Characterization of noise contaminations in lung sound recordings.

Lung sound auscultation in non-ideal or busy clinical settings is challenged by contaminations of environmental noise. Digital pulmonary measurements are inevitably degraded, impeding the physician's work or any further processing of the acquired signals. The task is even harder when the patient population includes young children. Agitation and/or crying are captured into the recordings, additionally to any existing ambient noise. This study focuses on characterizing the different types of signal contaminations, expected to be encountered during lung sound measurements in non-ideal environments. Different noise types were considered, including background talk, radio playing, subject's crying, electronic interference sounds and stethoscope displacement artifacts. The individual characteristics were extracted, discussed and further compared to characteristics of clean segments. Additional exploration of discriminatory features led to a spectro-temporal signal representation followed by a standard SVM classifier. Although pulmonary and ambient sounds were both dominant in most sound clips, such a complex representation was deemed to be adequate, capturing most of the signal's distinguishing characteristics.