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Developing a Reference of Normal Lung Sounds in Healthy Peruvian Children

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Abstract

Purpose

Lung auscultation has long been a standard of care for the diagnosis of respiratory diseases. Recent advances in electronic auscultation and signal processing have yet to find clinical acceptance; however, computerized lung sound analysis may be ideal for pediatric populations in settings, where skilled healthcare providers are commonly unavailable. We described features of normal lung sounds in young children using a novel signal processing approach to lay a foundation for identifying pathologic respiratory sounds.

Methods

186 healthy children with normal pulmonary exams and without respiratory complaints were enrolled at a tertiary care hospital in Lima, Peru. Lung sounds were recorded at eight thoracic sites using a digital stethoscope. 151 (81 %) of the recordings were eligible for further analysis. Heavy-crying segments were automatically rejected and features extracted from spectral and temporal signal representations contributed to profiling of lung sounds.

Results

Mean age, height, and weight among study participants were 2.2 years (SD 1.4), 84.7 cm (SD 13.2), and 12.0 kg (SD 3.6), respectively; and, 47 % were boys. We identified ten distinct spectral and spectro-temporal signal parameters and most demonstrated linear relationships with age, height, and weight, while no differences with genders were noted. Older children had a faster decaying spectrum than younger ones. Features like spectral peak width, lower-frequency Mel-frequency cepstral coefficients, and spectro-temporal modulations also showed variations with recording site.

Conclusions

Lung sound extracted features varied significantly with child characteristics and lung site. A comparison with adult studies revealed differences in the extracted features for children. While sound-reduction techniques will improve analysis, we offer a novel, reproducible tool for sound analysis in real-world environments.

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References

  1. Grenier MC, Gagnon K, Genest J Jr, Durand J, Durand LG (1998) Clinical comparison of acoustic and electronic stethoscopes and design of a new electronic stethoscope. Am J Cardiol 81:653–656

    Article  CAS  PubMed  Google Scholar 

  2. Gurung A, Scrafford CG, Tielsch JM, Levine OS, Checkley W (2011) Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: a systematic review and meta-analysis. Respir Med 105:1396–1403

    Article  PubMed Central  PubMed  Google Scholar 

  3. Guntupalli KK, Alapat PM, Bandi VD, Kushnir I (2008) Validation of automatic wheeze detection in patients with obstructed airways and in healthy subjects. J Asthma 45:903–907

    Article  PubMed  Google Scholar 

  4. Murphy RL, Vyshedskiy A, Power-Charnitsky VA, Bana DS, Marinelli PM, Wong-Tse A, Paciej R (2004) Automated lung sound analysis in patients with pneumonia. Respir Care 49:1490–1497

    PubMed  Google Scholar 

  5. Abaza AA, Day JB, Reynolds JS, Mahmoud AM, Goldsmith WT, McKinney WG, Petsonk EL, Frazer DG (2009) Classification of voluntary cough sound and airflow patterns for detecting abnormal pulmonary function. Cough 5:8

    Article  PubMed Central  PubMed  Google Scholar 

  6. Reichert S, Gass R, Brandt C, Andrès E (2008) Analysis of respiratory sounds: state of the art. Clin Med Circ Respirat Pulm Med 2:45–58

    PubMed Central  PubMed  Google Scholar 

  7. Lu X, Bahoura M (2008) An integrated automated system for crackles extraction and classification. Biomed Signal Process Control 3:244–254

    Article  Google Scholar 

  8. Ertel PY, Lawrence M, Brown RK, Stern AM (1966) Stethoscope acoustics: II. Transmission and filtration patterns. Circulation 34:899–909

    Article  CAS  PubMed  Google Scholar 

  9. Morrow B, Angus L, Greenhough D, Hansen A, McGregor G, Olivier O, Shillington L, Van der Horn P, Argent A (2010) The reliability of identifying bronchial breathing by auscultation. Int J Ther Rehabil 17:69–74

    Article  Google Scholar 

  10. Elphick HE, Lancaster GA, Solis A, Majumdar A, Gupta R, Smyth RL (2004) Validity and reliability of acoustic analysis of respiratory sounds in infants. Arch Dis Child 89:1059–1063

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  11. Waitman LR, Clarkson KP, Barwise JA, King PH (2000) Representation and classification of breath sounds recorded in an intensive care setting using neural networks. J Clin Monit Comput 16:95–105

    Article  CAS  PubMed  Google Scholar 

  12. Kahya YP, Yeginer M, Bilgic B (2006) Classifying respiratory sounds with different feature sets. Conf Proc IEEE Eng Med Biol Soc 1:2856–2859

    Article  PubMed  Google Scholar 

  13. Kandaswamy A, Kumar CS, Ramanathan RP, Jayaraman S, Malmurugan N (2004) Neural classification of lung sounds using wavelet coefficients. Comput Biol Med 34:523–537

    Article  CAS  PubMed  Google Scholar 

  14. Riella RJ, Nohama P, Maia JM (2009) Method for automatic detection of wheezing in lung sounds. Braz J Med Biol Res 42:674–684

    Article  CAS  PubMed  Google Scholar 

  15. Ellington LE, Gilman RH, Tielsch JM, Steinhoff M, Figueroa D, Rodriguez S, Caffo B, Tracey B, Elhilali M, West J, Checkley W (2012) Computerised lung sound analysis to improve the specificity of paediatric pneumonia diagnosis in resource-poor settings: protocol and methods for an observational study. BMJ Open 2:e000506

    Article  PubMed Central  PubMed  Google Scholar 

  16. Lederman D (2010) Estimation of infants’ cry fundamental frequency using a modified SIFT algorithm. Cornell Univ Online Lib 703–9. http://arxiv.org/abs/1009.2796v1. Accessed 25 Nov 2013

  17. Iyer SN, Oller DK (2008) Fundamental frequency development in typically developing infants and infants with severe-to-profound hearing loss. Clin Linguist Phon 22:917–936

    Article  PubMed Central  PubMed  Google Scholar 

  18. Emmanouilidou D, Elhilal M (2013) Characterization of noise contaminations in lung sound recordings. Conf Proc IEEE Eng Med Biol Soc 2013:2551–2554

    PubMed  Google Scholar 

  19. Boersma P (1993) Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound. Proc Inst Phon Sci Univ Amst 17:97–110

    Google Scholar 

  20. Gavriely N, Herzberg M (1992) Parametric representation of normal breath sounds. J Appl Physiol 73:1776–1784

    CAS  PubMed  Google Scholar 

  21. Nygårds ME, Sörnmo L (1983) Delineation of the QRS complex using the envelope of the e.c.g. Med Biol Eng Comput 21:538–547

    Article  PubMed  Google Scholar 

  22. Zhang Q, Manriquez AI, Médigue C, Papelier Y, Sorine M (2006) An algorithm for robust and efficient location of T-wave ends in electrocardiograms. IEEE Trans Biomed Eng 53:2544–2552

    Article  PubMed  Google Scholar 

  23. Choi S, Jiang Z (2008) Comparison of envelope extraction algorithms for cardiac sound signal segmentation. Expert Syst Appl 34:1056–1069

    Article  Google Scholar 

  24. Gavriely N, Nissan M, Rubin AH, Cugell DW (1995) Spectral characteristics of chest wall breath sounds in normal subjects. Thorax 50:1292–1300

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  25. Hadjileontiadis LJ (2009) Lung sounds: an advanced signal processing perspective. Morgan & Claypool, San Rafael, CA

    Google Scholar 

  26. Chi T, Ru P, Shamma SA (2005) Multiresolution spectrotemporal analysis of complex sounds. J Acoust Soc Am 118:887–906

    Article  PubMed  Google Scholar 

  27. Yang X, Wang K, Shamma SA (1992) Auditory representations of acoustic signals. IEEE Trans Inf Theory 38:824–839

    Article  Google Scholar 

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Acknowledgments

Additional support came from A.B. PRISMA, Instituto Nacional de Salud del Niño, and collaborators at JHU and Cincinnati Children’s Hospital. Thinklabs Medical (Centennial, CO) generously provided us with electronic stethoscopes at discount. Laura Ellington was supported by the Doris Duke Charitable Foundation Clinical Research Fellowship. Dimitra Emmanouilidou and Mounya Elhilali were partially supported by grants IIS-0846112 (NSF), 1R01AG036424-01 (NIH), N000141010278 (ONR), and N00014-12-1-0740 (ONR). William Checkley and James Tielsch were partially supported by the Bill and Melinda Gates Foundation (OPP1017682).

Conflict of interest

All authors in the study report no conflict of interest.

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Correspondence to William Checkley.

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Ellington, L.E., Emmanouilidou, D., Elhilali, M. et al. Developing a Reference of Normal Lung Sounds in Healthy Peruvian Children. Lung 192, 765–773 (2014). https://doi.org/10.1007/s00408-014-9608-3

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  • DOI: https://doi.org/10.1007/s00408-014-9608-3

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