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Extraction of low-dimensional features for single-channel common lung sound classification

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Abstract

In this study, feature extraction methods used in the classification of single-channel lung sounds obtained by automatic identification of respiratory cycles were examined in detail in order to extract distinctive features at the lowest size. In this way, it will be possible to design a system for the detection of lung diseases, completely autonomously. In the study, automatic separation and classification of 400 respiratory cycles were performed from the single-channel common lung sounds obtained from 94 people. Leave one out cross validation (LOOCV) was used for the calibration and validation of the classification model. The Mel frequency cepstrum coefficients (MFCC), time domain features, frequency domain features, and linear predictive coding (LPC) were used for classification. The performance of the features was tested using linear discriminant analysis (LDA), k-nearest neighbors (k-NN), support vector machines (SVM), and naive Bayes (NB) classification algorithms. The success of combinations of features was explored and enhanced using the sequential forward selection (SFS). As a result, the best accuracy (90.14% in the training set and 90.63% in the test set) was acquired using the k-NN for the triple combination, which included the standard deviation of LPC and the standard deviation and the mean of MFCC.

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References

  1. Lehrer S (2008) Understanding lung sounds with audio CD, 3rd ed. WB Saunders, London, England

  2. Aras S, Öztürk M, Gangal A (2018) Automatic detection of the respiratory cycle from recorded, single-channel sounds from lungs. Turk J of Electr Eng Comput Scı 26:11–22. https://doi.org/10.3906/elk-1705-16

    Article  Google Scholar 

  3. Yilmaz CA, Kahya YP (2006) Multi-channel classification of respiratory sounds. Conf Proc IEEE Eng Med Biol Soc 2006:2864–2867. https://doi.org/10.1109/IEMBS.2006.259385

    Article  PubMed  Google Scholar 

  4. Murphy R (2007) Computerized multichannel lung sound analysis. Development of acoustic instruments for diagnosis and management of medical conditions. IEEE Eng Med Biol Mag 26:16–19. https://doi.org/10.1109/memb.2007.289117

    Article  PubMed  Google Scholar 

  5. Sen I, Kahya YP (2005) A multi-channel device for respiratory sound data acquisition and transient detection. Conf Proc IEEE Eng Med Biol Soc 2005:6658–6661. https://doi.org/10.1109/IEMBS.2005.1616029

    Article  CAS  PubMed  Google Scholar 

  6. Islam MA, Bandyopadhyaya I, Bhattacharyya P, Saha G (2018) Multichannel lung sound analysis for asthma detection. Comput Methods Programs Biomed 159:111–123. https://doi.org/10.1016/j.cmpb.2018.03.002

    Article  PubMed  Google Scholar 

  7. Charleston-Villalobos S, Martinez-Hernandez G, Gonzalez-Camarena R et al (2011) Assessment of multichannel lung sounds parameterization for two-class classification in interstitial lung disease patients. Comput Biol Med 41:473–482. https://doi.org/10.1016/j.compbiomed.2011.04.009

    Article  CAS  PubMed  Google Scholar 

  8. Messner E, Fediuk M, Swatek P et al (2020) Multi-channel lung sound classification with convolutional recurrent neural networks. Comput Biol Med 122:103831. https://doi.org/10.1016/j.compbiomed.2020.103831

    Article  PubMed  Google Scholar 

  9. Altan G, Kutlu Y, Gökçen A (2020) Chronic obstructive pulmonary disease severity analysis using deep learning on multi-channel lung sounds. Turk J of Electr Eng Comput Sci 28:2979–2996. https://doi.org/10.3906/elk-2004-68

    Article  Google Scholar 

  10. Huq S, Moussavi Z (2012) Acoustic breath-phase detection using tracheal breath sounds. Med Biol Eng Comput 50:297–308. https://doi.org/10.1007/s11517-012-0869-9

    Article  PubMed  Google Scholar 

  11. Tabata H, Hirayama M, Enseki M et al (2016) A novel method for detecting airway narrowing using breath sound spectrum analysis in children. Respir Investig 54:20–28. https://doi.org/10.1016/j.resinv.2015.07.002

    Article  PubMed  Google Scholar 

  12. Yahya O, Faezipour M (2014) Automatic detection and classification of acoustic breathing cycles. In: Proceedings of the 2014 Zone 1 Conference of the American Society for Engineering Education. IEEE

  13. Dabiri S, Masnadi Shirazi MA (2015) Estimation of respiratory rate from photoplethysmogram signal of sleep apnea patients: a comparative study of different methods. In: 2015 38th International Conference on Telecommunications and Signal Processing (TSP). IEEE

  14. Waitman LR, Clarkson KP, Barwise JA, King PH (2000) Representation and classification of breath sounds recorded in an intensive care setting using neural networks. J Clin Monit Comput 16:95–105. https://doi.org/10.1023/a:1009934112185

    Article  CAS  PubMed  Google Scholar 

  15. Bahoura M (2009) Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes. Comput Biol Med 39:824–843. https://doi.org/10.1016/j.compbiomed.2009.06.011

    Article  PubMed  Google Scholar 

  16. Sen I, Saraclar M, Kahya YP (2015) A comparison of SVM and GMM-based classifier configurations for diagnostic classification of pulmonary sounds. IEEE Trans Biomed Eng 62:1768–1776. https://doi.org/10.1109/TBME.2015.2403616

    Article  PubMed  Google Scholar 

  17. Palaniappan R, Sundaraj K, Lam CK (2016) Reliable system for respiratory pathology classification from breath sound signals. In: 2016 International Conference on System Reliability and Science (ICSRS). IEEE

  18. Göğüş FZ, Karlık B, Harman G (2016) Identification of pulmonary disorders by using different spectral analysis methods. Int J Comput Intell Syst 9:595. https://doi.org/10.1080/18756891.2016.1204110

    Article  Google Scholar 

  19. Koeipensri T, Boonchoo P, Sueaseenak D (2016) The development of biosignal processing system (BPS-SWU V1. 0) for learning and research in biomedical engineering. In: 9th Biomedical Engineering International Conference (BMEiCON). Laos, pp 1–4

  20. Sankur B, Kahya YP, Çağatay Güler E, Engin T (1994) Comparison of AR-based algorithms for respiratory sounds classification. Comput Biol Med 24:67–76. https://doi.org/10.1016/0010-4825(94)90038-8

    Article  CAS  PubMed  Google Scholar 

  21. Chamberlain D, Kodgule R, Ganelin D et al (2016) Application of semi-supervised deep learning to lung sound analysis. Annu Int Conf IEEE Eng Med Biol Soc 2016:804–807. https://doi.org/10.1109/EMBC.2016.7590823

    Article  PubMed  Google Scholar 

  22. Zulfiqar R, Majeed F, Irfan R et al (2021) Abnormal respiratory sounds classification using deep CNN through artificial noise addition. Front Med (Lausanne) 8:714811. https://doi.org/10.3389/fmed.2021.714811

    Article  PubMed Central  Google Scholar 

  23. Kim Y, Hyon Y, Jung SS et al (2021) Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning. Sci Rep 11:17186. https://doi.org/10.1038/s41598-021-96724-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Belkacem AN, Ouhbi S, Lakas A et al (2021) End-to-end AI-based point-of-care diagnosis system for classifying respiratory illnesses and early detection of COVID-19: a theoretical framework. Front Med (Lausanne) 8:585578. https://doi.org/10.3389/fmed.2021.585578

    Article  Google Scholar 

  25. Gurung A, Scrafford CG, Tielsch JM et al (2011) Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: a systematic review and meta-analysis. Respir Med 105:1396–1403. https://doi.org/10.1016/j.rmed.2011.05.007

    Article  PubMed  PubMed Central  Google Scholar 

  26. Hjorth B (1970) EEG analysis based on time domain properties. Electroencephalogr Clin Neurophysiol 29:306–310. https://doi.org/10.1016/0013-4694(70)90143-4

    Article  CAS  PubMed  Google Scholar 

  27. Rabiner LR, Juang B-H (1993) Fundamentals of speech recognition

  28. Makhoul J (1975) Linear prediction: a tutorial review. Proc IEEE Inst Electr Electron Eng 63:561–580. https://doi.org/10.1109/proc.1975.9792

    Article  Google Scholar 

  29. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19:711–720. https://doi.org/10.1109/34.598228

    Article  Google Scholar 

  30. Yavuz E, Aydemir O (2016) Olfaction recognition by EEG analysis using wavelet transform features. In: 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA). IEEE

  31. Sain SR, Vapnik VN (1996) The nature of statistical learning theory. Technometrics 38:409. https://doi.org/10.2307/1271324

    Article  Google Scholar 

  32. Theodoridis S, Koutroumbas K (2014) Pattern recognition, 3rd edn. Academic Press

    Google Scholar 

  33. Şen I, Saraclar M, Kahya YP (2015) A comparison of DVM and GMM-based classifier configurations for diagnostic classification of pulmonary sounds. IEEE Trans Biomed Eng 62:1768–1776

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank TUBITAK (The Scientific and Technological Research Council of Turkey) for supporting this work with project number 116E003.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Bayburt University, 69000, Bayburt, Turkey

    M. Alptekin Engin

  2. Department of Electrical and Electronics Engineering, Ondokuz Mayis University, 55200, Samsun, Turkey

    Selim Aras

  3. Department of Electrical and Electronics Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey

    Ali Gangal

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  1. M. Alptekin Engin
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  2. Selim Aras
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  3. Ali Gangal
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Correspondence to M. Alptekin Engin.

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Engin, M.A., Aras, S. & Gangal, A. Extraction of low-dimensional features for single-channel common lung sound classification. Med Biol Eng Comput 60, 1555–1568 (2022). https://doi.org/10.1007/s11517-022-02552-w

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