M]. New York: Springer-Verlag, 1998.

    Google Scholar 

  20. [20]

    J. GÄrtner. On large deviations from the invariant measure [J]. Theory Probab. Appl., 1977, 22 (1): 24 - 39.

    MATH  Article  Google Scholar 

  21. [21]

    S. Lehrer. Understanding Lung Sounds [M]. 3rd ed. Philadelphia: W. B. Sounders Company, 2002.

    Google Scholar 

  22. [22]

    H. Pasterkamp, S. Kraman, G. Wodicka. Respiratory sounds [J]. Am.J.Respir Crit.Care Med., 1997, 156(3): 974 - 987.

    Google Scholar 

  23. [23]

    A. R.Sovijarvi, P. Helisto, L. P.Malmberg, et al. A new versatile PC-based lung sound analyzer with automatic crackle analysis (HeLSA) ; repeatability of spectral parameters and sound amplitude in healthy subjects [J]. Technology & Health Care, 1998, 6(1): 11 - 22.

    Google Scholar 

  24. [24]

    G. Mirchandani, R. L. Zinser, J. B. Evans. A new adaptive noise cancellation scheme in presence of crosstalk [J]. IEEE Trans. on Circuit and System, 1992, 39(10): 681 - 694.

    MATH  Article  Google Scholar 

  25. [25]

    L. Ljung. System Identification: Theory for the User [M]. Englewood Cliffs, NJ: Prentice-Hall, 1987.

    Google Scholar 

  26. [26]

    B. Widrow, S. D. Stearns. Adaptive Signal Processing [M]. Englewood Oliffs, NJ: Prentice-Hall, 1985.

    Google Scholar 

Download references

Author information



Additional information

Hong Wang's research was supported in part by the Anesthesiology Department at Wayne State University and in part by Wayne State University Research Enhancement Program;Leyi Wang's research was supported in part by the National Science Foundation (No. ECS - 0329597) , and in part by Wayne State University Research Enhancement Program;George Yin‘s research was supported in part by the National Science Foundation (No.DMS-O3O4928) ,and in part by Wayne State University Research Enhancement Program.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Han, Z., Wang, H., Wang, L. et al. Time-shared channel identification for adaptive noise cancellation in breath sound extraction. J. Control Theory Appl. 2, 209–221 (2004). https://doi.org/10.1007/s11768-004-0001-2

Download citation


  • Lung sound analysis
  • Noise cancellation
  • Blind signal extraction
  • System identification
  • Adaptive filtering