Using of the machine learning methods to identify bronchopulmonary system diseases with the use of lung sounds
DOI:
https://doi.org/10.20535/RADAP.2018.73.55-62Keywords:
electronic stethoscope, lung sound, spectral analysis, wavelet transform, machine learningAbstract
This study reviews the main approaches to the analyzing of modern methods of digital processing of lung sounds. It is shown that each of the existing methods gives a definite result in solving a particular problem. However, none of the methods that were reviewed, can’t be called universal and completely convenient for using in the real conditions of the hospital. Certain numerical parameters can be obtained, as a result of the work of each method. In this study it is showed that machine learning can serve as a unifying mechanism for the considered methods. A set of different parameters can be the input arguments of the classifier, which will be properly trained. As a result, the primary opinion in a convenient and accessible form can be presented to the doctor.References
Quandt V.I., Pacola E.R., Pichorim S.F. and Sovierzoski M.A. (2013) Border Extension in the Wavelet Analysis of Lung Sounds. IFMBE Proceedings, pp. 597-600. DOI: 10.1007/978-3-642-29305-4_156
Shaharum S.M., Sundaraj K. and Palaniappan R. (2012) Tracheal sound reliability for wheeze data collection method: A review. 2012 IEEE International Conference on Control System, Computing and Engineering. DOI: 10.1109/iccsce.2012.6487153
Morillo D.S., Moreno S.A., and Jiménez A.L. (2013) Computerized analysis of respiratory sounds during COPD exacerbations. Computers in Biology and Medicine, Vol. 43, Iss. 7, pp. 914-921. DOI: 10.1016/j.compbiomed.2013.03.011
Piirilä P., Sovijärvi A.R., Kaisla T., Rajala H. and Katila T. (1991) Crackles in Patients with Fibrosing Alveolitis, Bronchiectasis, COPD, and Heart Failure. Chest, Vol. 99, Iss. 5, pp. 1076-1083. DOI: 10.1378/chest.99.5.1076
Murphy R.L., Vyshedskiy A., Power V., Bana D., Marinelli P., Wong-Tse A. and Paciej R. (2003) Automated Lung Sound Analysis in Patients With Pneumoni. Chest, Vol. 124, Iss. 4, pp. 190S. DOI: 10.1378/chest.124.4_meetingabstracts.190s-b
Aydore S., Sen I., Kahya Y. and Mihcak M. (2009) Classification of respiratory signals by linear analysis. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. DOI: 10.1109/iembs.2009.5335395
Yi G.A. (2004) A software toolkit for acoustic respiratory analysis, Thesis, Massachusetts Institute of Technology.
Lin B.-S., Lin B.-S., Wu H.-D., Chong F.-C. and Chen S.-J. (2006) Wheeze Recognition Based on 2D Bilateral Filtering of Spectrogram. Biomedical Engineering: Applications, Basis and Communications, Vol. 18, Iss. 03, pp. 128-137. DOI: 10.4015/s1016237206000221
Serbes G., Sakar C.O., Kahya Y.P. and Aydin N. (2011) Feature extraction using time-frequency/scale analysis and ensemble of feature sets for crackle detection. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. DOI: 10.1109/iembs.2011.6090899
Kamble H. and Phadke G.S. (2016) Frequency response analysis of respiratory sounds and comparative study for windowing techniques. 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES). DOI: 10.1109/scopes.2016.7955809
Ifeachor E. (1995) A practical approach to digital signal processing. IEE Colloquium on The Teaching of Digital Signal Processing (DSP) in Universities. DOI: 10.1049/ic:19950210
Quandt V.I., Pacola E.R., Pichorim S.F. and Sovierzoski M.A. (2013) Border Extension in the Wavelet Analysis of Lung Sounds. IFMBE Proceedings, pp. 597-600. DOI: 10.1007/978-3-642-29305-4_156 .
Petropulu A. (1999) Higher-Order Spectral Analysis. Electrical Engineering Handbook. DOI: 10.1201/9781420049510.ch57
Chekhovych M., Poreva A., Karplyuk Y. and Makarenkova A. (2016) Application of higher-order spectral analysis to lung sounds in patients with chronic bronchitis. 2016 IEEE 36th International Conference on Electronics and Nanotechnology (ELNANO). DOI: 10.1109/elnano.2016.7493059
Yeginer M. and Kahya Y.P. (2008) Elimination of vesicular sounds from pulmonary crackle waveforms. Computer Methods and Programs in Biomedicine, Vol. 89, Iss. 1, pp. 1-13. DOI: 10.1016/j.cmpb.2007.10.002
Korenbaum V., Pochekutova I., Kostiv A., Tagiltsev A., Shubin S. and Bodin N. (2011) Acoustic tool to test status of respiratory ventilation function. 2011 Defense Science Research Conference and Expo (DSR). DOI: 10.1109/dsr.2011.6026821
Wang D., Fang B., Liang S. and Wang S. (2016) The research progress about the intelligent recognition of lung sounds. 2016 2nd IEEE International Conference on Computer and Communications (ICCC). DOI: 10.1109/compcomm.2016.7924807
Chamberlain D., Kodgule R., Ganelin D., Miglani~V. and Fletcher R.R. (2016) Application of semi-supervised deep learning to lung sound analysis. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). DOI: 10.1109/embc.2016.7590823
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
