Classification of Respiratory Sounds Including Normal and Crackle/Rhonchi Pathologies

In the diagnosis of chest diseases, the success of classification with computerized decision assistance systems during auscultation and diagnosis by the specialist is increasing day by day. In this study, 100 respiratory sounds (50 normal and 50 abnormal) sampled at 4kHz, collected by electronic stethoscope for 15 seconds in accordance with routine clinical protocols, were used and no further patient data were used These sounds were classified by using Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) methods by applying normalization, spectrogram image with STFT, preprocessing such as MFCC and extraction of spectral features. The training set was determined as 80 and the test set as 20, and the success of classification after the study was 67% and 80%, respectively. It has been shown that CNN yields better results than SVM with clinical data with a success rate of 80% and also can be used to classes Ral and Rhonchi front pathological sounds and the highest success rate has been demonstrated by the use of the three layers CNN architecture.