title: Acoustic mapping of the lung based on source localization of adventitious respiratory sound components.
abstract: The aim of this study is to devise a methodology to estimate and depict the source locations of respiratory adventitious sound components in the lungs, particularly crackles, associated with certain pulmonary diseases. Using the multichannel respiratory sound signals recorded on the chest wall, we have tried to locate the sources of crackling sounds. The source localization is performed using basic independent component analysis (basic ICA) followed by an evaluation of the mixing coefficients in a center of weights approach, where after the ICA, by taking the relevant mixing matrix coefficients and assuming them to be placed on the microphone locations, the estimated sound source location is calculated as the center of those weights. In order to select both the proper data segments prior to the ICA, and the relevant independent component (IC) among the source signal estimates of the ICA subsequently, a Bayesian classifier (under the assumption of Gaussian likelihoods) has been trained, using the data of the same subject yet a different acquisition session from the one intended for source localization. The outcome of the algorithm is a map of estimated source locations of crackles with respect to the microphone locations, which is presented together with the error performances (both validation and test) of the classifier. This approach for the estimation and mapping of the adventitious sound source locations in the lungs using the acoustic data may be a promising imaging alternative, which is practical, non-expensive and harmless.
authors.affiliation: Department of Electrical and Electronics Engineering, Bogazici University, Istanbul, Turkey. ipek@boun.edu.tr
authors.forename: Ipek
authors.initials: I
authors.lastname: Sen
authors.forename: Murat
authors.initials: M
authors.lastname: Saraclar
authors.forename: Yasemin P
authors.initials: YP
authors.lastname: Kahya
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001314:Auscultation / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008168:Lung / Q000503:physiopathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D017286:Polysomnography / Q000379:methods*; D015203:Reproducibility of Results; D012135:Respiratory Sounds / Q000503:physiopathology*; D012680:Sensitivity and Specificity; D013018:Sound Spectrography / Q000379:methods*