This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
McKusick VS (1958) Cardiovascular sound in health and disease. Williams & Wilkins, Baltimore, MD
Rapoport J (1986) Laennec and the discovery of auscultation. Israel J Med 22:597–601
Foucault M (1973) The birth of the clinic: an archaeology of medical perception (A. M. Sheridan Smith, Trans.). Pantheon Books, New York, NY
Sterne J (2001) Mediate auscultation, the stethoscope, and the ‘autopsy of the living’: medicine’s acoustic culture. J Med Humanities 22(2):115–136
Davis AB (1981) Medicine and its technology: an introduction to the history of medical instrumentation. Greenwood Press, Westport, CT, pp 88–89
Hadjileontiadis LJ (2009a) Lung sounds: an advanced signal processing perspective (Volume 9 of Synthesis lectures on biomedical engineering). Williston, VT: Morgan & Claypool Publishers
Crary J (1990) Techniques of the observer: on vision and modernity in the nineteenth century. MIT Press, Cambridge, MA, pp 89–90
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
Thacker RE, Kraman SS (1982) The prevalence of auscultatory crackles in subjects without lung disease. Chest 81(6):672–674
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
Robertson AJ (1957) Rales, rhonchi, and Laennec. Lancet 1:417–423
Bohadana A, Izbicki G, Kraman SS (2014) Fundamentals of lung auscultation. N Engl J Med 370(8):744–751
Cugell DW (1987) Lung sound nomenclature. Am Rev Respir Dis 136:1016
Kraman SS (1983) Lung sounds: an introduction to the interpretation of auscultatory findings (workbook). American College of Chest Physicians, Northbrook, IL
Murphy RLH (1985) Discontinuous adventitious lung sounds. Semin Respir Med 6:210–219
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
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
Gavriely N, Cugell DW (1995) Breath sounds methodology. CRC Press, Boca Raton, FL, p 2
Gavriely N, Palti Y, Alroy G (1981) Spectral characteristics of normal breath sounds. J Appl Physiol 50:307–314
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
Murphy RLH, Holford SK, Knowler WC (1978) Visual lung-sound characterization by time-expanded wave-form analysis. N Engl J Med 296:968–971
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
Huq S, Moussavi Z (2012) Acoustic breath-phase detection using tracheal breath sounds. Med Biol Eng Comput 50(3):297–308
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
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
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
Yadollahi A, Moussavi Z (2006) A robust method for estimating respiratory flow using tracheal sound entropy. IEEE Trans Biomed Eng 53(4):662–668
Yadollahi A, Moussavi ZM (2007) Acoustical respiratory flow. IEEE Eng Med Biol Mag 26(1):56–61
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
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
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
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
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
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
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
Hadjileontiadis LJ, Panas SM (1998a) A wavelet-based reduction of heart sound noise from lung sounds. Int J Med Inform 52:183–190
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
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
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
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
Nersisson R, Noel MM (2017) Heart sound and lung sound separation algorithms: a review. J Med Eng Technol 41(1):13–21
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
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
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
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
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
Grønnesby M, Solis JCA, Holsbø E, Melbye H, Bongo LA (2017) Machine learning based crackle detection in lung sounds. arXiv preprint arXiv:1706.00005
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
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
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
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
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
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
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
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
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
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
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
Lu X, Bahoura M (2008) An integrated automated system for crackles extraction and classification. Biomed Signal Process Control 3(3):244–254
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
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
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
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
Rekanos IT, Hadjileontiadis LJ (2006) An iterative kurtosis-based technique for the detection of nonstationary bioacoustic signals. Signal Process 86:3787–3795
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
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
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
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
Ahlstrom C, Johansson A, Hult P, Ask P (2006) Chaotic dynamics of respiratory sounds. J Chaos Soliton Fractals 29:1054–1069
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
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
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
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
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
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
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
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
Taplidou SA, Hadjileontiadis LJ (2007) Nonlinear analysis of wheezes using wavelet bicoherence. Comput Biol Med 37(4):563–570
Taplidou SA, Hadjileontiadis LJ (2010) Analysis of wheezes using wavelet higher order spectral features. IEEE Trans Biomed Eng 57(7):1596–1610
Bahoura M (2009) Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes. Comput Biol Med 39(9):824–843
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)
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
Cohen A (1990) Signal processing methods for upper airway and pulmonary dysfunction diagnosis. IEEE Eng Med Biol Mag 9(1):72–75
Dokur Z (2009) Respiratory sound classification by using an incremental supervised neural network. Pattern Anal Applic 12(4):309–319
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
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
Hadjileontiadis LJ (2003) Discrimination analysis of discontinuous breath sounds using higher-order crossings. Med Biol Eng Comput 41(4):445–455
Hadjileontiadis LJ (2009b) A texture-based classification of crackles and squawks using lacunarity. IEEE Trans Biomed Eng 56(3):718–732
Hoevers J, Loudon RG (1990) Measuring crackles. Chest 98(5):1240–1243
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
Jin F, Sattar F, Goh DY (2014) New approaches for spectro-temporal feature extraction with applications to respiratory sound classification. Neurocomputing 123:362–371
Kahya YP, Yilmaz CA (2000) Modeling of respiratory crackles. In: Proc. 22nd IEEE Eng. Med. Biol. Soc. (EMBS), vol 1, pp 632–634
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
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
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
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
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
Oweis RJ, Abdulhay EW, Khayal A, Awad A (2015) An alternative respiratory sounds classification system utilizing artificial neural networks. Biomed J 38:153–161
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
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
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
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
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
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
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
Yilmaz CA, Kahya YP (2005) Modeling of pulmonary crackles using wavelet networks. In: Proc. 27th IEEE Eng. Med. Biol. Soc. (EMBS), pp 7560–7563
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
Murphy RLH, Holford SK, Knowler WC (1977) Visual lung sound characterization by time-expanded waveform analysis. New Eng J Med 296:968–971
Palaniappan R, Sundaraj K, Ahamed NU (2013) Machine learning in lung sound analysis: a systematic review. Biocybernetics Biomed Eng 33(3):129–135
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
Nikias CL, Petropulu AP (1993) Higher-order spectra analysis: a nonlinear signal processing framework. Prentice-Hall, Englewood Cliffs, NJ
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
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
Raghuveer MR, Nikias CL (1985) Bispectrum estimation: a parametric approach. IEEE Trans Acoustics Speech Signal Process 33(4):1213–1230
Nikias CL, Raghuveer MR (1987) Bispectrum estimation: a digital signal processing framework. Proc IEEE 75(7):869–891
Gavriely N, Palti Y, and Alroy, G “Spectral characteristics of normal breath sounds,” J. Appl. Physiol., Vol.50, pp. 307–314, 1981
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
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
Grossmann A, Morlet J (1984) Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM J Math Anal 15:723–736
Addison PS (2017) The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance. CRC press, Cleveland, OΗ
Astafieva NM (1996) Wavelet analysis: basic theory and some applications. Physics-Uspekhi 39(11):1085–1108
Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Machine Intell 11(7):674–693
Daubechies I (1988) Orthonormal bases of compactly supported wavelets. Commun Pure Appl Math 41:909–996
Daubechies I (1991) Ten lectures on wavelets, CBMS lecture notes series. SIAM, Philadelphia, PA
Mallat SG (1996) Special issue on wavelets. Proc IEEE 84(4):507–686
Mallat SG (1998) A wavelet tour of signal processing. Academic, San Diego
Vetterli M, Kovačević J (1995) Wavelets and subband coding, ch. 4. Prentice-Hall, Englewood Cliffs, NJ, pp 201–298
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)
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
Ayari F, Alouani AT, Ksouri M (2008) Wavelets: an efficient tool for lung sounds analysis. Proc. IEEE computer systems and applications (AICCSA):875–878
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
Mallat SG, Hwang WL (1992) Singularity detection and processing with wavelets. IEEE Trans Inf Theory 38(2):617–643
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
Bayram I, Selesnick IW (2009) Frequency-domain design of overcomplete rational-dilation wavelet transforms. IEEE Trans Signal Process 57(8):2957–2972
Birkelund Y, Hanssen A (1999) Multitaper estimators for bispectra. In: Proc. IEEE SP workshop on higher-order (SPW-HOS), pp 207–213
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
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
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
van Milligen BP, Hidalgo C, Sánchez E (1995b) Nonlinear phenomena and intermittency in plasma turbulence. Phys Rev Lett 74(3):395–398
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
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
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
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
Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1(01):1–41
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
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
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
Nikias CL, Shao M (1995) Signal Processing with Alpha-Stable Distributions and Applications. Wiley & Sons, Inc., USA, New York
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
Kedem B (1994) Time series analysis by higher-order crossings. IEEE Press, Piscataway, NJ
Rasband SN (1997) Fractal dimension, Ch. 4. In: Chaotic dynamics of nonlinear systems. Wiley-Interscience, New York, pp 71–83
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
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
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
Allain C, Cloitre M (1991) Characterizing the lacunarity of random and deterministic fractal sets. Phys Rev A 44(6):3552–3558
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
Lin B, Yang ZR (1986) A suggested lacunarity expression for Sierpinski carpets. J Phys A 19(2):L49–L52
Mandelbrot BB (1983) The fractal geometry of nature. Freeman, New York
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
Du G, Yeo TS (2002) A novel lacunarity estimation method applied to SAR image segmentation. IEEE Trans Geosci Remote Sens 40(12):2687–2691
Apostolidis GK, Hadjileontiadis LJ (2017) Swarm decomposition: a novel signal analysis using swarm intelligence. Signal Process 132:40–50
Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828
Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324
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
Mavrodis K, Hadjileontiadis LJ (2017) Detection of wheezes and crackles using deep learning. IEEE Trans Biomed Eng [in process]
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
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117
Bottou L (2014) From machine learning to machine reasoning. Mach Learn 94:133–149
Thompson WR (2017) In defence of auscultation: a glorious future? Heart Asia 9:44–47
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
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)