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Computerized acoustical techniques for respiratory flow-sound analysis: a systematic review

  • Priya Devi MuthusamyEmail author
  • Kenneth Sundaraj
  • Nurulfajar Abd Manap
Article
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Abstract

Computerized respiratory sound analysis has recently captured the attention of researchers, and its implementation can assist physicians in the diagnosis of pulmonary pathologies. The relationship between respiratory sounds and breathing flow reveals the pathophysiology of the respiratory system and can be used as a basis for acoustical airflow estimation. Respiratory sound signals are also acoustically analysed for the detection of breath phases. Although this research area is being actively studied, the available literature has not been reviewed. This manuscript highlights the previous studies that focused on the use of computer-based acoustical techniques for the analysis of respiratory sounds and airflow. Articles related to computerized respiratory flow-sound analysis were identified through a search of the Scopus academic database, and 66 articles were ultimately selected for this systematic review. A brief overview of the subject details, auscultation sites, respiratory manoeuvres, sound parameters of interest and techniques used is provided. The findings revealed the following: (1) deterministic relationships can be established between airflow and respiratory sounds, (2) an established strong flow-sound correlation can be used for airflow estimation, (3) breath phase detection and identification without flow measuring devices remains in the infancy research stage and (4) further research needed to examine the potential of computerized respiratory sound analysis in revealing the pathophysiology of airways for future clinical implementation. This review concludes by discussing the possibilities and recommendations for further advancements in computerized acoustical flow-sound analysis.

Keywords

Respiratory sound analysis Flow-sound relationship Airflow estimation Breath phase detection 

Notes

Acknowledgements

The authors would like to thank Universiti Teknikal Malaysia Melaka (UTeM) and Malaysian Ministry of Education for providing financial aid through the Fundamental Research Grant Scheme, FRGS (FRGS/1/2016/TK04/UTEM/01/1).

Compliance with ethical standards

Conflict of interest

There is no conflict of interest to declare.

References

  1. Anusha AR, Soodi AL, Kumar SP (2012) Design of low-cost hardware for lung sound acquisition and determination of inspiratory-expiratory phase using respiratory waveform. In: Proceedings of IEEE international conference on computing communication and networking technologies, pp 1–5Google 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(10):1344–1351CrossRefGoogle Scholar
  3. Bohadana AB, Peslin R, Uffholtz H (1978) Breath sounds in the clinical assessment of airflow obstruction. Thorax 33(3):345–351CrossRefGoogle Scholar
  4. Bohadana AB, Peslin R, Ufffioltz H, Pauli G (1995) Potential for lung sound monitoring during bronchial provocation testing. Thorax 50(9):955–961CrossRefGoogle Scholar
  5. Christensen MA, Leitner E, Levy B, Sathiyamoorthy BB, Burke DT, Sisson TH (2014) Breath sounds analysis for asthma monitoring: a method for automated detection of flow events from tracheal recordings. In: Proceedings of IEEE conference on healthcare innovations and point of care technologies, pp 331–334Google Scholar
  6. Chuah JS, Moussavi ZK (2000) Automated respiratory phase detection by acoustical means. In: Proceedings of conference on systems, cybernatics & informatics, pp 228–231Google Scholar
  7. Ciftci K, Kahya YP (2008) Respiratory airflow estimation by time varying autoregressive modeling. In: Proceedings of IEEE conference on engineering in medicine and biology society, pp 347–350Google Scholar
  8. Fischer HS, Puder LC, Wilitzki S, Usemann J, Buhrer C, Godfrey S, Schmalisch G (2016) Relationship between computerized wheeze detection and lung function parameters in young infants. Pediatr Pulmonol 51:402–410CrossRefGoogle Scholar
  9. Fiz JA, Jané R, Lozano M, Gómez R, Ruiz J (2014) Detecting unilateral phrenic paralysis by acoustic respiratory analysis. PLoS ONE 9(4):e93595CrossRefGoogle Scholar
  10. Golabbakhsh M, Moussavi Z (2004) Relationship between airflow and frequency-based features of tracheal respiratory sound. In: Proceedings of IEEE Canadian conference on electrical and computer engineering, pp 751–754Google Scholar
  11. Golabbakhsh M, Moussavi Z, Aboofazeli M (2005) Respiratory flow estimation from tracheal sound by adaptive filters. In: Proceedings of IEEE conference on engineering in medicine and biology, pp 4216–4219Google Scholar
  12. Gomes LL, Oliveira NV, Tauil LM, Mattos RA, Melo PL (2018) Instrumentation for respiratory flow estimation using tracheal sounds analysis: design and evaluation in measurements of respiratory cycle periods and airflow amplitude. In: Proceedings of IOP Journal of Physics: Conference series, pp 1–7Google Scholar
  13. Habukawa C, Nagasaka Y, Murakami K, Takemura T (2009) High-pitched breath sounds indicate airflow limitation in asymptomatic asthmatic children. Respirology 14(3):399–403CrossRefGoogle Scholar
  14. Hossain I, Moussavi Z (2002a) Respiratory airflow estimation by acoustical means. In: Proceedings of IEEE conference on biomedical engineering society/engineering in medicine and biology society, pp 1476–1477Google Scholar
  15. Hossain I, Moussavi Z (2002b) Relationship between airflow and normal lung sounds. In: Proceedings of IEEE Canadian conference on electrical and computer engineering, pp 1120–1122Google Scholar
  16. Hossain I, Moussavi Z (2004) Finding the lung sound-flow relationship in normal and asthmatic subjects. In: Proceedings of IEEE conference on engineering in medicine and biology society, pp 3852–3855Google Scholar
  17. Huq S, Moussavi Z (2012) Acoustic breath-phase detection using tracheal breath sounds. Med Biol Eng Comput 50(3):297–308CrossRefGoogle Scholar
  18. Jin F, Sattar F (2016) Unsupervised phase detection for respiratory sounds using improved scale-space features. In: Proceedings of IEEE international symposium on signal processing and information technology, pp 1–6Google Scholar
  19. Jin F, Sattar F, Goh DYT, Loius IM (2009) A robust respiratory phase identification scheme based on a new mixing index. In: Proceedings of European signal processing conference, pp 637–641Google Scholar
  20. Jin F, Sattar F, Goh DYT (2014) New approaches for spectro-temporal feature extraction with applications to respiratory sound classification. Neurocomputing 123:362–371CrossRefGoogle Scholar
  21. Korenbaum VI, Kulakov YV, Tagiltsev AA (1997) Acoustic effects in the human respiraotry system under forced expiration. Acoust Phys 43:78–86Google Scholar
  22. Korenbaum VI, Tagiltsev AA, Kulakov JV, Kilin AS, Avdeeva HV, Pochekutova IA (1998) An acoustic model of noise production in the human bronchial tree under forced expiration. J Sound Vib 213:377–382CrossRefGoogle Scholar
  23. Kraman SS (1984) The relationship between airflow and lung sound amplitude in normal subjects. Chest 86(2):225–229CrossRefGoogle Scholar
  24. Kulkas A, Huupponen E, Virkkala J, Tenhunen M, Saastamoinen A, Rauhala E, Himanen SL (2009) Intelligent methods for identifying respiratory cycle phases from tracheal sound signal during sleep. Comput Biol Med 39(11):1000–1005CrossRefGoogle Scholar
  25. 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(3):427–438CrossRefGoogle Scholar
  26. Laënnec RTH (1819) De l’Auscultation Médiate ou Traité du Diagnostic des Maladies des Poumons et du Coeur. Brosson & Chaudé, ParisGoogle Scholar
  27. Le Cam S, Collet C, Salzenstein F (2008) Acoustical respiratory signal analysis and phase detection. In: Proceedings of IEEE international conference on acoustics, speech and signal processing, pp 3629–3632Google Scholar
  28. Lessard CS, Wong WC (1986) Correlation of constant flow rate with frequency spectrum of respiratory sounds when measured at the trachea. IEEE Trans Biomed Eng 33(4):461–463CrossRefGoogle Scholar
  29. Lyubimov GA, Skobeleva IM, Dyachenko AI, Strongin MM (2013) Estimation of tracheal sound intensity during forced expiration. Hum Physiol 39(1):106–113CrossRefGoogle Scholar
  30. Macgregor CA, Moussavi Z (2014) A novel expert classifier approach to pre-screening obstructive sleep apnea during wakefulness. In: Proceedings of IEEE international conference on engineering in medicine and biology society, pp 4236–4239Google Scholar
  31. Manecke GR Jr, Dilger JP, Kutner LJ, Poppers PJ (1997) Auscultation revisited the waveform and spectral characteristics of breath sounds during general anesthesia. Int J Clin Monit Comput 14(4):231–240CrossRefGoogle Scholar
  32. Messner E, Hagmuller M, Swatek P, Smolle-Juttner F, Pernkopf F (2017) Respiratory airflow estimation from lung sounds based on regression. In: Proceedings of IEEE international conference on acoustics, speech and signal processing, pp 1123–1127Google Scholar
  33. Mondal A, Bhattacharya P, Saha G (2014) Detection of lungs status using morphological complexities of respiratory sounds. Sci World J 182938:1–9CrossRefGoogle Scholar
  34. Mondal A, Banerjee P, Tang H (2018) A novel feature extraction technique for pulmonary sound analysis based on EMD. Comput Methods Programs Biomed 159:199–209CrossRefGoogle Scholar
  35. Morillo DS, Moreno SA, Granero MAF, Jiménez AL (2013) Computerized analysis of respiratory sounds during COPD exacerbations. Comput Biol Med 43:914–921CrossRefGoogle Scholar
  36. Moussavi ZK, Leopando MT, Pasterkamp H, Rempel G (2000) Computerised acoustical respiratory phase detection without airflow measurement. Med Biol Eng Comput 38(2):198–203CrossRefGoogle Scholar
  37. Nabi FG, Sundaraj K, Lam CK, Palaniappan R, Sundaraj S (2017) Wheeze sound analysis using computer-based techniques: a systematic review. Biomed Tech 64(1):1–28Google Scholar
  38. 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 34:820–827CrossRefGoogle Scholar
  39. Oliveira A, Marques A (2014) Respiratory sounds in healthy people: a systematic review. Respir Med 108(4):550–570CrossRefGoogle Scholar
  40. Palaniappan R, Sundaraj K, Ahamed N, Arjunan A, Sundaraj S (2013a) Computer-based respiratory sound analysis: a systematic review. IETE Tech Rev 30(3):248–256CrossRefGoogle Scholar
  41. Palaniappan R, Sundaraj K, Ahamed NU (2013b) Machine learning in lung sound analysis: a systematic review. Biocybern Biomed Eng 33(3):129–135CrossRefGoogle Scholar
  42. Palaniappan R, Sundaraj K, Sundaraj S (2014) Artificial intelligence techniques used in respiratory sound analysis—a systematic review. Biomed Tech 59(1):7–18CrossRefGoogle Scholar
  43. Palaniappan R, Sundaraj K, Nabi FG (2015) An overview of breath phase detection—techniques & applications. J Telecommun Electron Comput Eng 10(2–7):33–36Google Scholar
  44. Palaniappan R, Sundaraj K, Sundaraj S (2017) Adaptive neuro-fuzzy inference system for breath phase detection and breath cycle segmentation. Comput Methods Programs Biomed 145:67–72CrossRefGoogle Scholar
  45. Parida PK, Shanmugasundaram N, Gopalakrishnan S (2016) Clinico-radiological parameters predicting early diagnosis of foreign body aspiration in children. Turkish Journal of Ear Nose and Throat 26(5):268–275CrossRefGoogle Scholar
  46. Pasterkamp H, Carson C, Daien D, Oh Y (1989) Digital respirosonography. New images of lung sounds. Chest 96:1405–1412CrossRefGoogle Scholar
  47. Pochekutova IA, Korenbaum VI (2001) Analysis of tracheal forced exiratory noise in diagnostics of bronchial obstruction. In: Proceedings of XI session of Russian acoustical society, pp 704–707Google Scholar
  48. Pochekutova IA, Korenbaum VI (2013) Diagnosis of hidden bronchial obstruction using computer-assessed tracheal forced expiratory noise time. Respirology 18:501–506CrossRefGoogle Scholar
  49. Poreva A, Karplyuk Y, Makarenkova A, Makarenkov A (2015) Detection of COPD’s diagnostic signs based on polyspectral lung sounds analysis of respiratory phases. In: Proceedings of IEEE international conference on electronics and nanotechnology, pp 351–355Google Scholar
  50. Pramono RXA, Bowyer S, Rodriguez-Villegas E (2017) Automatic adventitious respiratory sound analysis: a systematic review. PLoS ONE 12(5):e0177926CrossRefGoogle Scholar
  51. Reyes BA, Charleston-Villalobos S, González-Camarena R, Aljama-Corrales T (2014a) Assessment of time-frequency representation techniques for thoracic sounds analysis. Comput Methods Programs Biomed 114:276–290CrossRefGoogle Scholar
  52. Reyes BA, Reljin N, Chon KH (2014b) Tracheal sounds acquisition using smartphones. Sensors 14(8):13830–13850CrossRefGoogle Scholar
  53. Saarinen A, Rihkanen H, Malmberg LP, Pekkanen L, Sovijärvi ARA (2001) Tracheal sounds and airflow dynamics in surgically treated unilateral vocal fold paralysis. Clin Physiol 21(2):223–228CrossRefGoogle Scholar
  54. Saha S, Bradley TD, Taheri M, Moussavi Z, Yadollahi A (2016) A subject-specific acoustic model of the upper airway for snoring sounds generation. Sci Rep 6:25730CrossRefGoogle Scholar
  55. Sanchez I, Avital A, Wong I, Tal A, Pasterkamp H (1993a) Acoustic vs. spirometric assessment of bronchial responsiveness to methacholine in children. Pediatr Pulmonol 15(1):28–35CrossRefGoogle Scholar
  56. Sanchez I, Powell RE, Pasterkamp H (1993b) Wheezing and airflow obstruction during methacholine challenge in children with cystic fibrosis and in normal children. Am Rev Respir Dis 147(3):705–709CrossRefGoogle Scholar
  57. Sarkar M, Madabhavi I, Niranjan N, Dogra M (2015) Auscultation of the respiratory system. Ann Thorac Med 10(3):158–168CrossRefGoogle Scholar
  58. Schreur HJW, 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 153:1474–1480CrossRefGoogle Scholar
  59. Schudt F, Gross V, Weissflog A, Mursina L, Koehler U, Sohrabi K (2014) Estimation of respiratory flow by means of normal lung sound. Stud Health Technol Inform 198:232–237Google Scholar
  60. Shykoff BE, Ploysongsang Y, Chang HK (1988) Airflow and normal lung sounds. Am Rev Respir Dis 137(4):872–876CrossRefGoogle Scholar
  61. Sohrabi KA, Basu D, Schudt F, Scholtes M, Seifert O, Koehler U, Gross V (2012) Quantification of nasal respiratory flow by tracheal sound analysis. Biomed Tech 57:733–735Google Scholar
  62. Soufflet G, Charbonneau G, Polit M, Attal P, Denjean A, Escourrou P, Gaultier C (1990) Interaction between tracheal sound and flow rate: a comparison of some different flow evaluations from lung sounds. IEEE Trans Biomed Eng 37(4):384–391CrossRefGoogle Scholar
  63. Sovijärvi ARA, Malmberg LP, Paajanen E, Pürilä P, Kallio K, Katila T (1996) Averaged and time-gated spectral analysis of respiratory sounds: repeatability of spectral parameters in healthy men and in patients with fibrosing alveolitis. Chest 109(5):1283–1290CrossRefGoogle Scholar
  64. Sovijärvi ARA, Vanderschoot J, Earis JE (2000) Standardization of computerized respiratory sound analysis current methods used for computerized respiratory sound analysis. Eur Respir Rev 10(77):585Google Scholar
  65. Yadollahi A, Moussavi ZMK (2006) A robust method for estimating respiratory flow using tracheal sounds entropy. IEEE Trans Biomed Eng 53(4):662–668CrossRefGoogle Scholar
  66. Yadollahi A, Moussavi Z (2008) Comparison of flow-sound relationship for different features of tracheal sound. In: Proceedings of IEEE conference on engineering in medicine and biology society, pp 805–808Google Scholar
  67. Yadollahi A, Moussavi Z (2009) On arithmetic misconceptions of spectral analysis of biological signals, in particular respiratory sounds. In: Proceedings of IEEE conference on engineering in medicine and biology society, pp 388–391Google Scholar
  68. Yadollahi A, Moussavi ZMK (2011) The effect of anthropometric variations on acoustical flow estimation: proposing a novel approach for flow estimation without the need for individual calibration. IEEE Trans Biomed Eng 58(6):1663–1670CrossRefGoogle Scholar
  69. Yadollahi A, Giannouli E, Moussavi Z (2010) Sleep apnea monitoring and diagnosis based on pulse oximetery and tracheal sound signals. Med Biol Eng Comput 48(11):1087–1097CrossRefGoogle Scholar
  70. 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(3):537–546CrossRefGoogle Scholar
  71. Yadollahi A, Rudzicz F, Mahallati S, Coimbra M, Bradley TD (2014) Acoustic estimation of neck fluid volume. Ann Biomed Eng 42:2132–2142CrossRefGoogle Scholar
  72. Yap YL, Moussavi Z (2001) Respiratory onset detection using variance fractal dimension. In: Proceedings of IEEE international conference on engineering in medicine and biology society, pp 1554–1556Google Scholar
  73. Yap YL, Moussavi Z (2002) Acoustic airflow estimation from tracheal sound power. In: Proceedings of IEEE Canadian conference on electrical and computer engineering, pp 1073–1076Google Scholar
  74. Yildirim I, Ansari R, Moussavi Z (2008) Automated respiratory phase and onset detection using only chest sound signal. In: Proceedings of IEEE international conference on engineering in medicine and biology society, pp 2578–2581Google Scholar
  75. Yu L, Ting CK, Hill BE, Orr JA, Brewer LM, Johnson KB, Egan TD, Westenskow DR (2013) Using the entropy of tracheal sounds to detect apnea during sedation in healthy nonobese volunteers. Anesthesiology 118:1341–1349CrossRefGoogle Scholar
  76. Yuasa Y, Suzuki K (2019) Wearable device for monitoring respiratory phases based on breathing sound and chest movement. Adv Biomed Eng 8:85–91CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Priya Devi Muthusamy
    • 1
    Email author
  • Kenneth Sundaraj
    • 1
  • Nurulfajar Abd Manap
    • 1
  1. 1.Centre for Telecommunication Research & Innovation, Fakulti Kejuruteraan Elektronik & Kejuruteraan KomputerUniversiti Teknikal Malaysia MelakaDurian TunggalMalaysia

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