Research on Biomedical Engineering
Research on Biomedical Engineering
Original Article

Pulmonary crackle characterization: approaches in the use of discrete wavelet transform regarding border effect, mother-wavelet selection, and subband reduction

Quandt, Verônica Isabela; Pacola, Edras Reily; Pichorim, Sérgio Francisco; Gamba, Humberto Remigio; Sovierzoski, Miguel Antônio

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Introduction: Crackles are discontinuous, non-stationary respiratory sounds and can be characterized by their duration and frequency. In the literature, many techniques of filtering, feature extraction, and classification were presented. Although the discrete wavelet transform (DWT) is a well-known tool in this area, issues like signal border extension, mother-wavelet selection, and its subbands were not properly discussed. Methods: In this work, 30 different mother-wavelets 8 subbands were assessed, and 9 border extension modes were evaluated. The evaluations were done based on the energy representation of the crackle considering the mother-wavelet and the border extension, allowing a reduction of not representative subbands. Results: Tests revealed that the border extension mode considered during the DWT affects crackle characterization, whereas SP1 (Smooth‑Padding of order 1) and ASYMW (Antisymmetric-Padding (whole-point)) modes shall not be used. After DWT, only 3 subbands (D3, D4, and D5) were needed to characterize crackles. Finally, from the group of mother-wavelets tested, Daubechies 7 and Symlet 7 were found to be the most adequate for crackle characterization. Discussion: DWT can be used to characterize crackles when proper border extension mode, mother-wavelet, and subbands are taken into account.


Crackles, Border extension, Discrete Wavelet Transform, Mother-wavelet.


Abbas A, Fahim A. An automated computerized auscultation and diagnostic system for pulmonary diseases. Journal of Medical Systems. 2010; 34(6):1149-55. PMid:20703592

ACCP-ATS. Updated nomenclature for membership reaction. AnnalsATS:ATS News. 1977; 3:5-6

Callegari-Jacques SM. Bioestatística: princípios e aplicações. Porto Alegre: Artmed; 2007

Charleston-Villalobos S, Dorantes-Méndez G, González-Camarena R, Chi-Lem G, Carrillo JG, Aljama-Corrales T. Acoustic thoracic image of crackle sounds using linear and nonlinear processing techniques. Medical & Biological Engineering & Computing. 2011; 49(1):15-24. PMid:20652429

Charleston-Villalobos S, González-Camarena R, Chi-Lem G, Aljama-Corrales T. Crackle sounds analysis by empirical mode decomposition. Nonlinear and nonstationary signal analysis for distinction of crackles in lung sounds. Engineering in Medicine and Biology Magazine. 2007; 26(1):40-7. PMid:17278771

Chen MY, Chou CH. Applying cybernetic technology to diagnose human pulmonary sounds. Journal of Medical Systems. 2014; 38(6):58. PMid:24878780

Dokur Z. Respiratory sound classification by using an incremental supervised neural network. Pattern Analysis & Applications. 2009; 12(4):309-19.

Du M, Chan F, Lam F, Sun J. Crackle detection and classification based on matched wavelet analysis. In: Proceedings – 19th International Conference – IEEE/EMBS; 1997 Oct. 30-Nov. 2; Chicago, IL. Chicago: IEEE; 1997. p. 1638-41

Fraser D. IMD 420-C Review of lung sounds. Davis: School of Medicine, University of California; 1999. [accessed 2014 May 01]. Avaiable from:

Hadjileontiadis LJ, Panas SM. Separation of discontinuous adventitious sounds from vesicular sounds using a wavelet-based filter. IEEE Transactions on Biomedical Engineering. 1997; 44(12):1269-81. PMid:9401227

Hadjileontiadis LJ, Patakas DA, Margaris NJ, Panas SM. Separation of crackles and squawks from vesicular sounds using a wavelet-based filtering technique. COMPEL – The International Journal for Computation and Mathematics in Electrical and Electronic Engineering. 1998; 17:649-57

Hoevers J, Loudon RG. Measuring crackles. Chest. 1990; 98(5):1240-3. PMid:2225972

Içer S, Gengec S. Classification and analysis of non-stationary characteristics of crackle and rhonchus lung adventitious sounds. Digital Signal Processing. 2014; 28:18-27.

Jones A. A brief overview of the analysis of lung sounds. Physiotherapy. 1995; 81(1):37-42.

Kandaswamy A, Kumar CS, Ramanathan RP, Jayaraman S, Malmurugan N. Neural classification of lung sounds using wavelet coefficients. Computers in Biology and Medicine. 2004; 34(6):523-37. PMid:15265722

Kiyokawa H, Greenberg M, Shirota K, Pasterkamp H. Auditory detection of simulated crackles in breath sounds. Chest. 2001; 119(6):1886-92. PMid:11399719

Lehrer S. Understanding lung sounds. 3rd. ed. Philadelphia: Saunders; 2002

Lu X, Bahoura M. An integrated automated system for crackles extraction and classification. Biomedical Signal Processing and Control. 2008; 3(3):244-54.

Mallat S. A Wavelet tour of signal processing. 3rd. ed. Burlington: Academic Press; 2009

Mastorocostas PA, Tolias YA, Theocharis JB, Hadjileontiadis LJ, Panas SM. An orthogonal least squares-based fuzzy filter for real-time analysis of lung sounds. IEEE Transactions on Biomedical Engineering. 2000; 47(9):1165-76. PMid:11008417

Misiti M, Misiti Y, Oppenheim G, Poggi J. Wavelet toolbox – user's guide. Natick: MathWorks; 2013

Munakata M, Ukita H, Doi I, Ohtsuka Y, Masaki Y, Homma Y, Kawakami Y. Spectral and waveform characteristics of fine and coarse crackles. Thorax. 1991; 46(9):651-7. PMid:1948794

Murphy RL Jr, Holford SK, Knowler WC. Visual lung-sound characterization by time-expanded wave-form analysis. The New England Journal of Medicine. 1977; 296(17):968-71. PMid:846543

Pesu L, Helistö P, Ademovic E, Pesquet J-C, Saarinen A, Sovijärvi ARA. Classification of respiratory sounds based on wavelet packet decomposition and learning vector quantization. Technology and Health Care: Official Journal of the European Society for Engineering and Medicine. 1998; 6(1):65-74. PMid:9754685

Ponte DF, Moraes R, Hizume DC, Alencar AM. Characterization of crackles from patients with fibrosis, heart failure and pneumonia. Medical Engineering & Physics. 2013; 35(4):448-56. PMid:22789810

Reichert S, Gass R, Brandt C, Andrès E. Analysis of respiratory sounds: state of the art. Clinical Medicine. Circulatory, Respiratory and Pulmonary Medicine. 2008; 2:45-58. PMid:21157521

Riella R, Nohama P, Maia J. Methodology for automatic classification of adventitious lung sounds. In: IFMBE Proceedings of the World Congress on Medical Physics and Biomedical Engineering; 2009 Sept 7-12; Munich, Germany. Munich: Springer; 2009. p. 1392-5

Sankur B, Güler EC, Kahya YP. Multiresolution biological transient extraction applied to respiratory crackles. Computers in Biology and Medicine. 1996; 26(1):25-39. PMid:8654051

Serbes G, Sakar CO, Kahya YP, Aydin N. Pulmonary crackle detection using time–frequency and time–scale analysis. Digital Signal Processing. 2013; 23(3):1012-21.

Sovijarvi A, Dalmasso F, Vanderschoot J, Malmberg L, Righini G, Stoneman S. Definition of terms for applications of respiratory sounds. European Respiratory Review. 2000a; 10:597-610

Sovijarvi A, Malmberg L, Charbonneau G, Vanderschoot J, Dalmasso F, Sacco C, Rossi M, Earis J. Characteristics of breath sounds and adventitious respiratory sounds. European Respiratory Review. 2000b; 10:591-6

Tolias YA, Hadjileontiadis LJ, Panas SM. A fuzzy rule-based system for real-time separation of crackles from vesicular sounds. In: Proceedings of the 19th Annual International Conference of Engineering in Medicine and Biology Society; 1997 Oct 30 - Nov 2; Chicago, Illinois. Chicago: IEEE; 1997. p. 1115-8

Xie S, Jin F, Krishnan S, Sattar F. Signal feature extraction by multi-scale PCA and its application to respiratory sound classification. Medical & Biological Engineering & Computing. 2012; 50(7):759-68. PMid:22467314

Yeginer M, Kahya Y. Modeling of pulmonary crackles using wavelet networks. In: Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology; 2005 Sept 1-4; Shanghai, China. Shanghai: IEEE; 2005. p. 7560-3

Yeginer M, Kahya YP. Feature extraction for pulmonary crackle representation via wavelet networks. Computers in Biology and Medicine. 2009; 39(8):713-21. PMid:19539902

Yeginer M, Kahya YP. Probing the existence of medium pulmonary crackles via model-based clustering. Computers in Biology and Medicine. 2010; 40(9):765-74. PMid:20728880

Zhenzhen L, Xiaoming W, Minghui D. A novel method for feature extraction of crackles in lung sound. In: Proceedings of the 5th International Conference on BioMedical Engineering and Informatics; 2012 Oct 16-18; Chongqing, China. Chongqing: IEEE; 2012. p. 399-402
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