Multimedia Respiratory Database (RespiratoryDatabase@TR): Auscultation Sounds and Chest X-rays

Auscultation is a method for diagnosis of especially internal medicine diseases such as cardiac, pulmonary and cardio-pulmonary by listening the internal sounds from the body parts. It is the simplest and the most common physical examination in the assessment processes of the clinical skills. In this study, the lung and heart sounds are recorded synchronously from left and right sides of posterior and anterior chest wall and back using two digital stethoscopes in Antakya State Hospital. The chest X-rays and the pulmonary function test variables and spirometric curves, the St. George respiratory questionnaire (SGRQ-C) are collected as multimedia and clinical functional analysis variables of the patients. The 4 channels of heart sounds are focused on aortic, pulmonary, tricuspid and mitral areas. The 12 channels of lung sounds are focused on upper lung, middle lung, lower lung and costophrenic angle areas of posterior and anterior sides of the chest. The recordings are validated and labelled by two pulmonologists evaluating the collected chest x-ray, PFT and auscultation sounds of the subjects. The database consists of 30 healthy subjects and 45 subjects with pulmonary diseases such as asthma, chronic obstructive pulmonary disease, bronchitis. The novelties of the database are the combination ability between auscultation sound results, chest X-ray and PFT; synchronously assessment capability of the lungs sounds; image processing based computerized analysis of the respiratory using chest X-ray and providing opportunity for improving analysis of both lung sounds and heart sounds on pulmonary and cardiac diseases.

The Diagnosis of Asthma using Hilbert-Huang Transform and Deep Learning on Lung Sounds

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.