title: Computerised respiratory sounds can differentiate smokers and non-smokers.
abstract: Cigarette smoking is often associated with the development of several respiratory diseases however, if diagnosed early, the changes in the lung tissue caused by smoking may be reversible. Computerised respiratory sounds have shown to be sensitive to detect changes within the lung tissue before any other measure, however it is unknown if it is able to detect changes in the lungs of healthy smokers. This study investigated the differences between computerised respiratory sounds of healthy smokers and non-smokers. Healthy smokers and non-smokers were recruited from a university campus. Respiratory sounds were recorded simultaneously at 6 chest locations (right and left anterior, lateral and posterior) using air-coupled electret microphones. Airflow (1.0-1.5 l/s) was recorded with a pneumotachograph. Breathing phases were detected using airflow signals and respiratory sounds with validated algorithms. Forty-four participants were enrolled: 18 smokers (mean age 26.2, SD = 7 years; mean FEV1% predicted 104.7, SD = 9) and 26 non-smokers (mean age 25.9, SD = 3.7 years; mean FEV1% predicted 96.8, SD = 20.2). Smokers presented significantly higher frequency at maximum sound intensity during inspiration [(M = 117, SD = 16.2 Hz vs. M = 106.4, SD = 21.6 Hz; t(43) = -2.62, p = 0.0081, dz= 0.55)], lower expiratory sound intensities (maximum intensity: [(M = 48.2, SD = 3.8 dB vs. M = 50.9, SD = 3.2 dB; t(43) = 2.68, p = 0.001, dz= -0.78)]; mean intensity: [(M = 31.2, SD = 3.6 dB vs. M = 33.7,SD = 3 dB; t(43) = 2.42, p = 0.001, dz= 0.75)] and higher number of inspiratory crackles (median [interquartile range] 2.2 [1.7-3.7] vs. 1.5 [1.2-2.2], p = 0.081, U = 110, r = -0.41) than non-smokers. Significant differences between computerised respiratory sounds of smokers and non-smokers have been found. Changes in respiratory sounds are often the earliest sign of disease. Thus, computerised respiratory sounds might be a promising measure to early detect smoking related respiratory diseases.
authors.affiliation: Lab 3R-Respiratory, Research and Rehabilitation Laboratory, School of Health Sciences, University of Aveiro (ESSUA), Aveiro, Portugal.|Institute for Research in Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal.
authors.forename: Ana
authors.initials: A
authors.lastname: Oliveira
authors.affiliation: Department of Electrical and Electronics Engineering, Bogazici University, Istanbul, Turkey.
authors.forename: Ipek
authors.initials: I
authors.lastname: Sen
authors.affiliation: Department of Electrical and Electronics Engineering, Bogazici University, Istanbul, Turkey.
authors.forename: Yasemin P
authors.initials: YP
authors.lastname: Kahya
authors.affiliation: Institute for Research in Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal.|Department of Mathematics, University of Aveiro, Aveiro, Portugal.
authors.forename: Vera
authors.initials: V
authors.lastname: Afreixo
authors.affiliation: Lab 3R-Respiratory, Research and Rehabilitation Laboratory, School of Health Sciences, University of Aveiro (ESSUA), Aveiro, Portugal. amarques@ua.pt.|Institute for Research in Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal. amarques@ua.pt.
authors.forename: Alda
authors.initials: A
authors.lastname: Marques
mesh_terms: D000328:Adult; D001314:Auscultation / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D017563:Lung Diseases, Interstitial / Q000175:diagnosis* / Q000503:physiopathology*; D008297:Male; D012016:Reference Values; D015203:Reproducibility of Results; D012135:Respiratory Sounds / Q000503:physiopathology*; D012680:Sensitivity and Specificity; D012907:Smoking / Q000503:physiopathology*; D013018:Sound Spectrography / Q000379:methods*
keywords: Computerised auscultation; Crackles; Early diagnosis; Smoking; Sound spectrum