Startseite Classification of pulmonary pathology from breath sounds using the wavelet packet transform and an extreme learning machine
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Classification of pulmonary pathology from breath sounds using the wavelet packet transform and an extreme learning machine

  • Rajkumar Palaniappan EMAIL logo , Kenneth Sundaraj , Sebastian Sundaraj , N. Huliraj und S.S. Revadi
Veröffentlicht/Copyright: 8. Juni 2017
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Abstract

Background:

Auscultation is a medical procedure used for the initial diagnosis and assessment of lung and heart diseases. From this perspective, we propose assessing the performance of the extreme learning machine (ELM) classifiers for the diagnosis of pulmonary pathology using breath sounds.

Methods:

Energy and entropy features were extracted from the breath sound using the wavelet packet transform. The statistical significance of the extracted features was evaluated by one-way analysis of variance (ANOVA). The extracted features were inputted into the ELM classifier.

Results:

The maximum classification accuracies obtained for the conventional validation (CV) of the energy and entropy features were 97.36% and 98.37%, respectively, whereas the accuracies obtained for the cross validation (CRV) of the energy and entropy features were 96.80% and 97.91%, respectively. In addition, maximum classification accuracies of 98.25% and 99.25% were obtained for the CV and CRV of the ensemble features, respectively.

Conclusion:

The results indicate that the classification accuracy obtained with the ensemble features was higher than those obtained with the energy and entropy features.

Acknowledgments

The authors of this work sincerely thank Prof. Dr. M. K. Sudarshan and Assoc. Prof. Dr. D. H. Aswath Narayana for their support and directions.

  1. Author Statement

  2. Research funding: Authors state no funding involved.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Informed consent: All the subjects involved in the study provided written informed consent.

  5. Ethical approval: Ethical clearance was granted by the institutional Ethics Committee of KIMS. The research was performed in accordance with the principles of the Declaration of Helsinki.

  6. Authors’ Contributions Rajkumar Palaniappan, Kenneth Sundaraj, Sebastian Sundaraj implemented algorithms, carried out analysis and drafted the manuscript, N. Huliraj, S.S.Revadi participated in the design of the study, data validation and coordination.

References

[1] Abbas A, Fahim A. An automated computerized auscultation and diagnostic system for pulmonary diseases. J Med Syst 2010; 34: 1149–1155.10.1007/s10916-009-9334-1Suche in Google Scholar PubMed

[2] Alsmadi S, Kahya YP. Design of a DSP-based instrument for real-time classification of pulmonary sounds. Comput Biol Med 2008; 38: 53–61.10.1016/j.compbiomed.2007.07.001Suche in Google Scholar PubMed

[3] Anisimov VN, Herbst JA, Abramchuk AN, Latanov AV, Hahnloser RH, Vyssotski AL. Reconstruction of vocal interactions in a group of small songbirds. Nat Methods 2014; 11; 1135–1137.10.1038/nmeth.3114Suche in Google Scholar PubMed

[4] Bhaskar H, Hoyle DC, Singh S. Machine learning in bioinformatics: a brief survey and recommendations for practitioners. Comput Biol Med 2006; 36: 1104–1125.10.1016/j.compbiomed.2005.09.002Suche in Google Scholar PubMed

[5] Bouckaert R, Frank E. Evaluating the replicability of significance tests for comparing learning algorithms. In: Dai H, Srikant R, Zhang C, editors. Advances in knowledge discovery and data mining. Berlin Heidelberg: Springer 2004: 3–12.10.1007/978-3-540-24775-3_3Suche in Google Scholar

[6] Charleston-Villalobos S, Martinez-Hernandez G, Gonzalez-Camarena R, Chi-Lem G, Carrillo JG, Aljama-Corrales T. Assessment of multichannel lung sounds parameterization for two-class classification in interstitial lung disease patients. Comput Biol Med 2011; 41: 473–482.10.1016/j.compbiomed.2011.04.009Suche in Google Scholar PubMed

[7] Cohen A, Daubechies I, Feauveau J-C. Biorthogonal bases of compactly supported wavelets. Commun Pure Appl Math 1992; 45: 485–560.10.1002/cpa.3160450502Suche in Google Scholar

[8] Dokur Z. Respiratory sound classification by using an incremental supervised neural network. Pattern Anal Appl 2009; 12: 309–319.10.1007/s10044-008-0125-ySuche in Google Scholar

[9] Fiz JA, Jané R, Lozano M, Gómez R, Ruiz J. Detecting unilateral phrenic paralysis by acoustic respiratory analysis. PLoS One 2014; 9: e93595.10.1371/journal.pone.0093595Suche in Google Scholar PubMed PubMed Central

[10] Göğüş FZ, Karlik B, Guclu G. Classification of asthmatic breath sounds by using wavelet transforms and neural networks. Int J Signal Process Sys 2015; 3: 106–111.10.12720/ijsps.3.2.106-111Suche in Google Scholar

[11] Gross V, Dittmar A, Penzel T, Schüttler F, von Wichert P. The relationship between normal lung sounds, age, and gender. Am J Respir Crit Care Med 2000; 162; 905–909.10.1164/ajrccm.162.3.9905104Suche in Google Scholar

[12] Güler I, Polat H, Ergün U. Combining neural network and genetic algorithm for prediction of lung sounds. J Med Syst 2005; 29: 217–231.10.1007/s10916-005-5182-9Suche in Google Scholar

[13] Hariharan M, Polat K, Sindhu R, Yaacob S. A hybrid expert system approach for telemonitoring of vocal fold pathology. Appl Soft Comput 2013; 13: 4148–4161.10.1016/j.asoc.2013.06.004Suche in Google Scholar

[14] Hashemi A, Arabalibiek H, Agin K. Classification of wheeze sounds using wavelets and neural networks. In: International Conference on Biomedical Engineering and Technology, 2011, pp. 127–131.Suche in Google Scholar

[15] Hashemi A, Arabalibeik H, Agin K. Classification of wheeze sounds using cepstral analysis and neural networks. Stud Health Technol Inform 2012; 173: 161–165.10.3233/978-1-61499-022-2-161Suche in Google Scholar

[16] Huang G-B. An insight into extreme learning machines: random neurons, random features and kernels. Cognit Comput 2014; 6: 376–390.10.1007/s12559-014-9255-2Suche in Google Scholar

[17] Huang G, Huang G-B, Song S, You K. Trends in extreme learning machines: a review. Neural Netw 2015; 61: 32–48.10.1016/j.neunet.2014.10.001Suche in Google Scholar

[18] İçer S, Gengeç Ş. Classification and analysis of non-stationary characteristics of crackle and rhonchus lung adventitious sounds. Digit Signal Process 2014; 28: 18–27.10.1016/j.dsp.2014.02.001Suche in Google Scholar

[19] Kandaswamy A, Kumar CS, Ramanathan RP, Jayaraman S, Malmurugan N. Neural classification of lung sounds using wavelet coefficients. Comput Biol Med 2004; 34: 523–537.10.1016/S0010-4825(03)00092-1Suche in Google Scholar

[20] Lei B, Rahman SA, Song I. Content-based classification of breath sound with enhanced features. Neurocomputing 2014; 141: 139–147.10.1016/j.neucom.2014.04.002Suche in Google Scholar

[21] Lu X, Bahoura M. An integrated automated system for crackles extraction and classification. Biomed Signal Process Control 2008; 3: 244–254.10.1016/j.bspc.2008.04.003Suche in Google Scholar

[22] Matsunaga S, Yamauchi K, Yamashita M. Classification between normal and abnormal respiratory sounds based on maximum likelihood approach. In: IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE: Taipei, Taiwan 2009, pp. 517–520.10.1109/ICASSP.2009.4959634Suche in Google Scholar

[23] Mayorga P, Druzgalski C, Morelos RL, Gonzalez OH, Vidales J. Acoustics based assessment of respiratory diseases using GMM classification. Conf Proc IEEE Eng Med Biol Soc 2010; 2010: 6312–6316.10.1109/IEMBS.2010.5628092Suche in Google Scholar PubMed

[24] Mayorga P, Druzgalski C, González OH, López HS. Modified classification of normal lung sounds applying quantile vectors. Conf Proc IEEE Eng Med Biol Soc 2012; 2012: 4262–4265.10.1109/EMBC.2012.6346908Suche in Google Scholar PubMed

[25] Palaniappan R, Sundaraj K. Respiratory sound classification using cepstral features and support vector machine. Trivandram, India: IEEE Recent Advances in Intelligent Computational Systems (RAICS) 2013: 132–136.10.1109/RAICS.2013.6745460Suche in Google Scholar

[26] Palaniappan R, Sundaraj K, Ahamed NU, Arjunan A, Sundaraj S. Computer-based respiratory sound analysis: a systematic review. IETE Tech Rev 2013; 30: 248–256.10.4103/0256-4602.113524Suche in Google Scholar

[27] Palaniappan R, Sundaraj K, Ahamed NU. Machine learning in lung sound analysis: a systematic review. Biocybern Biomed Eng 2013; 33: 129–135.10.1016/j.bbe.2013.07.001Suche in Google Scholar

[28] Palaniappan R, Sundaraj K, Sundaraj S. A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals. BMC Bioinformatics 2014; 15: 223.10.1186/1471-2105-15-223Suche in Google Scholar PubMed PubMed Central

[29] Palaniappan R, Sundaraj K, Sundaraj S, Archana B. Pulmonary acoustic signal classification using autoregressive coefficients and k-nearest neighbor. Appl Mech Mater 2014; 591: 211–214.10.4028/www.scientific.net/AMM.591.211Suche in Google Scholar

[30] Palaniappan R, Sundaraj K, Sundaraj S, Huliraj N, Revadi SS, Archana B. Classification of respiratory pathology in pulmonary acoustic signals using parametric features and artificial neural network. Coimbatore, India: IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) 2014: 1–6.10.1109/ICCIC.2014.7238315Suche in Google Scholar

[31] Palaniappan R, Sundaraj K, Sundaraj S, Huliraj N, Revadi SS. A telemedicine tool to detect pulmonary pathology using computerized pulmonary acoustic signal analysis. Appl Soft Comput 2015; 37: 952–959.10.1016/j.asoc.2015.05.031Suche in Google Scholar

[32] Palaniappan R, Sundaraj K, Sundaraj S, Huliraj N, Revadi SS. A novel approach to detect respiratory phases from pulmonary acoustic signals using normalised power spectral density and fuzzy inference system. Clin Respir J 2016; 10: 486–494.10.1111/crj.12250Suche in Google Scholar PubMed

[33] Pasterkamp H, Kraman SS, Wodicka GR. Respiratory sounds advances beyond the stethoscope. Am J Respir Crit Care Med 1997; 156: 974–987.10.1164/ajrccm.156.3.9701115Suche in Google Scholar PubMed

[34] Priyadarshini R, Dash N, Mishra R. A Novel approach to predict diabetes mellitus using modified Extreme learning machine, in International Conference on Electronics and Communication Systems (ICECS), 2014, pp. 1–5.10.1109/ECS.2014.6892740Suche in Google Scholar

[35] Reyes BA, Reljin N, Chon KH. Tracheal sounds acquisition using smartphones. Sensors (Basel) Switzerland 2014; 14: 13830–13850.10.3390/s140813830Suche in Google Scholar PubMed PubMed Central

[36] Rossi M, Sovijarvi AR, Piirila P, Vannuccini L, Dalmasso F, Vanderschoot J. Environmental and subject conditions and breathing manoeuvres for respiratory sound recordings. Eur Respir Rev 2000; 10: 611–615.Suche in Google Scholar

[37] Sankar AB, Durairaj K, Seethalakshmi K. Neural network based respiratory signal classification using various feed-forward back propagation training algorithms. Eur J Sci Res 2011; 49: 468–483.Suche in Google Scholar

[38] Serbes G, Sakar CO, Kahya YP, Aydin N. Pulmonary crackle detection using time–frequency and time–scale analysis. Digital Signal Processing 2013; 23: 1012–1021.10.1016/j.dsp.2012.12.009Suche in Google Scholar

[39] Ulukaya S, Kahya YP. Respiratory sound classification using perceptual linear prediction features for healthy – Pathological diagnosis. Istanbul, Turkey: IEEE 2014; 1–4.10.1109/BIYOMUT.2014.7026343Suche in Google Scholar

[40] Vannuccini L, Earis JE, Helisto P, et al. Capturing and preprocessing of respiratory sounds. Eur Respir Rev 2000; 10: 616–620.Suche in Google Scholar

[41] Vishwakarma VP, Gupta MN. A new learning algorithm for single hidden layer feedforward neural networks. Int J Comput Appl T 2011; 28: 8.10.5120/3390-4706Suche in Google Scholar

[42] Xie Z, Xu K, Shan W, Liu L, Xiong Y, Huang H. Projective feature learning for 3D shapes with multi-view depth images. Comput Graph Forum 2015; 34: 1–11.10.1111/cgf.12740Suche in Google Scholar

Received: 2016-04-21
Accepted: 2017-04-20
Published Online: 2017-06-08
Published in Print: 2018-07-26

©2018 Walter de Gruyter GmbH, Berlin/Boston

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