Classifying respiratory sounds with different feature sets.

In this study, different feature sets are used in conjunction with (k-nearest neighbors) k-NN and artificial neural network (ANN) classifiers to address the classification problem of respiratory sound signals. A comparison is made between the performances of k-NN and ANN classifiers with different feature sets derived from respiratory sound data acquired from one microphone placed on the posterior chest area. Each subject is represented by a single respiration cycle divided into sixty segments from which three different feature sets consisting of 6th order AR model coefficients, wavelet coefficients and crackle parameters in addition to AR model coefficients are extracted. Classification experiments are carried out on inspiration and expiration phases separately. The two class recognition problem between healthy and pathological subjects is addressed.