Automatic respiratory sound classification using temporal-spectral dominance

Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system. Auscultation based diagnosis of pulmonary disorders relies on the presence of adventitious sounds. This paper proposes a new method for automatic RS classification based on instantaneous frequency (IF) analysis with the aim to identify various types of pathological RS. The presented method produces a high definition representation of RS signals in the time-frequency (TF) plane. The discarded phase information in spectrogram has been adopted here for the computation of IF and the subsequent temporal-spectral dominance. A new set of features have been extracted to quantify the shapes of the obtained individual TF contour and therefore strongly enhances the identification of multi-components signals such as polyphonic wheezes. An overall accuracy of 92.7 ± 2.9% on real RS recordings shows the promising performance by the presented method.