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Determining airflow obstruction from tracheal sound analysis: simulated tests and evaluations in patients with acromegaly

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Abstract

To evaluate the ability of tracheal sound analysis (TSA) to detect airflow obstruction, particularly in patients with acromegaly. A simulated analysis compared free airflow conditions with airflow through orifice plates 6, 8, 10 and 12 mm in diameter. Based on these results, TSA and spirometry examinations were performed on controls (n = 17) and patients with acromegaly (n = 17). The simulated study showed that airway obstruction and airflow values increased the values of power and a progressive displacement of the spectral distribution towards higher frequencies. In agreement with the simulation, airway obstruction in patients with acromegaly also resulted in increased values of power (p < 0.002) and displacement of the spectral distribution (p < 0.01). Significant associations were observed between the TSA parameters and the spirometry indices of obstruction (p < 0.02). In addition, the TSA parameters achieved adequate diagnostic accuracy (AUC ≥ 0.887). The present study provides evidence that TSA during resting breathing would provide adequate biomarkers of early upper airway changes in patients with acromegaly. TSA is carried out during spontaneous ventilation, requires little from the patient, and is fast and inexpensive. Taken together, these practical considerations and the results of the present study suggest that TSA may improve lung function tests for patients with acromegaly.

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Summary of the study, overall design flow and the main results obtained.

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Abbreviations

%pred:

Percentage of the predicted values

AG:

Group with acromegaly

AUC:

Area under the receiver operating characteristic curve

CG:

Control group

FEF:

Forced expiratory flow between 25 and 75% of the FVC (L/s)

FEV1 :

The forced expiratory volume in the first second, obtained from a maximal expiratory effort manoeuvre (L)

FEV1/FVC:

Tiffeneau–Pinelli index

FVC:

Forced vital capacity, the total amount of air exhaled during the spirometry examinations (L)

LOOCV:

Leave-one-out cross-validation

PEF:

Peak expiratory flow (L/s)

Pm:

Mean power in the complete studied frequency range (100 to 2500 Hz) (dB)

PmLF:

Mean power in the frequency range of 130–250 Hz (dB)

PmLFnorm :

Mean power in the frequency range of 130–250 Hz normalized to the mean power observed at 0.2 L/s (dB)

Pmnorm :

Mean power normalized to the mean power observed at 0.2 L/s (dB)

ROC:

Receiver operating characteristic curve

S:

The slope of the regression line in the power spectra (db/dec)

Se:

Sensitivity, the proportion of actual positives that are correctly identified as such

Sp:

Specificity, the proportion of actual negatives that are correctly identified as such

TSA:

Tracheal sounds analysis

References

  1. Baughman RP, Loudon RG (1989) Stridor: differentiation from asthma or upper airway noise. Am Rev Respir Dis 139:1407–1409. https://doi.org/10.1164/ajrccm/139.6.1407

    Article  CAS  PubMed  Google Scholar 

  2. Beck R, Rosenhouse G, Mahagnah M, Chow RM, Cugell DW, Gavriely N (2005) Measurements and theory of normal tracheal breath sounds. Ann Biomed Eng 33:1344–1351. https://doi.org/10.1007/s10439-005-5564-7

    Article  PubMed  Google Scholar 

  3. Bousquet J, Tanasescu CC, Camuzat T, Anto JM, Blasi F, Neou A, Palkonen S, Papadopoulos NG, Antunes JP, Samolinski B, Yiallouros P, Zuberbier T (2013) Impact of early diagnosis and control of chronic respiratory diseases on active and healthy ageing. A debate at the European Union Parliament. Allergy 68:555–561. https://doi.org/10.1111/all.12115

    Article  CAS  PubMed  Google Scholar 

  4. Brody JS, Fisher AB, Gocmen A, DuBois AB (1970) Acromegalic pneumonomegaly: lung growth in the adult. J Clin Invest 49:1051–1060

    Article  CAS  Google Scholar 

  5. Camilo GB, Carvalho AR, Guimaraes AR, Kasuki L, Gadelha MR, Mogami R, de Melo PL, Lopes AJ (2017) Computed tomography airway lumen volumetry in patients with acromegaly: Association with growth hormone levels and lung function. J Med Imaging Radiat Oncol. https://doi.org/10.1111/1754-9485.12598

    Article  PubMed  Google Scholar 

  6. Camilo GB, Carvalho ARS, Guimaraes ARM, Kasuki L, Gadelha MR, Mogami R, de Melo PL, Lopes AJ (2017) Computed tomography airway lumen volumetry in patients with acromegaly: Association with growth hormone levels and lung function. J Med Imaging Radiat Oncol 61:591–599. https://doi.org/10.1111/1754-9485.12598

    Article  PubMed  Google Scholar 

  7. Camilo GB et al (2015) Correlations between forced oscillation technique parameters and pulmonary densitovolumetry values in patients with acromegaly. 48

  8. Colao A, Ferone D, Marzullo P, Lombardi G (2004) Systemic complications of acromegaly: epidemiology, pathogenesis, and management. Endocr Rev 25:102–152

    Article  CAS  Google Scholar 

  9. Croxton TL, Weinmann GG, Senior RM, Hoidal JR (2002) Future research directions in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 165:838–844. https://doi.org/10.1164/ajrccm.165.6.2108036

    Article  PubMed  Google Scholar 

  10. Davi MV, Dalle Carbonare L, Giustina A, Ferrari M, Frigo A, Lo Cascio V, Francia G (2008) Sleep apnoea syndrome is highly prevalent in acromegaly and only partially reversible after biochemical control of the disease. Eur J Endocrinol 159:533–540

    Article  CAS  Google Scholar 

  11. Dawson B, Trapp RG (2001) Basic & Clinical Biostatistics, 3rd ed. Lange Medical Bks. McGraw-Hill, London, England,

  12. Drummond MB, Buist AS, Crapo JD, Wise RA, Rennard SI (2014) Chronic obstructive pulmonary disease: NHLBI Workshop on the Primary Prevention of Chronic Lung Diseases. Ann Am Thorac Soc 11(Suppl 3):S154-160. https://doi.org/10.1513/AnnalsATS.201312-432LD

    Article  PubMed  PubMed Central  Google Scholar 

  13. Earis JE, Cheetham BMG (2000) Future perspectives for respiratory sound research. Eur Respir Rev 10:641–646

    Google Scholar 

  14. East KA, East TD (1985) Computerized acoustic detection of obstructive apnea. Comput Methods Programs Biomed 21:213–220. https://doi.org/10.1016/0169-2607(85)90006-9

    Article  CAS  PubMed  Google Scholar 

  15. Elwali A, Moussavi Z (2017) Obstructive Sleep Apnea Screening and Airway Structure Characterization During Wakefulness Using Tracheal Breathing Sounds. Ann Biomed Eng 45:839–850. https://doi.org/10.1007/s10439-016-1720-5

    Article  PubMed  Google Scholar 

  16. Elwali A, Moussavi Z (2019) A Novel Decision Making Procedure during Wakefulness for Screening Obstructive Sleep Apnea using Anthropometric Information and Tracheal Breathing Sounds. Sci Rep 9:11467. https://doi.org/10.1038/s41598-019-47998-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Evans CC, Hipkin LJ, Murray GM (1977) Pulmonary function in acromegaly. Thorax 32:322–327

    Article  CAS  Google Scholar 

  18. Fesmire FM, Pesce RR (1989) Tracheal obstruction presenting as new-onset wheezing. Am J Emerg Med 7:173–176. https://doi.org/10.1016/0735-6757(89)90132-0

    Article  CAS  PubMed  Google Scholar 

  19. Gavriely N, Cugell DW (1995) Breath Sounds Methodology. 1st Edition edn. CRC Press, Boca Raton. https://doi.org/10.1201/9780429260544

  20. Gavriely N, Nissan M, Cugell DW, Rubin AH (1994) Respiratory health screening using pulmonary function tests and lung sound analysis. Eur Respir J 7:35–42. https://doi.org/10.1183/09031936.94.07010035

    Article  CAS  PubMed  Google Scholar 

  21. Goedhart DM, Zanen P, Kerstjens HA, Lammers JW (2005) Discriminating asthma and COPD based on bronchodilator data: an improvement of the methods. Physiol Meas 26:1115–1123. https://doi.org/10.1088/0967-3334/26/6/020

    Article  PubMed  Google Scholar 

  22. Greiner M, Pfeiffer D, Smith RD (2000) Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev Vet Med 45:23–41

    Article  CAS  Google Scholar 

  23. Grunstein RR, Ho KY, Sullivan CE (1991) Sleep apnea in acromegaly. Ann Intern Med 115:527–532

    Article  CAS  Google Scholar 

  24. Harper VP, Pasterkamp H, Kiyokawa H, Wodicka GR (2003) Modeling and measurement of flow effects on tracheal sounds. IEEE Trans Biomed Eng 50:1–10. https://doi.org/10.1109/Tbme.2002.807327

    Article  PubMed  Google Scholar 

  25. Harrison BD, Millhouse KA, Harrington M, Nabarro JD (1978) Lung function in acromegaly. Q J Med 47:517–532

    CAS  PubMed  Google Scholar 

  26. Hok B, Bythell V, Bengtsson M (1988) Development of a wireless stethoscope for auscultatory monitoring during anaesthesia. Med Biol Eng Compu 26:317–320. https://doi.org/10.1007/BF02447088

    Article  CAS  Google Scholar 

  27. Huq S, Moussavi Z (2012) Acoustic breath-phase detection using tracheal breath sounds. Med Biol Eng Compu 50:297–308. https://doi.org/10.1007/s11517-012-0869-9

    Article  Google Scholar 

  28. Hyatt RE, Scandon PD, Nakamura M (1997) Interpretation of pulmonary function tests. Lippincott-Raven, Phyladelphia

    Google Scholar 

  29. Junior NAL, Queiroz IM, Oliveira NV, Lopes AJ, Melo PL (2018) Diagnosis of respiratory abnormalities using tracheal sounds analysis: Instrumentation and evaluation in simulated and in vivo tests. Paper presented at the XXVI Congresso Brasileiro de Engenharia Biomédica - CBEB 2018, Nuzios, Rio de Janeiro, October, 21–25.

  30. Kulkas A, Huupponen E, Virkkala J, Saastamoinen A, Rauhala E, Tenhunen M, Himanen SL (2010) Tracheal sound parameters of respiratory cycle phases show differences between flow-limited and normal breathing during sleep. Physiol Meas 31:427–438. https://doi.org/10.1088/0967-3334/31/3/010

    Article  CAS  PubMed  Google Scholar 

  31. Luboshitzky R, Barzilai D (1980) Hypoxemia and pulmonary function in acromegaly. Am Rev Respir Dis 121:471–475. https://doi.org/10.1164/arrd.1980.121.3.471

    Article  CAS  PubMed  Google Scholar 

  32. Malmberg LP, Sovijarvi AR, Paajanen E, Piirila P, Haahtela T, Katila T (1994) Changes in frequency spectra of breath sounds during histamine challenge test in adult asthmatics and healthy control subjects. Chest 105:122–131. https://doi.org/10.1378/chest.105.1.122

    Article  CAS  PubMed  Google Scholar 

  33. Melmed S (2006) Medical progress: Acromegaly. N Engl J Med 355:2558–2573. https://doi.org/10.1056/NEJMra062453

    Article  CAS  PubMed  Google Scholar 

  34. Moussavi Z (2006) Fundamentals of Respiratory Sounds and Analysis. Synthesis Lectures in Biomedical Engineering. Morgan & Claypool Publishers, USA. https://doi.org/10.2200/S00054ED1V01Y200609BME008

  35. Mussell MJ, Nakazono Y, Miyamoto Y, Okabe S, Takishima T (1990) Distinguishing normal and abnormal tracheal breathing sounds by principal component analysis. Jpn J Physiol 40:713–721

    Article  CAS  Google Scholar 

  36. Muthusamy PD, Sundaraj K, Abd Manap N (2020) Computerized acoustical techniques for respiratory flow-sound analysis: a systematic review. Artif Intell Rev 53:3501–3574. https://doi.org/10.1007/s10462-019-09769-6

    Article  Google Scholar 

  37. Niu J, Shi Y, Cai M, Cao Z, Wang D, Zhang Z, Zhang XD (2018) Detection of sputum by interpreting the time-frequency distribution of respiratory sound signal using image processing techniques. Bioinformatics 34:820–827. https://doi.org/10.1093/bioinformatics/btx652

    Article  CAS  PubMed  Google Scholar 

  38. Pasterkamp H, Sanchez I (1992) Tracheal sounds in upper airway obstruction. Chest 102:963–965

    Article  CAS  Google Scholar 

  39. Pereira CA (2002) Espirometria. Jornal de Pneumologia 28

  40. Plante F, Kessler H, Sun XQ, Cheetham BM, Earis JE (1998) Inverse filtering applied to upper airway sounds. Technology and health care : official journal of the European Society for Engineering and Medicine 6:23–32

    Article  CAS  Google Scholar 

  41. Priftis KN, Hadjileontiadis LJ, Everard ML (2018) Breath Sounds: From Basic Science to Clinical Practice. Springer International Publishing,

  42. Que CL, Kolmaga C, Durand LG, Kelly SM, Macklem PT (2002) Phonospirometry for noninvasive measurement of ventilation: methodology and preliminary results. J Appl Physiol 93:1515–1526. https://doi.org/10.1152/japplphysiol.00028.2002

    Article  PubMed  Google Scholar 

  43. Saarinen A, Rihkanen H, Malmberg LP, Pekkanen L, Sovijarvi AR (2001) Tracheal sounds and airflow dynamics in surgically treated unilateral vocal fold paralysis. Clin Physiol 21:223–228

    Article  CAS  Google Scholar 

  44. Sabil A, Glos M, Gunther A, Schobel C, Veauthier C, Fietze I, Penzel T (2019) Comparison of Apnea Detection Using Oronasal Thermal Airflow Sensor, Nasal Pressure Transducer, Respiratory Inductance Plethysmography and Tracheal Sound Sensor. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine 15:285–292. https://doi.org/10.5664/jcsm.7634

    Article  Google Scholar 

  45. Sabil A, Marien C, LeVaillant M, Baffet G, Meslier N, Gagnadoux F (2020) Diagnosis of sleep apnea without sensors on the patient’s face. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine 16:1161–1169. https://doi.org/10.5664/jcsm.8460

    Article  Google Scholar 

  46. Sarraf Shirazi S, Moussavi Z (2012) Silent aspiration detection by breath and swallowing sound analysis. Conf Proc IEEE Eng Med Biol Soc 2012:2599–2602. https://doi.org/10.1109/EMBC.2012.6346496

    Article  Google Scholar 

  47. Schreur HJ, Diamant Z, Vanderschoot J, Zwinderman AH, Dijkman JH, Sterk PJ (1996) Lung sounds during allergen-induced asthmatic responses in patients with asthma. Am J Respir Crit Care Med 153:1474–1480. https://doi.org/10.1164/ajrccm.153.5.8630589

    Article  CAS  PubMed  Google Scholar 

  48. Schreur HJ, Sterk PJ, Vanderschoot J, van Klink HC, van Vollenhoven E, Dijkman JH (1992) Lung sound intensity in patients with emphysema and in normal subjects at standardised airflows. Thorax 47:674–679. https://doi.org/10.1136/thx.47.9.674

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Schreur HJ, Vanderschoot J, Zwinderman AH, Dijkman JH, Sterk PJ (1994) Abnormal lung sounds in patients with asthma during episodes with normal lung function. Chest 106:91–99. https://doi.org/10.1378/chest.106.1.91

    Article  CAS  PubMed  Google Scholar 

  50. Sovijärvi ARA, Malmberg LP, Charbonneau G, Vanderschoot J, Dalmasso F, Sacco C, Rossi M, Earis JE (2000) Characteristics of breath sounds and adventitious respiratory sounds. Eur Respir Rev 10:591–596

    Google Scholar 

  51. Stormann S, Gutt B, Roemmler-Zehrer J, Bidlingmaier M, Huber RM, Schopohl J, Angstwurm MW (2017) Assessment of lung function in a large cohort of patients with acromegaly. Eur J Endocrinol 177:15–23. https://doi.org/10.1530/EJE-16-1080

    Article  PubMed  Google Scholar 

  52. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293

    Article  CAS  Google Scholar 

  53. Tukiman MM, Ghazali MNM, Sadikin A, Nasir NF, Nordin N, Sapit A, Razali MA (2017) CFD simulation of flow through an orifice plate. Paper presented at the IOP Conference Series: Materials Science and Engineering, Volume 243, 2nd International Conference on Computational Fluid Dynamics in Research and Industry (CFDRI 2017), Songhkla, Thailand,

  54. Witten IH, Frank E (1999) Data Mining: Practical Machine Learning Tools and Techniques. 2nd edn. Morgan Kaufmann Publishers, San Francisico. https://doi.org/10.1186/1475-925X-5-51

  55. Yadollahi A, Montazeri A, Azarbarzin A, Moussavi Z (2013) Respiratory Flow-Sound Relationship During Both Wakefulness and Sleep and Its Variation in Relation to Sleep Apnea. Ann Biomed Eng 41:537–546. https://doi.org/10.1007/s10439-012-0692-3

    Article  PubMed  Google Scholar 

  56. Yadollahi A, Moussavi Z (2011) Detailed analysis of the relationship between tracheal breath sounds and airflow in relation to OSA during wake and sleep. Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual Conference 2011:6797–6800. https://doi.org/10.1109/IEMBS.2011.6091676

    Article  CAS  Google Scholar 

  57. Yonemaru M, Kikuchi K, Mori M, Kawai A, Abe T, Kawashiro T, Ishihara T, Yokoyama T (1993) Detection of tracheal stenosis by frequency analysis of tracheal sounds. J Appl Physiol 75:605–612

    Article  CAS  Google Scholar 

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Acknowledgements

This study was supported by the Brazilian Council for Scientific and Technological Development (CNPq), the Rio de Janeiro State Research Supporting Foundation (FAPERJ) and in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.

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Lima Junior, N.A., Oliveira, N.V., Tavares, A.B.W. et al. Determining airflow obstruction from tracheal sound analysis: simulated tests and evaluations in patients with acromegaly. Med Biol Eng Comput 60, 2001–2014 (2022). https://doi.org/10.1007/s11517-022-02584-2

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