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

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.