Top

Journal of Clinical Monitoring and Computing

Published in:

01-06-2017 | Original Research

Computerised respiratory sounds can differentiate smokers and non-smokers

Authors: Ana Oliveira, Ipek Sen, Yasemin P. Kahya, Vera Afreixo, Alda Marques

Published in: Journal of Clinical Monitoring and Computing | Issue 3/2017

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, d _z = 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, d _z = −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, d _z = 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.

Available only for authorised users

Milner D. The physiological effects of smoking on the respiratory system. Nurs Times. 2004;100(24):56–9.PubMed

Saldías F, Díaz O (2011) Cigarette smoking and lower respiratory tract infection. In: Martin-Loeches I (ed) Bronchitis. InTech. doi:10.5772/17652.

Beane J, Sebastiani P, Liu G, et al. Reversible and permanent effects of tobacco smoke exposure on airway epithelial gene expression. Genome Biol. 2007;8(9):R201. doi:10.1186/gb-2007-8-9-r201.CrossRefPubMedPubMedCentral

Marques A, Bruton A, Barney A. Clinically useful outcome measures for physiotherapy airway clearance techniques: a review. Phys Therapy Rev. 2006;11(4):299–307. doi:10.1179/108331906X163441.CrossRef

Parkes G, Greenhalgh T, Griffin M et al (2008) Effect on smoking quit rate of telling patients their lung age: the Step2quit randomised controlled trial, vol 336, vol 7644. doi:10.1136/bmj.39503.582396.25.

Baughman RP, Loudon RG. Lung sound analysis for continuous evaluation of airflow obstruction in asthma. Chest. 1985;88(3):364–8. doi:10.1378/chest.88.3.364.CrossRefPubMed

Piirila P. Changes in crackle characteristics during the clinical course of pneumonia. Chest. 1992;102(1):176–83. doi:10.1378/chest.102.1.176.CrossRefPubMed

Kiyokawa H, Pasterkamp H. Volume-dependent variations of regional lung sound, amplitude, and phase. J Appl Physiol (Bethesda, Md: 1985). 2002;93(3):1030–8. doi:10.1152/japplphysiol.00110.2002.CrossRef

Laird CW, Homburger F, Ishikawa S. Letter: Breath-sound changes after cigarette smoking. Lancet. 1974;1(7861):808. doi:10.1016/S0140-6736(74)92876-1.CrossRefPubMed

10.

Gavriely N, Nissan M, Cugell DW, et al. Respiratory health screening using pulmonary function tests and lung sound analysis. Eur Respir J. 1994;7(1):35–42. doi:10.1183/09031936.94.07010035.CrossRefPubMed

11.

Gavriely N, Nissan M, Rubin AH, et al. Spectral characteristics of chest wall breath sounds in normal subjects. Thorax. 1995;50(12):1292–300. doi:10.1136/thx.50.12.1292.CrossRefPubMedPubMedCentral

12.

Gross V, Dittmar A, Penzel T, et al. The relationship between normal lung sounds, age, and gender. Am J Respir Crit Care Med. 2000;162(3 Pt 1):905–9. doi:10.1164/ajrccm.162.3.9905104.CrossRefPubMed

13.

Bohadana A, Teculescu D, Martinet Y. Mechanisms of chronic airway obstruction in smokers. Respir Med. 2004;98(2):139–51.CrossRefPubMed

14.

Doherty DE, Belfer MH, Brunton SA et al. Chronic obstructive pulmonary disease: consensus recommendations for early diagnosis and treatment. J Fam Pract. (2006);55:S1–S5. http://www.jfponline.com/view-pdf.html?file=fileadmin/jfp_archive/uploadedFiles/Journal_Site_Files/Journal_of_Family_Practice/supplement_archive/JFPSupp_COPD_1106

15.

Miller MR. Standardisation of spirometry. Eur Respir J. 2005;26(2):319–38. doi:10.1183/09031936.05.00034805.CrossRefPubMed

16.

Sen I, Kahya YP (2005) A multi-channel device for respiratory sound data acquisition and transient detection. In: Conference proceedings: annual international conference of the IEEE engineering in medicine and biology society IEEE engineering in medicine and biology society annual conference, vol 6, pp 6658–6661. doi:10.1109/iembs.2005.1616029.

17.

Kraman SS, Wodicka GR, Oh Y, et al. Measurement of respiratory acoustic signals. Effect of microphone air cavity width, shape, and venting. Chest. 1995;108(4):1004–8.CrossRefPubMed

18.

Rossi M, Sovijarvi ARA, Piirila P, et al. Environmental and subject conditions and breathing manoeuvres for respiratory sound recordings. Eur Respir Rev. 2000;10(77):611–5.

19.

Vyshedskiy A, Murphy R. Crackle pitch rises progressively during inspiration in pneumonia, CHF, and IPF patients. Pulm Med. 2012;2012:240160. doi:10.1155/2012/240160.PubMedPubMedCentral

20.

Pinho C, Oliveira A, Jácome C, et al. Automatic crackle detection algorithm based on fractal dimension and box filtering. Proc Comput Sci. 2015;64:705–12. doi:10.1016/j.procs.2015.08.592.CrossRef

21.

Taplidou SA, Hadjileontiadis LJ. Wheeze detection based on time-frequency analysis of breath sounds. Comput Biol Med. 2007;37(8):1073–83. doi:10.1016/j.compbiomed.2006.09.007.CrossRefPubMed

22.

Jacome C, Oliveira A, Marques A. Computerized respiratory sounds: a comparison between patients with stable and exacerbated COPD. Clin Respir J. 2015;. doi:10.1111/crj.12392.PubMed

23.

Kompis M, Pasterkamp H, Oh Y et al. Distribution of inspiratory and expiratory respiratory sound intensity on the surface of the human thorax. In: Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th annual international conference of the IEEE, 1997 1997, vol 2045, pp 2047–2050. doi:10.1109/IEMBS.1997.758750.

24.

Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hellsdale: Lawrence Earlbaum Associates; 1988.

25.

Fritz CO, Morris PE, Richler JJ. Effect size estimates: current use, calculations, and interpretation. J Exp Psychol Gen. 2012;141(1):2–18. doi:10.1037/a0024338.CrossRefPubMed

26.

Jones A, Jones RD, Kwong K, et al. Effect of positioning on recorded lung sound intensities in subjects without pulmonary dysfunction. Phys Ther. 1999;79(7):682–90.CrossRefPubMed

27.

Malmberg LP, Sovijarvi AR, Paajanen E, et al. Changes in frequency spectra of breath sounds during histamine challenge test in adult asthmatics and healthy control subjects. Chest. 1994;105(1):122–31. doi:10.1378/chest.105.1.122.CrossRefPubMed

28.

Malmberg LP, Pesu L, Sovijarvi AR. Significant differences in flow standardised breath sound spectra in patients with chronic obstructive pulmonary disease, stable asthma, and healthy lungs. Thorax. 1995;50(12):1285–91. doi:10.1136/thx.50.12.1285.CrossRefPubMedPubMedCentral

29.

Vanuccini L, Earis J, Helisto P, et al. Capturing and pre-processing of respiratory sounds. Eur Respir Rev. 2000;10(77):616–20.

30.

Pasterkamp H, Kraman SS, Wodicka GR. Respiratory sounds. Advances beyond the stethoscope. Am J Respir Crit Care Med. 1997;156(3 Pt 1):974–87. doi:10.1164/ajrccm.156.3.9701115.CrossRefPubMed

31.

Leblanc P, Macklem PT, Ross WR. Breath sounds and distribution of pulmonary ventilation. Am Rev Respir Dis. 1970;102(1):10–6.PubMed

32.

Vyshedskiy A, Ishikawa S, Murphy RL Jr. Crackle pitch and rate do not vary significantly during a single automated-auscultation session in patients with pneumonia, congestive heart failure, or interstitial pulmonary fibrosis. Respir Care. 2011;56(6):806–17. doi:10.4187/respcare.00999.CrossRefPubMed

33.

Jacome C, Marques A. Pulmonary rehabilitation for mild COPD: a systematic review. Respir Care. 2014;59(4):588–94. doi:10.4187/respcare.02742.CrossRefPubMed

34.

Rennard SI, Drummond MB. Early chronic obstructive pulmonary disease: definition, assessment, and prevention. Lancet. 2015;385(9979):1778–88. doi:10.1016/s0140-6736(15)60647-x.CrossRefPubMedPubMedCentral

35.

Alzahrani M. Quantifying crackles in the lung of smoking and non-smoking young adults. Southampton: University of Southampton; 2011.

36.

Vannuccini L, Rossi M, Pasquali G. A new method to detect crackles in respiratory sounds. Technol Health Care Off J Eur Soc Eng Med. 1998;6(1):75–9.

37.

Strulovici-Barel Y, Omberg L, O’Mahony M, et al. Threshold of biologic responses of the small airway epithelium to low levels of tobacco smoke. Am J Respir Crit Care Med. 2010;182(12):1524–32. doi:10.1164/rccm.201002-0294OC.CrossRefPubMedPubMedCentral

38.

Bohadana A, Izbicki G, Kraman SS. Fundamentals of lung auscultation. New Engl J Med. 2014;370(8):744–51. doi:10.1056/NEJMra1302901.CrossRefPubMed

39.

Stendardi L, Binazzi B, Scano G. Exercise dyspnea in patients with COPD. Int J Chronic Obstr Pulm Dis. 2007;2(4):429–39.

40.

Pasterkamp H, Patel S, Wodicka GR. Asymmetry of respiratory sounds and thoracic transmission. Med Biol Eng Compu. 1997;35(2):103–6. doi:10.1007/BF02534138.CrossRef

41.

Kraman S. Lung sounds: relative sites of origin and comparative amplitudes in normal subjects. Lung. 1983;161(1):57–64. doi:10.1007/BF02713842.CrossRef

42.

Kanezaki M, Ebihara S, Nikkuni E, et al. Perception of urge-to-cough and dyspnea in healthy smokers with decreased cough reflex sensitivity. Cough (Lond, Engl). 2010;6:1–7. doi:10.1186/1745-9974-6-1.CrossRef

Title: Computerised respiratory sounds can differentiate smokers and non-smokers
Authors: Ana Oliveira
Ipek Sen
Yasemin P. Kahya
Vera Afreixo
Alda Marques
Publication date: 01-06-2017
Publisher: Springer Netherlands
Published in: Journal of Clinical Monitoring and Computing / Issue 3/2017
Print ISSN: 1387-1307
Electronic ISSN: 1573-2614
DOI: https://doi.org/10.1007/s10877-016-9887-8

At a glance: The STEP trials

Springer Medicine

Computerised respiratory sounds can differentiate smokers and non-smokers

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 3/2017

Erratum to: Journal of Clinical Monitoring and Computing 2016 end of year summary: cardiovascular and hemodynamic monitoring

The role of tracheal tube introducers and stylets in current airway management

Factors related to monitoring during admission of acute patients

In response to: tracheal tube stylets as a habit

Tracheal tube stylets as a habit

Tracheoscopic ventilation tube: a new step towards safer tracheostomy?