Skip to main content

Current Techniques for Breath Sound Analysis

  • Chapter
  • First Online:

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. McKusick VS (1958) Cardiovascular sound in health and disease. Williams & Wilkins, Baltimore, MD

    Google Scholar 

  2. Rapoport J (1986) Laennec and the discovery of auscultation. Israel J Med 22:597–601

    CAS  Google Scholar 

  3. Foucault M (1973) The birth of the clinic: an archaeology of medical perception (A. M. Sheridan Smith, Trans.). Pantheon Books, New York, NY

    Google Scholar 

  4. Sterne J (2001) Mediate auscultation, the stethoscope, and the ‘autopsy of the living’: medicine’s acoustic culture. J Med Humanities 22(2):115–136

    Article  Google Scholar 

  5. Davis AB (1981) Medicine and its technology: an introduction to the history of medical instrumentation. Greenwood Press, Westport, CT, pp 88–89

    Google Scholar 

  6. Hadjileontiadis LJ (2009a) Lung sounds: an advanced signal processing perspective (Volume 9 of Synthesis lectures on biomedical engineering). Williston, VT: Morgan & Claypool Publishers

    Google Scholar 

  7. Crary J (1990) Techniques of the observer: on vision and modernity in the nineteenth century. MIT Press, Cambridge, MA, pp 89–90

    Google Scholar 

  8. Kraman SS (1985) Vesicular (normal) lung sounds: how are they made, where do they come from and what do they mean? Semin Respir Med 6:183–191

    Article  Google Scholar 

  9. Thacker RE, Kraman SS (1982) The prevalence of auscultatory crackles in subjects without lung disease. Chest 81(6):672–674

    Article  CAS  PubMed  Google Scholar 

  10. Workum P, Holford SK, Delbono EA, Murphy RLH (1982) The prevalence and character of crackles (rales) in young women without significant lung disease. Am Rev Respir Dis 126(5):921–923

    CAS  PubMed  Google Scholar 

  11. Robertson AJ (1957) Rales, rhonchi, and Laennec. Lancet 1:417–423

    Article  Google Scholar 

  12. Bohadana A, Izbicki G, Kraman SS (2014) Fundamentals of lung auscultation. N Engl J Med 370(8):744–751

    Article  CAS  PubMed  Google Scholar 

  13. Cugell DW (1987) Lung sound nomenclature. Am Rev Respir Dis 136:1016

    Article  CAS  PubMed  Google Scholar 

  14. Kraman SS (1983) Lung sounds: an introduction to the interpretation of auscultatory findings (workbook). American College of Chest Physicians, Northbrook, IL

    Google Scholar 

  15. Murphy RLH (1985) Discontinuous adventitious lung sounds. Semin Respir Med 6:210–219

    Article  Google Scholar 

  16. Sovijarvi ARA, Dalmasso F, Vanderschoot J, Malmberg LP, Righini G, Stoneman SAT (2000) Definition of terms for applications of respiratory sounds. Eur Respir Rev 10:597–610

    Google Scholar 

  17. Earis JE, Marsh K, Rearson MG, Ogilvie CM (1982) The inspiratory squawk in extrinsic allergic alveolitis and other pulmonary fibroses. Thorax 37(12):923–936

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Gavriely N, Cugell DW (1995) Breath sounds methodology. CRC Press, Boca Raton, FL, p 2

    Google Scholar 

  19. Gavriely N, Palti Y, Alroy G (1981) Spectral characteristics of normal breath sounds. J Appl Physiol 50:307–314

    Article  CAS  PubMed  Google Scholar 

  20. Katila T, Piirila P, Kallio K, Paajanen E, Rosqvist T, Sovijarvi ARA (1991) Original waveform of lung sound crackles: a case study of the effect of high-pass filtration. J Appl Physiol 71(6):2173–2177

    Article  CAS  PubMed  Google Scholar 

  21. Murphy RLH, Holford SK, Knowler WC (1978) Visual lung-sound characterization by time-expanded wave-form analysis. N Engl J Med 296:968–971

    Google Scholar 

  22. Holmes MS, Seheult JN, Geraghty C, D’Arcy S, O’Brien U, O’Connell GC et al (2013) A method of estimating inspiratory flow rate and volume from an inhaler using acoustic measurements. Physiol Meas 34(8):903–914

    Article  PubMed  Google Scholar 

  23. Huq S, Moussavi Z (2012) Acoustic breath-phase detection using tracheal breath sounds. Med Biol Eng Comput 50(3):297–308

    Article  PubMed  Google Scholar 

  24. Moussavi Z, Leopando MT, Pasterkamp H, Rempel G (2000) Computerized acoustical respiratory phase detection without airflow measurement. Med Biol Eng Comput 38(2):198–203

    Article  CAS  PubMed  Google Scholar 

  25. Nam Y, Reyes BA, Chon KH (2016) Estimation of respiratory rates using the built-in microphone of a smartphone or headset. IEEE J Biomed Health Informatics 20(6):1493–1501

    Article  Google Scholar 

  26. Palaniappan R, Sundaraj K, Sundaraj S (2017) Adaptive neuro-fuzzy inference system for breath phase detection and breath cycle segmentation. Comput Methods Prog Biomed 145:67–72

    Article  Google Scholar 

  27. Yadollahi A, Moussavi Z (2006) A robust method for estimating respiratory flow using tracheal sound entropy. IEEE Trans Biomed Eng 53(4):662–668

    Article  PubMed  Google Scholar 

  28. Yadollahi A, Moussavi ZM (2007) Acoustical respiratory flow. IEEE Eng Med Biol Mag 26(1):56–61

    Article  PubMed  Google Scholar 

  29. 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):537–546

    Article  PubMed  Google Scholar 

  30. Ahlstrom C, Liljefeldt O, Hult P, Ask P (2005) Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction. IEEE Signal Process Lett 12(12):812–815

    Article  Google Scholar 

  31. Falk TH, Chan WY (2008) Modulation filtering for heart and lung sound separation from breath sound recordings. In: 30th Annual international conference of the IEEE engineering in medicine and biology society, EMBS 2008, pp 1859–1862

    Google Scholar 

  32. Floras D, Moussavi Z, Thomas G (2007) Heart sound cancellation based on multiscale product and linear prediction. IEEE Trans Biomed Eng 54(2):234–243

    Article  Google Scholar 

  33. Ghaderi F, Mohseni HR, Sanei S (2011) Localizing heart sounds in respiratory signals using singular spectrum analysis. IEEE Trans Biomed Eng 58(12):3360–3367

    Article  PubMed  Google Scholar 

  34. Gnitecki J, Hossain I, Moussavi Z, Pasterkamp H (2005a) Qualitative and quantitative evaluation of heart sound reduction from lung sound recordings. IEEE Trans Biomed Eng 52(10):1788–1792

    Article  PubMed  Google Scholar 

  35. Hadjileontiadis LJ, Panas SM (1997c) Adaptive reduction of heart sounds from lung sounds using fourth-order statistics. IEEE Trans Biomed Eng 44(7):642–648

    Article  CAS  PubMed  Google Scholar 

  36. Hadjileontiadis LJ, Panas SM (1998a) A wavelet-based reduction of heart sound noise from lung sounds. Int J Med Inform 52:183–190

    Article  CAS  PubMed  Google Scholar 

  37. Hossain I, Moussavi Z (2003) An overview of heart-noise reduction of lung sound using wavelet transform based filter. In: Proc. 25th IEEE Eng. Med. Biol. Soc. (EMBS), pp 458–461

    Google Scholar 

  38. Iyer VK, Ramamoorthy PA, Fan H, Ploysongsang Y (1986) Reduction of heart sounds from lung sounds by adaptive filtering. IEEE Trans Biomed Eng 33(12):1141–1148

    Article  CAS  PubMed  Google Scholar 

  39. Li T, Tang H, Qiu T, Park Y (2013) Heart sound cancellation from lung sound record using cyclostationarity. Med Eng Phys 35(12):1831–1836

    Article  PubMed  Google Scholar 

  40. Mondal A, Banerjee P, Somkuwar A (2017) Enhancement of lung sounds based on empirical mode decomposition and Fourier transform algorithm. Comput Methods Prog Biomed 139:119–136

    Article  Google Scholar 

  41. Nersisson R, Noel MM (2017) Heart sound and lung sound separation algorithms: a review. J Med Eng Technol 41(1):13–21

    Article  PubMed  Google Scholar 

  42. Pourazad MT, Moussavi Z, Thomas G (2006) Heart sound cancellation from lung sound recording using time-frequency filtering. J Med Biol Eng 44(3):216–225

    CAS  Google Scholar 

  43. Sathesh K, Muniraj NJR (2014) Real time heart and lung sound separation using adaptive line enhancer with NLMS. J Theor Appl Inf Technol 65(2):559–564

    Google Scholar 

  44. Tsalaile T, Naqvi SM, Nazarpour K, Sanei S, Chambers JA (2008) Blind source extraction of heart sound signals from lung sound recordings exploiting periodicity of the heart sound. In: IEEE international conference on acoustics, speech and signal processing, ICASSP, pp 461–464

    Google Scholar 

  45. Zivanovic M, González-Izal M (2013) Quasi-periodic modeling for heart sound localization and suppression in lung sounds. Biomed Signal Process Control 8(6):586–595

    Article  Google Scholar 

  46. Emmanouilidou D, McCollum ED, Park DE, Elhilali M (2017) Computerized lung sound screening for pediatric auscultation in noisy field environments. IEEE Trans Biomed Eng. https://doi.org/10.1109/TBME.2017.2717280

  47. Grønnesby M, Solis JCA, Holsbø E, Melbye H, Bongo LA (2017) Machine learning based crackle detection in lung sounds. arXiv preprint arXiv:1706.00005

    Google Scholar 

  48. Hadjileontiadis LJ, Panas SM (1996) Nonlinear separation of crackles and squawks from vesicular sounds using third-order statistics. In: Proc. IEEE 18th EMBS Conf. (EMBS), vol 5, pp 2217–2219

    Google Scholar 

  49. Hadjileontiadis LJ, Panas SM (1997d) Separation of discontinuous adventitious sounds from vesicular sounds using a wavelet-based filter. IEEE Trans Biomed Eng 44(12):1269–1281

    Article  CAS  PubMed  Google Scholar 

  50. Hadjileontiadis LJ, Patakas DA, Margaris NJ, Panas SM (1998) Separation of crackles and squawks from vesicular sounds using a wavelet-based filtering technique. COMPEL 17(5/6):649–657

    Article  Google Scholar 

  51. Hadjileontiadis LJ, Panas SM (1998c) Enhanced separation of crackles and squawks from vesicular sounds using nonlinear filtering with third-order statistics. J Tennessee Acad Sci 73(1–2):47–52

    Google Scholar 

  52. Hadjileontiadis LJ, Tolias YA, Panas SM (2002) Intelligent system modeling of bioacoustic signals using advanced signal processing techniques. In: Leondes CT (ed) Intelligent systems: technology and applications, vol 3. CRC Press, Boca Raton, FL, pp 103–156

    Google Scholar 

  53. Hadjileontiadis LJ, Rekanos IT (2003) Detection of explosive lung and bowel sounds by means of fractal dimension. IEEE Signal Process Lett 10(10):311–314

    Article  Google Scholar 

  54. Hadjileontiadis LJ (2005a) Wavelet-based enhancement of lung and bowel sounds using fractal dimension thresholding-Part I: Methodology. IEEE Trans Biomed Eng 52(6):1143–1148

    Article  PubMed  Google Scholar 

  55. Hadjileontiadis LJ (2005b) Wavelet-based enhancement of lung and bowel sounds using fractal dimension thresholding—Part II: Application results. IEEE Trans Biomed Eng 52(6):1050–1064

    Article  PubMed  Google Scholar 

  56. Hadjileontiadis LJ (2007) Empirical mode decomposition and fractal dimension filter: a novel technique for denoising explosive lung sounds. IEEE Eng Med Biol Mag 26(1):30–39

    Article  PubMed  Google Scholar 

  57. Jin F, Krishnan S, Sattar F (2011) Adventitious sounds identification and extraction using temporal–spectral dominance-based features. IEEE Trans Biomed Eng 58(11):3078–3087

    Article  PubMed  Google Scholar 

  58. Li Z, Wu X (2013) Pulmonary crackle detection based on fractional Hilbert Transform. In: World Congress on medical physics and biomedical engineering, May 26–31, 2012, Beijing, China, pp 578–580

    Google Scholar 

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

    Article  Google Scholar 

  60. Maruf SO, Azhar MU, Khawaja SG, Akram MU (2015) Crackle separation and classification from normal Respiratory sounds using Gaussian Mixture Model. In: IEEE 10th international conference on industrial and information systems (ICIIS), pp 267–271

    Google Scholar 

  61. Mastorocostas PM, Tolias YA, Theocharis JB, Hadjileontiadis LJ, Panas SM (1997) An orthogonal least squares-based fuzzy filter for real time analysis of lung sounds. IEEE Trans Biomed Eng 47(9):1165–1176

    Article  Google Scholar 

  62. Ono M, Arakawa K, Mori M, Sugimoto T, Harashima H (1989) Separation of fine crackles from vesicular sounds by a nonlinear digital filter. IEEE Trans Biomed Eng 36(2):286–291

    Article  CAS  PubMed  Google Scholar 

  63. Pinho C, Oliveira A, Jácome C, Rodrigues JM, Marques A (2016) Integrated approach for automatic crackle detection based on fractal dimension and box filtering. Int J Reliable Qual E-Healthcare 5(4):34–50

    Article  Google Scholar 

  64. Rekanos IT, Hadjileontiadis LJ (2006) An iterative kurtosis-based technique for the detection of nonstationary bioacoustic signals. Signal Process 86:3787–3795

    Article  Google Scholar 

  65. Serbes G, Sakar CO, Kahya YP, Aydin N (2013) Pulmonary crackle detection using time–frequency and time–scale analysis. Digital Signal Process 23(3):1012–1021

    Article  Google Scholar 

  66. Tolias YA, Hadjileontiadis LJ, Panas SM (1997) A fuzzy rule-based system for real-time separation of crackles from vesicular sounds. In: Proc. 19th IEEE Eng. Med. Biol. Soc. (EMBS), pp 1115–1118

    Google Scholar 

  67. Tolias YA, Hadjileontiadis LJ, Panas SM (1998) Real-time separation of discontinuous adventitious sounds from vesicular sounds using a fuzzy rule-based filter. IEEE Trans Inf Technol Biomed 2(3):204–215

    Article  CAS  PubMed  Google Scholar 

  68. Zhang K, Wang X, Han F, Zhao H (2015) The detection of crackles based on mathematical morphology in spectrogram analysis. Technol Health Care 23(s2):S489–S494

    Article  PubMed  Google Scholar 

  69. Ahlstrom C, Johansson A, Hult P, Ask P (2006) Chaotic dynamics of respiratory sounds. J Chaos Soliton Fractals 29:1054–1069

    Article  Google Scholar 

  70. Charleston-Villalobos S, Albuerne-Sanchez L, Gonzalez-Camarena R, Mejia-Avila M, Carrillo-Rodriguez G, Aljama-Corrales T (2013) Linear and nonlinear analysis of base lung sound in extrinsic allergic alveolitis patients in comparison to healthy subjects. Methods Inf Med 52(3):266–276

    Article  CAS  PubMed  Google Scholar 

  71. Conte E, Vena A, Federici A, Giuliani R, Zbilut JP (2004) A brief note on possible detection of physiological singularities in respiratory dynamics by recurrence quantification analysis of lung sounds. J Chaos Soliton Fractals 21:869–877

    Article  Google Scholar 

  72. Gnitecki J, Moussavi Z, Pasterkamp H (2004) Classification of lung sounds during bronchial provocation using waveform fractal dimensions. In: Proc. 26th IEEE Eng. Med. Biol. Soc. (EMBS), pp 3844–3847

    Google Scholar 

  73. Gnitecki J, Moussavi Z (2005) The fractality of lung sounds: a comparison of three waveform fractal dimension algorithms. J. Chaos Soliton Fractals 26(4):1065–1072

    Article  Google Scholar 

  74. Gnitecki J, Moussavi Z, Pasterkamp H (2005b) Geometrical and dynamical state space parameters of lung sounds. In 5th international workshop on biosignal interpretation (BSI), pp 113–116

    Google Scholar 

  75. Hadjileontiadis LJ, Panas SM (1997a) Autoregressive modeling of lung sounds using higher-order statistics: estimation of source and transmission. In: Proc. IEEE signal processing workshop on higher-order statistics (SPW-HOS), pp 4–8

    Google Scholar 

  76. Hadjileontiadis LJ, Panas SM (1997b) Nonlinear analysis of musical lung sounds using the bicoherence index. In: Proc. 19th IEEE Eng. Med. Biol. Soc. (EMBS), pp 1126–1129

    Google Scholar 

  77. Reyes BA, Charleston-Villalobos S, Gonzalez-Camarena R, Aljama-Corrales T (2008) Analysis of discontinuous adventitious lung sounds by Hilbert-Huang spectrum. In 30th Annual international conference of the IEEE, Engineering in Medicine and Biology Society, 2008, EMBS 2008, pp 3620–3623

    Google Scholar 

  78. Taplidou SA, Hadjileontiadis LJ (2007) Nonlinear analysis of wheezes using wavelet bicoherence. Comput Biol Med 37(4):563–570

    Article  PubMed  Google Scholar 

  79. Taplidou SA, Hadjileontiadis LJ (2010) Analysis of wheezes using wavelet higher order spectral features. IEEE Trans Biomed Eng 57(7):1596–1610

    Article  PubMed  Google Scholar 

  80. Bahoura M (2009) Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes. Comput Biol Med 39(9):824–843

    Article  PubMed  Google Scholar 

  81. Chamberlain D, Kodgule R, Ganelin D, Miglani V, Fletcher RR (2016) Application of semi-supervised deep learning to lung sound analysis. In: IEEE 38th annual international conference of the engineering in medicine and biology society (EMBC), pp 804–807)

    Google Scholar 

  82. Charleston-Villalobos S, Martinez-Hernandez G, Gonzalez-Camarena R, Chi-Lem G, Carrillo JG, Aljama-Corrales T (2011) Assessment of multichannel lung sounds parameterization for two-class classification in interstitial lung disease patients. Comput Biol Med 41(7):473–482

    Article  CAS  PubMed  Google Scholar 

  83. Cohen A (1990) Signal processing methods for upper airway and pulmonary dysfunction diagnosis. IEEE Eng Med Biol Mag 9(1):72–75

    Article  CAS  PubMed  Google Scholar 

  84. Dokur Z (2009) Respiratory sound classification by using an incremental supervised neural network. Pattern Anal Applic 12(4):309–319

    Article  Google Scholar 

  85. Göğüş FZ, Karlik B, Harman G (2015) Classification of asthmatic breath sounds by using wavelet transforms and neural networks. Int J Signal Process Syst 3:106–111

    Google Scholar 

  86. Hadjileontiadis LJ, Panas SM (1998b) On modeling impulsive bioacoustic signals with symmetric alpha-stable distributions: application in discontinuous adventitious lung sounds and explosive bowel sounds. In: Proc. 20th IEEE Eng. Med. Biol. Soc. (EMBS), vol 1, pp 13–16

    Google Scholar 

  87. Hadjileontiadis LJ (2003) Discrimination analysis of discontinuous breath sounds using higher-order crossings. Med Biol Eng Comput 41(4):445–455

    Article  CAS  PubMed  Google Scholar 

  88. Hadjileontiadis LJ (2009b) A texture-based classification of crackles and squawks using lacunarity. IEEE Trans Biomed Eng 56(3):718–732

    Article  PubMed  Google Scholar 

  89. Hoevers J, Loudon RG (1990) Measuring crackles. Chest 98(5):1240–1243

    Article  CAS  PubMed  Google Scholar 

  90. İçer S, Gengeç Ş (2014) Classification and analysis of non-stationary characteristics of crackle and rhonchus lung adventitious sounds. Digital Signal Process 28:18–27

    Article  Google Scholar 

  91. Jin F, Sattar F, Goh DY (2014) New approaches for spectro-temporal feature extraction with applications to respiratory sound classification. Neurocomputing 123:362–371

    Article  Google Scholar 

  92. Kahya YP, Yilmaz CA (2000) Modeling of respiratory crackles. In: Proc. 22nd IEEE Eng. Med. Biol. Soc. (EMBS), vol 1, pp 632–634

    Google Scholar 

  93. Kahya YP, Yeginer M, Bilgic B (2006) Classifying respiratory sounds with different feature sets. In: EMBS‘06. 28th Annual international conference of the IEEE, Engineering in Medicine and Biology Society, 2006, pp 2856–2859

    Google Scholar 

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

    Article  CAS  Google Scholar 

  95. Matsunaga S, Yamauchi K, Yamashita M, Miyahara S (2009). Classification between normal and abnormal respiratory sounds based on maximum likelihood approach. In: IEEE international conference on acoustics, speech and signal processing (ICASSP 2009), pp 517–520

    Google Scholar 

  96. Munakata M, Ukita H, Doi I, Ohtsuka Y, Masaki Y, Homma Y, Kawakami Y (1991) Spectral and waveform characteristics of fine and coarse crackles. Thorax 46(9):651–657

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Naves R, Barbosa BH, Ferreira DD (2016) Classification of lung sounds using higher-order statistics: a divide-and-conquer approach. Comput Methods Prog Biomed 129:12–20

    Article  Google Scholar 

  98. Oweis RJ, Abdulhay EW, Khayal A, Awad A (2015) An alternative respiratory sounds classification system utilizing artificial neural networks. Biomed J 38:153–161

    Article  PubMed  Google Scholar 

  99. Palaniappan R, Sundaraj K, Sundaraj S, Huliraj N, Revadi SS, Archana B (2014) Pulmonary acoustic signal classification using autoregressive coefficients and k-nearest neighbor. Appl Mech Mater 591:211–214

    Article  Google Scholar 

  100. Pesu L, Helistö P, Ademovic E, Pesquet JC, Saarinen A, Sovijarvi AR (1998) Classification of respiratory sounds based on wavelet packet decomposition and learning vector quantization. Technol Health Care 6(1):65–74

    CAS  PubMed  Google Scholar 

  101. Sankur B, Kahya YR, Güler EC, Engin TS (1994) Comparison of AR-based algorithms for respiratory sounds classification respiratory disease diagnosis using lung sounds. Comput Biol Med 24(1):67–76

    Article  CAS  PubMed  Google Scholar 

  102. Sosa GD, Cruz-Roa A, González FA (2015) Automatic detection of wheezes by evaluation of multiple acoustic feature extraction methods and C-weighted SVM. In: 10th International symposium on medical information processing and analysis, International Society for Optics and Photonics, pp 928709–928709

    Google Scholar 

  103. Taketoshi O, Hayaru S, Shoji K (2006) Discrimination of lung sounds using a statistics of waveform intervals. IPSJ SIG Technical Reports, 2006(68(MPS-60)), pp 1–4

    Google Scholar 

  104. Ulukaya S, Serbes G, Sen I, Kahya YP (2016) A lung sound classification system based on the rational dilation wavelet transform. In: IEEE 38th annual international conference of the engineering in medicine and biology society (EMBC), pp 3745–3748

    Google Scholar 

  105. Xie S, Jin F, Krishnan S, Sattar F (2012) Signal feature extraction by multi-scale PCA and its application to respiratory sound classification. Med Biol Eng Comput 50(7):759–768

    Article  Google Scholar 

  106. Yilmaz CA, Kahya YP (2005) Modeling of pulmonary crackles using wavelet networks. In: Proc. 27th IEEE Eng. Med. Biol. Soc. (EMBS), pp 7560–7563

    Google Scholar 

  107. Yilmaz CA, Kahya YP (2006) Multi-channel classification of respiratory sounds. In: Proc. 28th IEEE Eng. Med. Biol. Soc. (EMBS), vol 1, pp 2864–2867

    Google Scholar 

  108. Murphy RLH, Holford SK, Knowler WC (1977) Visual lung sound characterization by time-expanded waveform analysis. New Eng J Med 296:968–971

    Article  PubMed  Google Scholar 

  109. Palaniappan R, Sundaraj K, Ahamed NU (2013) Machine learning in lung sound analysis: a systematic review. Biocybernetics Biomed Eng 33(3):129–135

    Article  Google Scholar 

  110. Mendel JM (1991) Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications. Proc IEEE 79(3):278–305

    Article  Google Scholar 

  111. Nikias CL, Petropulu AP (1993) Higher-order spectra analysis: a nonlinear signal processing framework. Prentice-Hall, Englewood Cliffs, NJ

    Google Scholar 

  112. Huber PJ, Kleiner B, Gasser T, Dumermuth G (1971) Statistical methods for investigating phase relations in stationary stochastic processes. IEEE Trans Audio Electracoust 19:78–86

    Article  Google Scholar 

  113. Iyer VK, Ramamoorthy PA, Ploysongsang Y (1989) Autoregressive modeling of lung sounds: characterization of source and transmission. IEEE Trans Biomed Eng 36(11):1133–1137

    Article  CAS  PubMed  Google Scholar 

  114. Raghuveer MR, Nikias CL (1985) Bispectrum estimation: a parametric approach. IEEE Trans Acoustics Speech Signal Process 33(4):1213–1230

    Article  Google Scholar 

  115. Nikias CL, Raghuveer MR (1987) Bispectrum estimation: a digital signal processing framework. Proc IEEE 75(7):869–891

    Article  Google Scholar 

  116. Gavriely N, Palti Y, and Alroy, G “Spectral characteristics of normal breath sounds,” J. Appl. Physiol., Vol.50, pp. 307–314, 1981

    Article  CAS  PubMed  Google Scholar 

  117. Hadjileontiadis LJ (1997) Analysis and processing of lung sounds using higher-order statistics-spectra and wavelet transform. PhD dissertation, Aristotle University of Thessaloniki, Thessaloniki, Greece, pp 139–175

    Google Scholar 

  118. Li S, Liu Y (2010) Feature extraction of lung sounds based on bispectrum analysis. In: Third international symposium on information processing (ISIP), pp 393–397

    Google Scholar 

  119. Grossmann A, Morlet J (1984) Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM J Math Anal 15:723–736

    Article  Google Scholar 

  120. Addison PS (2017) The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance. CRC press, Cleveland, OΗ

    Google Scholar 

  121. Astafieva NM (1996) Wavelet analysis: basic theory and some applications. Physics-Uspekhi 39(11):1085–1108

    Article  Google Scholar 

  122. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Machine Intell 11(7):674–693

    Article  Google Scholar 

  123. Daubechies I (1988) Orthonormal bases of compactly supported wavelets. Commun Pure Appl Math 41:909–996

    Article  Google Scholar 

  124. Daubechies I (1991) Ten lectures on wavelets, CBMS lecture notes series. SIAM, Philadelphia, PA

    Google Scholar 

  125. Mallat SG (1996) Special issue on wavelets. Proc IEEE 84(4):507–686

    Article  Google Scholar 

  126. Mallat SG (1998) A wavelet tour of signal processing. Academic, San Diego

    Google Scholar 

  127. Vetterli M, Kovačević J (1995) Wavelets and subband coding, ch. 4. Prentice-Hall, Englewood Cliffs, NJ, pp 201–298

    Google Scholar 

  128. Gross V, Penzel T, Hadjileontiadis LJ, Koehler U, Vogelmeier C (2002) Electronic auscultation based on wavelet transformation in clinical use. In: Proc. 24th IEEE Eng. Med. Biol. Soc. (EMBS), pp 1531–1532)

    Google Scholar 

  129. Gross V, Penzel T, Fachinger P, Fröhlich M, Sulzer J, von Wichert P (1999) A simple method for detecting pneumonia with using wavelet-transformation. In: Proc. Med. and Biol. Eng. and Comp. (EMBEC), vol 37, Suppl 2, pp 536–537

    Google Scholar 

  130. Ayari F, Alouani AT, Ksouri M (2008) Wavelets: an efficient tool for lung sounds analysis. Proc. IEEE computer systems and applications (AICCSA):875–878

    Google Scholar 

  131. Ke L, Houjun W (2007) A novel wavelet transform modulus maxima based method of measuring Lipschitz exponent. In: Proc. International conference communications, circuits and systems (ICCCAS), pp 628–632

    Google Scholar 

  132. Mallat SG, Hwang WL (1992) Singularity detection and processing with wavelets. IEEE Trans Inf Theory 38(2):617–643

    Article  Google Scholar 

  133. Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Ruspini H (ed) Proc. IEEE international conference on neural networks (ICNN), pp 586–591

    Google Scholar 

  134. Bayram I, Selesnick IW (2009) Frequency-domain design of overcomplete rational-dilation wavelet transforms. IEEE Trans Signal Process 57(8):2957–2972

    Article  Google Scholar 

  135. Birkelund Y, Hanssen A (1999) Multitaper estimators for bispectra. In: Proc. IEEE SP workshop on higher-order (SPW-HOS), pp 207–213

    Google Scholar 

  136. Dudok de Wit T, Krasnosel’Skikh VV (1995) Wavelet bicoherence analysis of strong plasma turbulence at the earth’s quasiparallel bow shock. Phys Plasmas 2(11):4307–4311

    Article  CAS  Google Scholar 

  137. Larsen Y, Hanssen A (2000) Wavelet-polyspectra: analysis of non-stationary and non-Gaussian/non-linear signals. In: Proc. IEEE workshop on statistical signal and array processing (WSSAP), pp 539–543

    Google Scholar 

  138. van Milligen BP, Sánchez E, Estrada T, Hidalgo C, Braňas B, Carreras B, Garcia L (1995a) Wavelet bicoherence: a new turbulence analysis tool. Phys Plasmas 2(8):3017–3032

    Article  Google Scholar 

  139. van Milligen BP, Hidalgo C, Sánchez E (1995b) Nonlinear phenomena and intermittency in plasma turbulence. Phys Rev Lett 74(3):395–398

    Article  PubMed  Google Scholar 

  140. Duke JR Jr, Good JT Jr, Hudson LD, Hyers TM, Iseman MD, Mergenthaler DD, Murray JF, Petty TL, Rollins DR (2000) Frontline assessment of common pulmonary presentations. In: Murray JF, Hudson LD, Petty TL (eds) A monograph for primary care physicians. Snowdrift Pulmonary Conference, Inc, Denver, CO. http://www.lungcancerfrontiers.org/pdf-books/asmnt_cmn_pulmryPrsntn.pdf

    Google Scholar 

  141. Huang NE, Shen Z, Long SR, Wu ML, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc R Soc Lond A 454(1971):903–995

    Article  Google Scholar 

  142. Gloersen P, Huang NE (2003) Comparison of interannual intrinsic modes in hemispheric sea ice covers and other geophysical parameters. IEEE Trans GeosciRemote Sens 41(5):1062–1074

    Article  Google Scholar 

  143. Wu Z, Huang NE (2004) A study of the characteristics of white noise using the empirical mode decomposition method. Proc Roy Soc London 460A:1597–1611

    Article  Google Scholar 

  144. Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1(01):1–41

    Article  Google Scholar 

  145. Yeh JR, Lin TY, Shieh JS, Chen Y, Huang NE, Wu Z, Peng CK (2008) Investigating complex patterns of blocked intestinal artery blood pressure signals by empirical mode decomposition and linguistic analysis. J Phys Conf Ser 96:1–7. https://doi.org/10.1088/1742-6596/96/1/012153

    Article  Google Scholar 

  146. Villalobos SC, Camarena RG, Lem GC, Corrales TA (2007) Crackle sounds analysis by empirical mode decomposition: nonlinear and nonstationary signal analysis for distinction of crackles in lung sounds. IEEE Eng Med Biol Mag 26(15):40–47

    Article  Google Scholar 

  147. Lozano M, Fiz JA, Jané R (2016) Automatic differentiation of normal and continuous adventitious respiratory sounds using ensemble empirical mode decomposition and instantaneous frequency. IEEE J Biomed Health Informatics 20(2):486–497

    Article  Google Scholar 

  148. Nikias CL, Shao M (1995) Signal Processing with Alpha-Stable Distributions and Applications. Wiley & Sons, Inc., USA, New York

    Google Scholar 

  149. Hadjileontiadis LJ, Giannakidis AJ, Panas SM (2000) a-Stable modeling: a novel tool for classifying crackles and artifacts. In: Pasterkamp H (ed) Proc. 25th international lung sounds association conference (ILSA), Chicago, IL

    Google Scholar 

  150. Kedem B (1994) Time series analysis by higher-order crossings. IEEE Press, Piscataway, NJ

    Google Scholar 

  151. Rasband SN (1997) Fractal dimension, Ch. 4. In: Chaotic dynamics of nonlinear systems. Wiley-Interscience, New York, pp 71–83

    Google Scholar 

  152. Esteller R, Vachtsevanos G, Echauz J, Henry T, Pennell P, Epstein C, Bakay R, Bowen C, Litt B (1999). Fractal dimension characterizes seizure onset in epileptic patients. In: Proc. IEEE international conference on acoustics, speech & signal processing (ICASPP), 1999, vol 4, Phoenix, AZ, pp 2343–2346

    Google Scholar 

  153. Kinsner W (1994) Batch and real-time computation of a fractal dimension based on variance of a time series. Technical Report, DEL94-6, Dept. of Electrical & Computer Eng., University of Manitoba

    Google Scholar 

  154. Laennec RTH (1830) A treatise on the diseases of the chest and on mediate auscultation (J. Forbes, Trans.), 3rd edn. Samuel Wood and Sons, and Collins and Hannay, New York

    Google Scholar 

  155. Allain C, Cloitre M (1991) Characterizing the lacunarity of random and deterministic fractal sets. Phys Rev A 44(6):3552–3558

    Article  CAS  PubMed  Google Scholar 

  156. Gefen Y, Meir Y, Mandelbrot BB, Aharony A (1983) Geometric implementation of Hypercubic lattices with non-integer dimensionality by use of low lacunarity fractal lattices. Phys Rev Lett 50(3):145–148

    Article  Google Scholar 

  157. Lin B, Yang ZR (1986) A suggested lacunarity expression for Sierpinski carpets. J Phys A 19(2):L49–L52

    Article  Google Scholar 

  158. Mandelbrot BB (1983) The fractal geometry of nature. Freeman, New York

    Google Scholar 

  159. Plotnick RE, Gardner RH, Hargrove WW, Prestegaard K, Perlmutter M (1996) Lacunarity analysis: a general technique for the analysis of spatial patterns. Phys Rev E 53(5):5461–5468

    Article  CAS  Google Scholar 

  160. Du G, Yeo TS (2002) A novel lacunarity estimation method applied to SAR image segmentation. IEEE Trans Geosci Remote Sens 40(12):2687–2691

    Article  Google Scholar 

  161. Apostolidis GK, Hadjileontiadis LJ (2017) Swarm decomposition: a novel signal analysis using swarm intelligence. Signal Process 132:40–50

    Article  Google Scholar 

  162. Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

    Article  PubMed  Google Scholar 

  163. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    Article  PubMed  Google Scholar 

  164. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  CAS  PubMed  Google Scholar 

  165. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  166. Mavrodis K (2017) Identification of crackles and wheezes in recordings of respiratory cycles, utilizing advanced signal processing techniques, machine learning and convolutional neural networks. Diploma Thesis, Dept. of Electrical & Computer Engineering, Aristotle University of Thessaloniki, July 2017, Thessaloniki, Greece

    Google Scholar 

  167. Mavrodis K, Hadjileontiadis LJ (2017) Detection of wheezes and crackles using deep learning. IEEE Trans Biomed Eng [in process]

    Google Scholar 

  168. Baltatzis V, Bintsi K-M, Apostolidis GK, Hadjileontiadis LJ (2017) Bullying incidences identification within an immersive environment using HD EEG-based analysis: a swarm decomposition and deep learning approach. Nature Sci Rep. https://doi.org/10.1038/s41598-017-17562-0

  169. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Article  PubMed  Google Scholar 

  170. Bottou L (2014) From machine learning to machine reasoning. Mach Learn 94:133–149

    Article  Google Scholar 

  171. Thompson WR (2017) In defence of auscultation: a glorious future? Heart Asia 9:44–47

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Leontios J. Hadjileontiadis .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Hadjileontiadis, L.J., Moussavi, Z.M.K. (2018). Current Techniques for Breath Sound Analysis. In: Priftis, K., Hadjileontiadis, L., Everard, M. (eds) Breath Sounds. Springer, Cham. https://doi.org/10.1007/978-3-319-71824-8_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-71824-8_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71823-1

  • Online ISBN: 978-3-319-71824-8

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics