Respiratory sound classification by using an incremental supervised neural network

Abstract

Determination of lung condition by auscultation is a difficult task and requires special training of medical staff. It is, however, a difficult skill to acquire. In decision making, it is significant to analyze respiratory sounds by an algorithm to give support to medical doctors. In this study, first, a rectangular window is formed so that one cycle of respiratory sound (RS) is contained in this window. Then, the windowed time samples are normalized. In order to extract the features, the normalized RS signal is partitioned into 64 samples of long segments. The power spectrum of each segment is computed, and synchronized summation of power spectra components is performed. Feature vectors are formed by the averaged power spectrum components, yielding 32-dimensional vectors. In the study, classification performances of multi-layer perceptron (MLP), grow and learn (GAL) network and a novel incremental supervised neural network (ISNN) are comparatively examined for the classification of nine different RS classes: Bronchial sounds, bronchovesicular sounds, vesicular sounds, crackles sounds, wheezes sounds, stridor sounds, grunting sounds, squawk sounds, and sounds of friction rub.