  Lung auscultation is the most effective and indispensable method for
diagnosing various respiratory disorders by using the sounds from the airways
during inspirium and exhalation using a stethoscope. In this study, the
statistical features are calculated from intrinsic mode functions that are
extracted by applying the HilbertHuang Transform to the lung sounds from 12
different auscultation regions on the chest and back. The classification of the
lung sounds from asthma and healthy subjects is performed using Deep Belief
Networks (DBN). The DBN classifier model with two hidden layers has been tested
using 5-fold cross validation method. The proposed DBN separated lung sounds
from asthmatic and healthy subjects with high classification performance rates
of 84.61%, 85.83%, and 77.11% for overall accuracy, sensitivity, and
selectivity, respectively using frequencytime analysis.
