Diagnosing of the lungs status using morphological anomalies of the signals in transformed domain

Lung sound (LS) contains information regarding the lungs status. Medical practitioners listen to these sounds using stethoscope and make interpretation. This procedure is known as auscultation which totally depends on the physicians experience and knowledge. There is a probability of misinterpretation due to human factor involved. In this paper, we propose a method based on complexity measuring theorem that can give reliable diagnosis of LS in an automated environment. The developed algorithm detects the lung conditions by calculating the sample entropy value of the frequency spectrum. The results are evaluated through statistical analysis and corroborated by a pulmonologist. The technique could be very useful in developing assisting device for medical professionals.