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ALSD-Net: Automatic lung sounds diagnosis network from pulmonary signals

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Abstract

Chronic respiratory diseases (CRDs) are common across the world. In many countries, there is a shortage of medical professionals and hence there is a need to develop artificial intelligence-based automatic diagnostic tools that can help to diagnose pulmonary diseases by computing the lung sounds. This paper presents an automatic classification method using machine learning to diagnose multiple pulmonary diseases from lung sounds. A comprehensive database of lung sounds was collected and labelled by doctors which was used in a deep learning network. The proposed system involves a neural network model because of its high accuracy to diagnose lung sounds like wheezing sound which can be helpful to diagnose asthma patients. To improve the accuracy of diagnosis in a noisy environment and increase the robustness, the proposed method has used data augmentation techniques for training and multi-classification of lung sounds and has achieved 94.24% accuracy. The proposed model can be ported to any computing devices like computers, single board computing processors, android handheld devices, etc., to make a stand-alone diagnostic tool that may be of help in remote primary health care centres. The proposed method is non-invasive, efficient, robust and has low time complexity making it suitable for real-time applications to diagnose pulmonary diseases.

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References

  1. World Health Organization, “The top 10 causes of death”, https://www.who.int/en/news-room/fact-sheets/detail/the-top-10-causes-of-death, 24 May 2018. Accessed: Jan 2020.

  2. National Health Service England. Overview of potential to reduce lives lost from Chronic Obstructive Pulmonary Disease (COPD); 2014. Available from: https://www.england.nhs.uk/wp-content/uploads/2014/02/rm-fs-6.pdf.

  3. Gould G, Redpath A, Ryan M, Warren P, Best J, Flenley D, MacNee W (1991) Lung CT density correlates with measurements of airflow limitation and the diffusing capacity. Eur Respir J 4(2):141–146

    Google Scholar 

  4. Wipf JE, Lipsky BA, Hirschmann JV, Boyko EJ, Takasugi J, Peugeot RL, Davis CL (1999) Diagnosing pneumonia by physical examination: relevant or relic? Arch Intern Med 159(10):1082–1087

    Article  Google Scholar 

  5. Schulz NJ (2007) Spirometry essentials for medical assistants part iii: standards for the test procedure. J Continu Edu Topic & Issues 9(1):22–22

    Google Scholar 

  6. Kandaswamy A, Kumar C, Ramanathan R, Jayaraman S, Malmurugan N (2004) Neural classification of lung sounds using wavelet coefficients. Comput Biol Med 34(6):523–537

    Article  Google Scholar 

  7. Oletic D, Arsenali B, Bilas V (2011) Towards continuous wheeze detection body sensor node as a core of asthma monitoring system. In: International Conf. on Wireless Mobile Communication and Healthcare. Springer; p. 165–172

  8. Liu X, SerW, Zhang J, Goh DYT (2015) Detection of adventitious lung sounds using entropy features and a 2-D threshold setting. In: IEEE 10th International Conf. on Information, Communications and Signal Processing (ICICS). IEEE; 2015. p. 1–5.

  9. Chamberlain D, Mofor J, Fletcher R, Kodgule R (2015) Mobile stethoscope and signal processing algorithms for pulmonary screening and diagnostics. In: IEEE Global Humanitarian Technology Conference (GHTC). IEEE; 2015. p. 385–392.

  10. Orjuela-Caño ´n AD, Go ´mez-Cajas DF, Jime ´nez-Moreno R (2014) Artificial neural networks for acoustic lung signals classification. In: Iberoamerican Congress on Pattern Recognition. Springer; 2014. p. 214–221.

  11. Owens D (2002) RALE Lung Sounds 3.0. Comput Inform Nurs 5(3):9–10

    Google Scholar 

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

    Article  Google Scholar 

  13. Sankur B, Kahya YP, Gu¨ler EC, Engin T (1994) Comparison of AR-based algorithms for respiratory sounds classification. Comput Bio Med 24(1):67–76

    Article  Google Scholar 

  14. Oweis RJ, Abdulhay EW, Khayal A, Awad A (2015) An alternative respiratory sounds classification system utilizing artificial neural networks. Biomed J 38(2):153. https://doi.org/10.4103/2319-4170.137773

    Article  Google Scholar 

  15. Dokur Z (2009) Respiratory sound classification by using an incremental supervised neural network. Pattern Anal Appl 12(4):309–319. https://doi.org/10.1007/s10044-008-0125-y

    Article  MathSciNet  Google Scholar 

  16. Mendes L, Vogiatzis I, Perantoni E, Kaimakamis E, Chouvarda I, Maglaveras N, et al. (2015) Detection of wheezes using their signature in the spectrogram space and musical features. In: 37th Annual International Conf. of the IEEE EMBS. IEEE. p. 5581–5584

  17. Sengupta N, Md Sahidullah, G Saha (2017) "Lung sound classification using local binary pattern." arXiv preprint arXiv:1710.01703 (2017)

  18. Dubey SR, Chakraborty S, Roy SK, Mukherjee S, Singh SK, Chaudhuri BB (2019) “Diffgrad: an optimization method for convolutional neural networks”. IEEE Transactions on neural networks and learning systems

  19. Pramono RX, Imtiaz SA, Rodriguez-Villegas E (2019) Evaluation of features for classification of wheezes and normal respiratory sounds. PloS One 14(3):e0213659

    Article  Google Scholar 

  20. Aykanat M et al (2017) (2017) “Classification of lung sounds using convolutional neural networks.” EURASIP J Image Video Process 1:1–9

    Google Scholar 

  21. 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(7):1768–1776

    Article  Google Scholar 

  22. Emmanouilidou D et al (2017) Computerized lung sound screening for pediatric auscultation in noisy field environments. IEEE Trans Biomed Eng 65(7):1564–1574

    Article  Google Scholar 

  23. Bardou D, Kun Z, Ahmad SM (2018) Lung sounds classification using convolutional neural networks. Artificial Intell Med 88:58–69

    Article  Google Scholar 

  24. Messner E et al (2020) Multi-channel lung sound classification with convolutional recurrent neural networks. Comp Bio Med 122:103831

    Article  Google Scholar 

  25. Acharya J, Basu A (2020) Deep neural network for respiratory sound classification in wearable devices enabled by patient specific model tuning. IEEE Trans Biomed Circuits Syst 14(3):535–544

    Google Scholar 

  26. Demir F, Sengur A, Bajaj V (2020) Convolutional neural networks based efficient approach for classification of lung diseases. Health Info Sci Sys 8(1):1–8

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Department of Science and Technology, the Ministry of Science and Technology, India (Grant No. DST / BDTD / EAG / 2017).

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Authors and Affiliations

  1. Centre for Advanced Studies, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, India

    Neeraj Baghel & Malay Kishore Dutta

  2. Fortis Lung Center, Fortis Hospital, Vasant Kunj, New Delhi, India

    Vivek Nangia

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  1. Neeraj Baghel
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  2. Vivek Nangia
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  3. Malay Kishore Dutta
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Correspondence to Malay Kishore Dutta.

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Baghel, N., Nangia, V. & Dutta, M.K. ALSD-Net: Automatic lung sounds diagnosis network from pulmonary signals. Neural Comput & Applic 33, 17103–17118 (2021). https://doi.org/10.1007/s00521-021-06302-1

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