Artificial Intelligence–Based COVID-19 Detection Using Cough Records
Yıl: 2021 Cilt: 21 Sayı: 2 Sayfa Aralığı: 203 - 208 Metin Dili: İngilizce DOI: 10.5152/electrica.2021.21005 İndeks Tarihi: 29-07-2022
Artificial Intelligence–Based COVID-19 Detection Using Cough Records
Öz: In 2019, with the emergence of coronavirus disease 2019 (COVID-19) and its spread all over the world, many people were directly affected by thepandemic. As its spread increases, it is difficult to diagnose who is actually infected. In addition to continuing vaccination studies, some technologicalsolutions are being used to try to control the virus. One of these technological solutions is presented in this study. The disease is detected using coughdata through artificial intelligence (AI). To do this, an open source data set was used from the opensigma.mit.edu website. More than 20,000 coughrecords representing age, gender, geographic location, and COVID-19 status are available from this site. The AI model trained on cough detectionachieved 79% COVID-19 accuracy with an F1 of 80%. With the designed AI-based mobile application, COVID-19 can be detected and monitored.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
- 1. W. H. Organization, “Novel Coronavirus (2019-nCoV) SITUATION REPORT - 1,” World Health Organization, Geneva, 2019.
- 2. C. Huang, Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu, L. Zhang, G. Fan, J. Xu, C. X. Gu, Z. Cheng, T. Yu, J. Xia, Y. Wei, W. Wu, X. Xie, W. Yin, H. Li, “Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China,” The Lancet, vol. 395, no. 10223, pp. 497-506, 2020.
- 3. W. H. Organization, “Coronavirus disease 2019 (COVID-19) Situation Report - 90,” World Health Organization, Genova, 2020
- 4. N. Health, “The Official Website of the City of New York,” 15 04 2020. [Online]. Available: https://www1.nyc.gov/assets/ doh/downloads/pdf/imm/covid-19-daily-data-summarydeaths-04152020-1.pdf. [Accessed 08 01 2021].
- 5. T. T. Dernegi, “Turk Toraks Dernegi,” 18 11 2016. [Online]. Available: https://www.toraks.org.tr/site/news/3402. [Accessed 08 01 2021]. 6. Y. Y. Shih, K. Fang, “Effects of network quality attributes on customer adoption intentions of Internet banking”. Total Quality Management, vol. 17, no 1, page, 61-77, 2006. [Crossref]
- 7. R. DeYoung, W. W. Lang, D. L. Nolle, “How the Internet affects outputs and performance at community banks”, Journal of Banking & Finance, vol. 31, no 4, pages 1033-1060, 2007. [Crossref]
- 8. E. Leroux, H. Rivas, “Mobile health without borders, evidence-based mHealth”, Stanford University; 2014.
- 9. I. K. Mahardika, T. Hamzah, T. Rahmawati, L. Soetjiatie, “Measuring Respiration Rate Based Android”, IJEEEMI, vol. 1, no. 1, pp. 39-44, Aug. 2019. [Crossref]
- 10. Y. J. Park, H. S. Cho, H. K. Lyu, “A method of detection of respiration rate on android using uwb impulse radar”, ICT Express, vol. 2, no. 145, pages 83149, 2016. [Crossref]
- 11. L. Orlandic, T. Teijeiro, D. Atienza, “The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms (Version 1.0) [Data set]”, Zenodo, 2020. [Crossref]
- 12. R. X. A. Pramono, S. A. Imtiaz, E. Rodriguez-Villegas, ‘’Automatic cough detection in acoustic signal using spectral features. In: 2019 41st annual international”, Conference of the IEEE Engineering in Medicine and biology society. EMBC, pages 7153-7156, 2019.
- 13. I. D. Miranda, A. H. Diacon, T. R. Niesler, ‘’A comparative study of features for acoustic cough detection using deep architectures”, In: 41st annual international Conference of the IEEE Engineering in Medicine and biology society. EMBC), pages. 2601-2605, 2019.
- 14. M. Solinski, M. Łepek, Ł. Kołtowski, ‘’Automatic cough detection based on airflow signals for portable spirometry system”, Informatics in Medicine Unlocked, pages 100313, 2020. [Crossref]
- 15. J. Laguarta, F. Hueto, B. Subirana, “COVID-19 Artificial Intelligence Diagnosis using only Cough Recordings,” IEEE Open Journal of Engineering in Medicine and Biology, doi: 10.1109/ OJEMB.2020.3026928.[Crossref]
- 16. A. Imran, I. Posokhova, H. N. Qureshi, U. Masood, S. Riaz, K. Ali, C. N. John, I. Hussain, M. Nabeel, “Ai4covid-19: Ai enabled preliminary diagnosis for covid-19 from cough samples via an app,” Informatics in Medicine Unlocked. ArXiv preprint arXiv, 2004.01275 (2020). [Crossref]
- 17. B. Subirana, F. Hueto, P. Rajasekaran, J. Laguarta, S. Puig, J. Malvehy, O. Mitja, A. Trilla, C. I. Moreno, J. F. M. Valle, E.M. González, B. Vizmanos and S. Sarma, “Hi sigma, do I have the coronavirus?: Call for a new artificial intelligence approach to support health care professionals dealing with the covid-19 pandemic,” arXiv preprint arXiv:2004.06510, 2020.
- 18. M. Pahar, M. Klopper, R. Warren, T. Niesler, “COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings”, arXiv: 2012.01926, Dec 2020.
- 19. A. Pal, M. Sankarasubbu, “Pay Attention to the cough: Early Diagnosis of COVID-19 using Interpretable Symptoms Embeddings with Cough Sound Signal Processing”, arXiv: 2010.02417, 2020.
- 20. C. Brown, J. Chauhan, A. Grammenos, J. Han, A. Hasthanasombat, D. Spathis, T. Xia, “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, pages 3474-3484, Aug. 2020. [Crossref]
- 21. L. Wang, A. Wong, ‘’COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images”, arXiv preprint arXiv:2003.09871vol. 1., 2020. [Crossref]
- 22. A. Narin, C. Kaya, Z. Pamuk, ‘’Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks”, arXiv preprint arXiv:2003.10849, 2020.
- 23. E. E. D. Hemdan, M. A. Shouman, M. E. Karar, “COVIDX-Net: a framework of deep learning classifiers to diagnose COVID-19 in x-ray images”, arXiv preprint arXiv:2003.11055, 2020.
- 24. L. Li, L. Qin, Z. Xu, Y. Yin, X. Wang, B. Kong, J. Bai, Y. Lu, Z. Fang, Q. Song, ‘’Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT”, Radiology 200905, 2020.
- 25. X. Mei, H. C. Lee, K. Y. Diao, M. Huang, B. Lin, C. Liu, Z. Xie, Y. Ma, P. M. Robson, M. Chung, “Artificial intelligence-enabled rapid diagnosis of patients with covid-19”, Nature Medicine, volume 26, pages 1224-1228, 2020. [Crossref]
- 26. B. Subirana, F. Hueto, P. Rajasekaran, J. Laguarta, S. Puig, J. Malvehy, O. Mitja, A. Trilla, C. I. Moreno, J. F. M. Valle, A. E. Mercado González, B. Vizmanos, S. Sarma, “Hi sigma, do I have the coronavirus?: Call for a new artificial intelligence approach to support health care professionals dealing with the COVID-19 pandemic,” arXiv:2004.06510, 2020.
- 27. J. Lyons, D. Yow-Bang Wang, G. H. Shteingart, E. Mavrinac, Y. Gaurkar, W. Watcharawisetkul, S. Birch, L. Zhihe, J. Hölzl, J. Lesinskis, H. Almér, C. Lord, A. Stark, (2020, January 14). jameslyons/python_ speech_features: release v0.6.1 (Version 0.6.1). Zenodo. http://doi. org/10.5281/zenodo.3607820
- 28. N. Chigozie, I. Winifred, A. Gachagan, S. Marshall, “Activation Functions: Comparison of trends in Practice and Research for Deep Learning”, 2020.
- 29. M. Xu, L. Y. Duan, J. Cai, L. T. Chia, C. Xu, Q. Tian. “HMM-Based Audio Keyword Generation”. In: Aizawa K., Nakamura Y., Satoh S. (eds) Advances in Multimedia Information Processing - PCM 2004. PCM 2004. Lecture Notes in Computer Science, vol 3333. Springer, Berlin, Heidelberg, 2004. [Crossref]
| APA | GÖKCEN A, KARADAĞ B, Riva C, BOYACI A (2021). Artificial Intelligence–Based COVID-19 Detection Using Cough Records. , 203 - 208. 10.5152/electrica.2021.21005 |
| Chicago | GÖKCEN Alpaslan,KARADAĞ Bulut,Riva Cengiz,BOYACI Ali Artificial Intelligence–Based COVID-19 Detection Using Cough Records. (2021): 203 - 208. 10.5152/electrica.2021.21005 |
| MLA | GÖKCEN Alpaslan,KARADAĞ Bulut,Riva Cengiz,BOYACI Ali Artificial Intelligence–Based COVID-19 Detection Using Cough Records. , 2021, ss.203 - 208. 10.5152/electrica.2021.21005 |
| AMA | GÖKCEN A,KARADAĞ B,Riva C,BOYACI A Artificial Intelligence–Based COVID-19 Detection Using Cough Records. . 2021; 203 - 208. 10.5152/electrica.2021.21005 |
| Vancouver | GÖKCEN A,KARADAĞ B,Riva C,BOYACI A Artificial Intelligence–Based COVID-19 Detection Using Cough Records. . 2021; 203 - 208. 10.5152/electrica.2021.21005 |
| IEEE | GÖKCEN A,KARADAĞ B,Riva C,BOYACI A "Artificial Intelligence–Based COVID-19 Detection Using Cough Records." , ss.203 - 208, 2021. 10.5152/electrica.2021.21005 |
| ISNAD | GÖKCEN, Alpaslan vd. "Artificial Intelligence–Based COVID-19 Detection Using Cough Records". (2021), 203-208. https://doi.org/10.5152/electrica.2021.21005 |
| APA | GÖKCEN A, KARADAĞ B, Riva C, BOYACI A (2021). Artificial Intelligence–Based COVID-19 Detection Using Cough Records. Electrica, 21(2), 203 - 208. 10.5152/electrica.2021.21005 |
| Chicago | GÖKCEN Alpaslan,KARADAĞ Bulut,Riva Cengiz,BOYACI Ali Artificial Intelligence–Based COVID-19 Detection Using Cough Records. Electrica 21, no.2 (2021): 203 - 208. 10.5152/electrica.2021.21005 |
| MLA | GÖKCEN Alpaslan,KARADAĞ Bulut,Riva Cengiz,BOYACI Ali Artificial Intelligence–Based COVID-19 Detection Using Cough Records. Electrica, vol.21, no.2, 2021, ss.203 - 208. 10.5152/electrica.2021.21005 |
| AMA | GÖKCEN A,KARADAĞ B,Riva C,BOYACI A Artificial Intelligence–Based COVID-19 Detection Using Cough Records. Electrica. 2021; 21(2): 203 - 208. 10.5152/electrica.2021.21005 |
| Vancouver | GÖKCEN A,KARADAĞ B,Riva C,BOYACI A Artificial Intelligence–Based COVID-19 Detection Using Cough Records. Electrica. 2021; 21(2): 203 - 208. 10.5152/electrica.2021.21005 |
| IEEE | GÖKCEN A,KARADAĞ B,Riva C,BOYACI A "Artificial Intelligence–Based COVID-19 Detection Using Cough Records." Electrica, 21, ss.203 - 208, 2021. 10.5152/electrica.2021.21005 |
| ISNAD | GÖKCEN, Alpaslan vd. "Artificial Intelligence–Based COVID-19 Detection Using Cough Records". Electrica 21/2 (2021), 203-208. https://doi.org/10.5152/electrica.2021.21005 |
