A cough-based COVID-19 detection with gammatone and Mel-frequency cepstral coefficients

• Autorzy: Benmalek Elmehdi , El Mhamdi Jamal , Jilbab Abdelilah , Jbari Atman

Identyfikatory

DOI

10.29354/diag/166330

• Języki publikacji: EN

Abstrakty

EN

Many countries have adopted a public health approach that aims to address the particular challenges faced during the pandemic Coronavirus disease 2019 (COVID-19). Researchers mobilized to manage and limit the spread of the virus, and multiple artificial intelligence-based systems are designed to automatically detect the disease. Among these systems, voice-based ones since the virus have a major impact on voice production due to the respiratory system's dysfunction. In this paper, we investigate and analyze the effectiveness of cough analysis to accurately detect COVID-19. To do so, we distinguished positive COVID patients from healthy controls. After the gammatone cepstral coefficients (GTCC) and the Mel-frequency cepstral coefficients (MFCC) extraction, we have done the feature selection (FS) and classification with multiple machine learning algorithms. By combining all features and the 3-nearest neighbor (3NN) classifier, we achieved the highest classification results. The model is able to detect COVID-19 patients with accuracy and an f1-score above 98 percent. When applying FS, the higher accuracy and F1-score were achieved by the same model and the ReliefF algorithm, we lose 1 percent of accuracy by mapping only 12 features instead of the original 53.

Słowa kluczowe

EN

COVID-19; cough recordings; machine learning; mel-frequency cepstral coefficients; gammatone cepstral coefficients; feature selection

PL

COVID-19; uczenie maszynowe; współczynniki mel-cepstralne

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2023
• Tom: Vol. 24, No. 2
• Strony: art. no. 2023214
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Benmalek Elmehdi

• E2SN, ENSAM de Rabat, Mohammed V University in Rabat, Morocco

• https://orcid.org/0000-0003-1078-1421

autor

El Mhamdi Jamal

• E2SN, ENSAM de Rabat, Mohammed V University in Rabat, Morocco

• https://orcid.org/0000-0001-8219-3560

autor

Jilbab Abdelilah

• E2SN, ENSAM de Rabat, Mohammed V University in Rabat, Morocco

• https://orcid.org/0000-0002-1577-9040

autor

Jbari Atman

• E2SN, ENSAM de Rabat, Mohammed V University in Rabat, Morocco

• https://orcid.org/0000-0002-1855-2503

Bibliografia

• 1. Wu Z, McGoogan JM (2020). Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. JAMA 2019; 323(13): 1239-1242. https://doi.org/10.1001/jama.2020.2648.
• 2. Guan WJ, Ni Z, Hu Y, Liang WH, Ou CQ, He JX, Liu L, Shan H, Lei CL, Hui DSC, Du B, Li LJ. Clinical characteristics of coronavirus disease 2019 in China. New England Journal of Medicine 2020; 382(18): 1708-1720. https://doi.org/10.1056/NEJMoa2002032.
• 3. Wang W, Xu Y, Gao R, Lu R, Han K, Wu G, Tan W. Detection of SARS-CoV-2 in different types of clinical specimens. Jama 2020; 323(18): 1843-1844. https://doi.org/10.1001/jama.2020.3786.
• 4. Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, Xia L. (2020). Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 2020; 296(2): 32-40. https://doi.org/10.1148/radiol.2020200642.
• 5. Mei X, Lee HC, Diao KY, Huang M, Lin B, Liu C, Yang Y. Artificial intelligence–enabled rapid diagnosis of patients with COVID-19. Nature medicine 2020; 26(8): 1224-1228. https://doi.org/10.1038/s41591-020-0931-3.
• 6. Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, van Smeden M. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020; 369. https://doi.org/10.1136/bmj.m1328.
• 7. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Cao B. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet 2020; 395(10223): 497-506. https://doi.org/10.1016/S0140-6736(20)30183-5.
• 8. Fan DP, Zhou T, Ji GP, Zhou Y, Chen G, Fu H, Shao L. Inf-net: Automatic covid-19 lung infection segmentation from CT images. IEEE Transactions on Medical Imaging 2020; 39(8): 2626-2637. https://doi.org/10.1109/TMI.2020.2996645.
• 9. Pereira RM, Bertolini D, Teixeira LO, Silla Jr CN, Costa YM. COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Computer methods and programs in biomedicine 2020; 194: 105532. https://doi.org/10.1016/j.cmpb.2020.105532.
• 10. Benmalek E, Elmhamdi J, Jilbab A. Comparing CT scan and chest X-ray imaging for COVID-19 diagnosis. Biomedical Engineering Advances 2021; 1: 100003. https://doi.org/10.1016/j.bea.2021.100003.
• 11. El Asnaoui K, Chawki Y. Using X-ray images and deep learning for automated detection of coronavirus disease. Journal of Biomolecular Structure and Dynamics 2021; 39(10): 3615-3626. https://doi.org/10.1080/07391102.2020.1767212.
• 12. Brown C, Chauhan J, Grammenos A., Han J, Hasthanasombat A, Spathis D, Mascolo C. Exploring automatic diagnosis of COVID-19 from crowdsourced respiratory sound data. arXiv preprint arXiv 2020; 05919. https://doi.org/10.48550/arXiv.2006.05919.
• 13. Huang Y, Meng S, Zhang Y, Wu S, Zhang Y, Zhang Y, Cai J. The respiratory sound features of COVID19 patients fill gaps between clinical data and screening methods. MedRxiv 2020. https://doi.org/10.1101/2020.04.07.20051060.
• 14. Xu Z, Shi L, Wang Y, Zhang J, Huang L, Zhang C, Wang FS. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. The Lancet respiratory medicine 2020, 8(4), 420-422. https://doi.org/10.1016/s2213-2600(20)30076-x.
• 15. Pan F, Ye T, Sun P, Gui S, Liang B, Li L, Zheng C. (2020). Time course of lung changes on chest CT during recovery from 2019 novel coronavirus (COVID-19) pneumonia. Radiology 2020. https://doi.org/10.1148%2Fradiol.2020200370.
• 16. Han J, Brown C, Chauhan J, Grammenos A, Hasthanasombat A, Spathis D, Mascolo C. (2021, June). Exploring automatic COVID-19 diagnosis via voice and symptoms from crowdsourced data. ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021; 8328-8332. https://doi.org/10.1109/ICASSP39728.2021.941457 6.
• 17. Kumar LK, Alphonse PJA. Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: cough, voice, and breath. Alexandria Engineering Journal 2022; 61(2): 1319-1334. https://doi.org/10.1016/j.aej.2021.06.024.
• 18. Sharma N, Krishnan P, Kumar R, Ramoji S, Chetupalli SR, Ghosh PK, Ganapathy S. Coswara-a database of breathing, cough, and voice sounds for COVID-19 diagnosis. arXiv preprint arXiv:2005.10548 2022. https://doi.org/10.48550/arXiv.2005.10548.
• 19. Valero X, Alias F. Gammatone cepstral coefficients: Biologically inspired features for non-speech audio classification. IEEE Transactions on Multimedia 2012; 14(6):1684-1689. https://doi.org/10.1109/TMM.2012.2199972.
• 20. Liu JM, You M, Li GZ, Wang Z, Xu X, Qiu Z, Chen S. Cough signal recognition with gammatone cepstral coefficients. In 2013 IEEE China Summit and International Conference on Signal and Information Processing 2013; 160-164. https://doi.org/10.1109/ChinaSIP.2013.6625319.
• 21. Haton JP, Cerisara C, Fohr D, Laprie Y, Smaïli K. Reconnaissance automatique de la parole: Du Signal à son Interprétation. Dunod 2006; 392.
• 22. Slaney M. An efficient implementation of the Patterson-Holdsworth auditory filter bank. Apple Computer, Perception Group, Technical Report 1997; 35(8).
• 23. Moore BC, Glasberg BR. A revision of Zwicker's loudness model. Acta Acustica united with Acustica 1996; 82(2): 335-345.
• 24. Indyk P, Motwani R. Approximate nearest neighbors: towards removing the curse of dimensionality. Proceedings of the thirtieth annual ACM symposium on Theory of computing 1998; 604-613. https://doi.org/10.1145/276698.276876.
• 25. Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on Computational learning theory 1992; 144-152. https://doi.org/10.1145/130385.130401.
• 26. Quinlan JR. Induction of decision trees. Machine learning 1986; 1(1): 81-106. https://doi.org/10.1007/BF00116251.
• 27. Dietz WE, Kiech EL, Ali M. (1989, January). Classification of data patterns using an autoassociative neural network topology. International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems 1989; 2: 1028-1036. https://doi.org/10.1145/67312.67378.
• 28. Peng H, Long F, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on pattern analysis and machine intelligence 2005; 27(8): 1226-1238. https://doi.org/10.1109/TPAMI.2005.159.
• 29. Kira K, Rendell LA. A practical approach to feature selection. Machine Learning Proceedings 1992; 249-256. https://doi.org/10.1016/B978-1-55860-247-2.50037-1.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).