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Robust respiratory disease classification using breathing sounds (RRDCBS) multiple features and models

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Abstract

Classification of respiratory diseases using X-ray and CT scan images of lungs is currently practised and used by many medical practitioners for clinical diagnosis. Respiratory disease classification, using breathing and wheezing sounds, remains scarce in the research field and is slowly upcoming. In this work, robust respiratory disease classification using breathing sounds (RRDCBS) is implemented by extracting multiple features from sounds, creating multiple modelling techniques, and experimental identification of diseases using appropriate testing procedures for multi-class and binary classification of respiratory diseases. Decision level fusion of features for Vector quantisation (VQ) modelling technique has provided 100% accuracy for classifying five respiratory diseases and healthy subjects. Decision level fusion of indices on the features has provided 100% accuracy for VQ, support vector machine (SVM), and K-nearest neighbour (KNN) modelling techniques to perform binary classification of the respiratory disease against healthy data sound. Deep recurrent and convolutional neural networks are also evaluated for multiple/binary classification of respiratory diseases.

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References

  1. Demir F, Sengur A, Bajaj V (2020) Convolutional neural networks based efficient approach for classification of lung diseases. Health Inf Sci Syst 8(4):1–8

    Google Scholar 

  2. Haider NS, Singh BK, Periyasamy R, Behera AK (2019) Respiratory sound based classification of chronic obstructive pulmonary disease: a risk stratification approach in machine learning paradigm. J Med Syst. https://doi.org/10.1007/s10916-019-1388-0

    Article  Google Scholar 

  3. Chen Q, Zhang W, Tiany X, Zhang X, Chen S and Lei W (2017) Automatic heart and lung sounds classification using convolutional neural networks,asia-pacific signal and information processing association summit and conference, 2017, pp.1–4

  4. Aykanat M, Kılıç Ö, Kurt B, Saryal S (2017) Classification of lung sounds using convolutional neural networks. EURASIP J Image Video Process 65:1–9

    Google Scholar 

  5. Pramono RXA, Imtiaz SA, Rodriguez-Villegas E (2019) Evaluation of features for classification of wheezes and normal respiratory sounds, 1–21. https://doi.org/10.1371/journal.pone.021365

  6. Mondal A, Bhattacharya P, Saha G (2014) Detection of lung status using morphological complexities of respiratory sounds. The Sci World J. https://doi.org/10.1155/2014/182938

    Article  Google Scholar 

  7. Bardou D, Zhang K, Ahamad SM (2018) Lung sound classification using convolutional neural networks. Artif Intell Med 58–69

  8. Nabi FG, Sundarraj K, Lam CK (2019) Identification of asthma severity levels through wheeze sounds, characterisation and classification using integrated power features. Biomed Sig Process Control 52:302–311. https://doi.org/10.1016/j.bspc.2019.04.018

    Article  Google Scholar 

  9. Rao A, Huynh E, Royston TJ, Kornblith A, Roy S (2019) Acoustic methods for pulmonary diagnosis. IEEE Rev Biomed Eng 12:22–39. https://doi.org/10.1109/RBME.2018.2874353

    Article  Google Scholar 

  10. Chen H, Yuan X, Pei Z, Li M, Jianqing L (2019) Triple-classification of respiratory sounds using optimised s-transform and deep residual. Networks 7:32845–32852

    Google Scholar 

  11. Wisniewski M, Zielinski TP (2015) Joint Application of audio spectral envelope and tonality index in an E-Asthma monitoring system. IEEE J Biomed Health Inform 19:1009–1018

    Google Scholar 

  12. De La Torre Cruz J, Quesada FJC, Reyes NR, Galán SG, Orti JJC, Chica GP (2021) Monophonic and polyphonic wheezing classification based on constrained low-rank non-negative matrix factorization. Sensors 21:1661

    Article  Google Scholar 

  13. Revathi A, Sasikaladevi N, Nagakrishnan R (2018) Robust emotion recognition from speech: gamma tone features and models. Int J Speech Technol 21:723–739. https://doi.org/10.1007/s10772-018-9546-1

    Article  Google Scholar 

  14. Nabi FG, Sundarraj K, Lam CK, Palaniappan R (2019) Characterisation and classification of asthmatic wheeze sound according to severity level using spectral integrated feature. Comput Biol Med 104:52–61

    Article  Google Scholar 

  15. Aida-zade K, Xocayev A and Rustamov S (2016) Speech recognition using Support Vector Machines. In: 2016 IEEE 10th international conference on application of information and communication technologies (AICT), pp. 1–4. https://doi.org/10.1109/ICAICT.2016.7991664

  16. Millicevic M, Mazic I, Bonkovic M (2016) Asthmatic wheezes detection – what contributes the most to the role of MFCC in classifiers accuracy. Int J Biol Biomed Eng 10:176–182

    Google Scholar 

  17. Albuquerque RQ, Mello CAB (2021) Automatic no-reference speech quality assessment with convolutional neural networks. Neural Comput Appl. https://doi.org/10.1007/s00521-021-05767-4

    Article  Google Scholar 

  18. Hewamalage H, Bergmeir C, Bandara K (2021) Recurrent neural networks for time series forecasting: current status and future directions. Int J Forecast 37(1):388–427

    Article  Google Scholar 

  19. Nabi FG, Sundaraj K, Lam CK, Palaniappan R (2019) Analysis of wheeze sounds during tidal breathing according to severity levels in asthma patients. J Asthma 1

  20. Arunachalam R (2019) A strategic approach to recognise the speech of the children with hearing impairment: different sets of features and models. Multimed Tools Appl 78:20787–20808. https://doi.org/10.1007/s11042-019-7329-6

    Article  Google Scholar 

  21. Revathi A, Sasikaladevi N (2019) Hearing impaired speech recognition: stockwell features and models. Int J Speech Technol 22:979–991

    Article  Google Scholar 

  22. Rabiner L, Juang BH (1993) Fundamentals of speech recognition. Prentice Hall, New Jersy

    Google Scholar 

  23. Abdel-Hamid O, Mohamed A-R, Jiang H, Deng Li, Penn G, Dong Yu (2014) Convolutional neural networks for speech recognition. IEEE/ACM Trans Audio Speech Lang Process 22(10):1533–1545. https://doi.org/10.1109/TASLP.2014.2339736

    Article  Google Scholar 

  24. Telmem M, Ghanou Y (2021) The convolutional neural networks for Amazigh speech recognition system. Telkomnika (Telecommun Comput Electron Control) 19(2):515–522. https://doi.org/10.12928/TELKOMNIKA.v19i2.16793

    Article  Google Scholar 

  25. Vázquez-Romero A, Gallardo-Antolín A (2020) Automatic detection of depression in speech using ensemble convolutional neural networks. Entropy. https://doi.org/10.3390/e22060688

    Article  Google Scholar 

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Acknowledgements

Authors thank the Department of Science & Technology, New Delhi, for the FIST funding (SR/FST/ET-I/2018/221(C)). The authors wish to express their sincere thanks to the SASTRA Deemed University, Thanjavur, India, for extending infrastructural support to carry out this work.

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Authors and Affiliations

  1. School of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur, 613 401, India

    A. Revathi & Rengarajan Amirtharajan

  2. School of Computing, SASTRA Deemed University, Thanjavur, 613 401, India

    N. Sasikaladevi

  3. Institute of Child Health & Research Centre, Madurai Medical College, Madurai, 625020, India

    D. Arunprasanth

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  1. A. Revathi
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  2. N. Sasikaladevi
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  3. D. Arunprasanth
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  4. Rengarajan Amirtharajan
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Correspondence to Rengarajan Amirtharajan.

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Revathi, A., Sasikaladevi, N., Arunprasanth, D. et al. Robust respiratory disease classification using breathing sounds (RRDCBS) multiple features and models. Neural Comput & Applic 34, 8155–8172 (2022). https://doi.org/10.1007/s00521-022-06915-0

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