Discrimination analysis of discontinuous breath sounds using higher-order crossings

The paper evaluates the performance of an automatic discrimination analysis (DA) method used to discriminate efficiently the types of discontinuous breath sound (DBS), i.e. fine crackles (FCs), coarse crackles (CCs) and squawks (SQs); this may lead to more accurate characterisation of the pulmonary acoustical changes due to the related pathology. Based on higher-order crossings (HOCs), the proposed method, HOC-DA, captured the differences in the oscillatory patterns of FCs, CCs and SQs, which are only exposed when higher (>1) crossings are employed. Prior to HOC-DA, wavelet-based de-noising of DBSs was employed to eliminate the effects of the vesicular sound (background noise) from their oscillatory pattern. The HOC-DA was applied to 157 discontinuous breath sounds corresponding to 16 cases included in three lung sound databases. Results showed that the HOC-DA efficiently separated FCs from CCs, SQs from CCs (both with an accuracy of 100%), and SQs from FCs (accuracy of 80%), with the optimum order ranging from 9 to 11. When compared with other classification tools, the HOC-DA resulted in high discrimination accuracy without involving high computational complexity. Owing to its simplicity, it could be implemented in a real-time context and be used in clinical medicine as a module of an integrated intelligent patient evaluation system.

[1]  A. Cohen.  Signal processing methods for upper airway and pulmonary dysfunction diagnosis , 1990, IEEE Engineering in Medicine and Biology Magazine.

[2]  L. Hadjileontiadis,et al.  On modeling impulsive bioacoustic signals with symmetric /spl alpha/-stable distributions: application in discontinuous adventitious lung sounds and explosive bowel sounds , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[3]  L Pesu,et al.  Classification of respiratory sounds based on wavelet packet decomposition and learning vector quantization. , 1998, Technology and health care : official journal of the European Society for Engineering and Medicine.

[4]  M Matsuzaki,et al.  [Polarity of crackle waveforms: a new index for crackle differentiation]. , 1985, [Hokkaido igaku zasshi] The Hokkaido journal of medical science.

[5]  R M Rudd,et al.  Lung crackle characteristics in patients with asbestosis, asbestos-related pleural disease and left ventricular failure using a time-expanded waveform analysis--a comparative study. , 1994, Respiratory medicine.

[6]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[7]  C. Metz.  Basic principles of ROC analysis. , 1978, Seminars in nuclear medicine.

[8]  C. Carrington,et al.  Crackles (rales) in the interstitial pulmonary diseases. , 1978, Chest.

[9]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[10]  Mary Boudreau Conover,et al.  Understanding heart sounds and murmurs : with an introduction to lung sounds , 1984 .

[11]  Y Homma,et al.  Spectral and waveform characteristics of fine and coarse crackles. , 1991, Thorax.

[12]  Yasemin P. Kahya,et al.  Modeling of respiratory crackles , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[13]  S Murao,et al.  Waveform and spectral analysis of crackles. , 1980, Thorax.

[14]  S. Lehrer.  Understanding Lung Sounds , 2002 .

[15]  Noam Gavriely,et al.  Breath Sounds Methodology , 1995 .

[16]  G. Gottlieb,et al.  Coordinating movement at two joints: a principle of linear covariance. , 1996, Journal of neurophysiology.

[17]  Toivo Katila,et al.  A New Versatile PC-Based Lung Sounds Analyzer With Automatic Crackle Analysis (HeLSA) , 1996 .

[18]  W. Gander,et al.  Adaptive Quadrature—Revisited , 2000 .

[19]  C. Trombly,et al.  Effect of rehabilitation tasks on organization of movement after stroke. , 1999, The American journal of occupational therapy : official publication of the American Occupational Therapy Association.

[20]  Yusuf Ozturk,et al.  Automatic detection and classification of crackles by using a neural network , 1992, 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  L.J. Hadjileontiadis,et al.  Separation of discontinuous adventitious sounds from vesicular sounds using a wavelet-based filter , 1997, IEEE Transactions on Biomedical Engineering.

[22]  Y.P. Kahya,et al.  Respiratory disease diagnosis using lung sounds , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[23]  V Chernick,et al.  The effect of anticholinergic treatment on postexertional wheezing in asthma studied by phonopneumography and spirometry. , 1985, The American review of respiratory disease.

[24]  W. Knowler,et al.  Visual lung-sound characterization by time-expanded wave-form analysis. , 1977, The New England journal of medicine.

[25]  K. E. Novak,et al.  Kinematic properties of rapid hand movements in a knob turning task , 2000, Experimental Brain Research.

[26]  Ed. McKenzie.  10. Time Series Analysis by Higher Order Crossings , 1996 .

[27]  Stavros M. Panas,et al.  Enhancement of bowel sounds by wavelet-based filtering , 2000, IEEE Transactions on Biomedical Engineering.

[28]  R G Loudon,et al.  Measuring crackles. , 1990, Chest.

[29]  Sheng He,et al.  Visual motion of missing-fundamental patterns: motion energy versus feature correspondence , 2000, Vision Research.