Efficient Covid-19 disease diagnosis based on cough signal processing and supervised machine learning

• Autorzy: Bensid Khaled , Lati Abdelhai , Benlamoudi Azeddine , Ghouar Brahim Elkhalil , Senoussi Mohammed Larbi

Identyfikatory

DOI

10.29354/diag/156751

• Języki publikacji: EN

Abstrakty

EN

The spread of the coronavirus has claimed the lives of millions worldwide, which led to the emergence of an economic and health crisis at the global level, which prompted many researchers to submit proposals for early diagnosis of the coronavirus to limit its spread. In this work, we propose an automated system to detect COVID-19 based on the cough as one of the most important infection indicators. Several studies have shown that coughing accounts for 65% of the total symptoms of infection. The proposed system is mainly based on three main steps: first, cough signal detection and segmentation; second, cough signal extraction; and third, three techniques of supervised machine learning-based classification: Support Vector Machine (SVM), KNearest Neighbours (KNN), and Decision Tree (DT). Our proposed system showed high performance through good accuracy values, where the best accuracy for classifying female coughs was 99.6% using KNN and 88% for males using SVM.

Słowa kluczowe

EN

voice disease; Covid-19; cough sounds; features extraction; classification

PL

Covid-19; ekstrakcja cech; klasyfikacja; kaszel; przetwarzanie sygnału; uczenie maszynowe

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2023
• Tom: Vol. 24, No. 1
• Strony: art. no. 2023103
• Opis fizyczny: Bibliogr. 24 poz., rys., tab.

Twórcy

autor

Bensid Khaled

• Faculty of New information and Communication Technologies University Kasdi Merbah Ouargla, BP 511, 30000, Ouargla, Algeria

• https://orcid.org/0000-0001-8502-907X

autor

Lati Abdelhai

• Faculty of New information and Communication Technologies University Kasdi Merbah Ouargla, BP 511, 30000, Ouargla, Algeria

autor

Benlamoudi Azeddine

• Faculty of New information and Communication Technologies University Kasdi Merbah Ouargla, BP 511, 30000, Ouargla, Algeria

autor

Ghouar Brahim Elkhalil

• Faculty of New information and Communication Technologies University Kasdi Merbah Ouargla, BP 511, 30000, Ouargla, Algeria

autor

Senoussi Mohammed Larbi

• Faculty of New information and Communication Technologies University Kasdi Merbah Ouargla, BP 511, 30000, Ouargla, Algeria

Bibliografia

• 1. Pramono RX, Imtiaz SA, Rodriguez-Villegas E. Automatic cough detection in acoustic signal using spectral features. In2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2019:7153-7156. https://doi.org/10.1109/EMBC.2019.8857792.
• 2. Alqudaihi KS, Aslam N, Khan IU, Almuhaideb AM, Alsunaidi SJ, Ibrahim NM, Alhaidari FA, Shaikh FS, Alsenbel YM, Alalharith DM, Alharthi HM. Cough sound detection and diagnosis using artificial intelligence techniques: challenges and opportunities. Ieee Access. 2021 15; 9:102327-44. https://doi.org/10.1109/ACCESS.2021.3097559.
• 3. World Health Organization. Coronavirus disease 2019 (covid-19): situation report. 2020;51.
• 4. 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 2020; arXiv:2005.10548. https://doi.org/10.48550/arXiv.2005.10548.
• 5. Brown C, Chauhan J, Grammenos A, Han J, Hasthanasombat A, Spathis D, Xia T, Cicuta P, Mascolo, C. Exploring automatic diagnosis of COVID-19 from crowdsourced respiratory sound data. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual. 2021:3474-3484. https://doi.org/10.48550/arXiv.2006. 05919.
• 6. Despotovic V, Ismael M, Cornil M, Mc Call R, Fagherazzi G. Detection of COVID-19 from voice, cough and breathing patterns: Dataset and preliminary results. Comput. Biol. Med. 2021;138: 104944. https://doi.org/10.1016/j.compbiomed.2021.104944.
• 7. Islam R, Abdel-Raheem E, Tarique M. A study of using cough sounds and deep neural networks for the early detection of COVID-19. Biomed. Eng. Adv. 2022;3:100025. https://doi.org/10.1016/j.bea.2022.100025.
• 8. Rahman T, Ibtehaz N, Khandakar A, Hossain MSA, Mekki YMS, Ezeddin M, Bhuiyan EH, Ayari MA, Tahir A, Qiblawey Y. An intelligent application to detect COVID-19 patients using cough and breath sounds. Diagnostics. 2022; 12:920. https://doi.org/10.3390/diagnostics12040920.
• 9. Morice AH, Fontana GA, Belvisi MG, et al. ERS guidelines on the assessment of cough, European Respiratory Journal. 2007;29(6):1256-1276. https://doi.org/10.1183/09031936.00101006.
• 10. Islam Rumana, Abdel-Raheem Esam, Tarique Mohammed. Early detection of COVID-19 patients using chromagram features of cough sound recordings with machine learning algorithms. In: 2021 International Conference on Microelectronics (ICM). IEEE. 2021:82-85. https://doi.org/10.1109/ICM52667.2021.9664931.
• 11. Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B. Support vector machines. IEEE Intelligent Systems and their applications. 1998;13(4):18-28. https://doi.org/10.1109/5254.708428.
• 12. Huynh TH, Tran VA, Tran HD. Semi-supervised tree support vector machine for online cough recognition. In Twelfth Annual Conference of the International Speech Communication Association 2011.
• 13. Hadjaidji E, Korba M, Khelil K. Spasmodic dysphonia detection using machine learning classifiers. In Inter Conference on Recent Advances in Mathematics and Infor (ICRAMI). 2021;1-5. https://doi.org/10.1109/ICRAMI52622.2021.95859 20.
• 14. Rajani Kumari LV, Padma Sai Y. Classification of arrhythmia beats using optimized K-nearest neighbor classifier. InIntelligent Systems. 2021:349-359. https://doi.org/10.1007/978-981-33-6081-5_31.
• 15. Brian Kulis. Metric Learning: A Survey. Foundations and Trends® in Machine Learning: 2013;5(4):287-364. https://doi.org/10.1561/2200000019.
• 16. Khorshid SF, Abdulazeez AM. Breast cancer diagnosis based on k-nearest neighbors: A review. Journal of Archaeology of Egypt/Egyptology. 2021;15;18(4):1927-51. https://archives.palarch.nl/index.php/jae/article/vie w/6601.
• 17. Sipper M, Moore JH. Conservation machine learning: a case study of random forests. Scientific Reports. 2021;11;11(1):1-6. https://doi.org/10.1038/s41598-021-83247-4.
• 18. Zhang, Z. Introduction to machine learning: knearest neighbors. Annals of translational medicine. 2016;4(11). https://doi.org/10.21037/atm.2016.03.37.
• 19. Usman M, Zubair M, Ahmad Z, Zaidi M, Ijyas T, Parayangat M, Wajid M, Shiblee M, Ali SJ. Heart rate detection and classification from speech spectral features using machine learning. Archives of Acoustics, 2021;46. https://doi.org/10.24425/aoa.2021.136559.
• 20. Goswami S, Pramanick R, Patra A, Rath SP, Foltin M, Ariando A, Thompson D, Venkatesan T, Goswami S, Williams RS. Decision trees within a molecular memristor. Nature. 2021;597(7874):51-6. https://doi.org/10.1038/s41586-021-03748-0.
• 21. Charbuty B, Abdulazeez A. Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends. 2021;24;2(01):20-28. https://doi.org/10.38094/jastt20165.
• 22. Bilski P, Bobiński P, Krajewski A, Witomski P. Detection of wood boring insects’ larvae based on the acoustic signal analysis and the artificial intelligence algorithm. Archives of Acoustics. 2016;42(1):61-70. https://doi.org/10.1515/aoa2017-0007.
• 23. Zhiyong G, Jiwu L, Rongxi W. Prognostics uncertainty reduction by right-time prediction of remaining useful life based on hidden Markov model and proportional hazard model. Eksploatacja i Niezawodność. 2021;23(1). https://doi.org/10.17531/ein.2021.1.16.
• 24. Pizzo DT, Esteban S, Scetta M. IATos: AI-powered pre-screening tool for COVID-19 from cough audio samples. arXiv preprint arXiv: 2021;2104:13247. https://doi.org/10.48550/arXiv.2104.13247.

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).