Respiratory sound classification using perceptual linear prediction features for healthy - Pathological diagnosis

Abstract

This study proposes a new model and feature extraction method for the classification of multi-channel respiratory sound data with the final aim of building a diagnosis aid tool for the medical doctor. Fourteen-channel data are processed separately and combined at feature level and fed to the support vector machines with radial basis kernel. Healthy-pathological subject based binary classification is employed where the recall rates for the healthy class and pathological class are 95 percent and 80 percent, respectively. The minimum precision rate is 80 percent. The method, when supported by additional features (adventitious sound frequency, type, etc.), may be employed in clinical practice as an aiding decision maker.