title: Adventitious sounds identification and extraction using temporal-spectral dominance-based features.
abstract: Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system by the presence of adventitious sounds (ASs). Although many studies have addressed the problem of pathological RS classification, only a limited number of scientific works have focused on the analysis of the evolution of symptom-related signal components in joint time-frequency (TF) plane. This paper proposes a new signal identification and extraction method for various ASs based on instantaneous frequency (IF) analysis. The presented TF decomposition method produces a noise-resistant high definition TF representation of RS signals as compared to the conventional linear TF analysis methods, yet preserving the low computational complexity as compared to those quadratic TF analysis methods. The discarded phase information in conventional spectrogram has been adopted for the estimation of IF and group delay, and a temporal-spectral dominance spectrogram has subsequently been constructed by investigating the TF spreads of the computed time-corrected IF components. The proposed dominance measure enables the extraction of signal components correspond to ASs from noisy RS signal at high noise level. A new set of TF features has also been proposed to quantify the shapes of the obtained TF contours, and therefore strongly, enhances the identification of multicomponents signals such as polyphonic wheezes. An overall accuracy of  92.4±2.9% for the classification of real RS recordings shows the promising performance of the presented method.
authors.affiliation: Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada. feng.jin@ryerson.ca
authors.forename: Feng
authors.initials: F
authors.lastname: Jin
authors.forename: Sridhar Sri
authors.initials: SS
authors.lastname: Krishnan
authors.forename: Farook
authors.initials: F
authors.lastname: Sattar
mesh_terms: D000293:Adolescent; D000465:Algorithms; D002648:Child; D005260:Female; D006801:Humans; D008297:Male; D012135:Respiratory Sounds / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography / Q000379:methods*; D055815:Young Adult