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Multi-sensor lung sound extraction via time-shared channel identification and adaptive noise cancellation

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4 Author(s)
Le Yi Wang ; Dept. of Electr. & Comput. Eng., Wayne State Univ., Detroit, MI, USA ; Hong Wang ; Han Zheng ; Yin, G.

Noise artifacts are one of the key obstacles in applying continuous monitoring and computer-assisted analysis of lung sounds. This paper introduces a new methodology for extracting authentic lung sounds from a noisy environment. Unlike traditional noise cancellation methods that rely on frequency band separation or signal/noise independence to achieve noise reduction, this methodology combines the traditional noise canceling methods with the unique feature of timesplit stages in breathing sounds. By employing a multi-sensor system, the method performs time-shared blind identification and adaptive noise cancellation with recursion from breathing cycle to cycle. Since no frequency separation or signal/noise independence is required, this method can provide a robust and reliable capability of noise reduction, complementing the traditional methods.

Published in:

Decision and Control, 2004. CDC. 43rd IEEE Conference on  (Volume:4 )

Date of Conference:

14-17 Dec. 2004