COVID-19 Detection Using Forced Cough Sounds and Medical Information

Authors

DOI:

https://doi.org/10.22456/2175-2745.126016

Keywords:

COVID-19 detection, Cough sounds, Deep Neural Networks

Abstract

The World Health Organization (WHO) has declared the novel coronavirus (COVID-19) outbreak a global pandemic in March 2020. Through a lot of cooperation and the effort of scientists, several vaccines have been created. However, there is no guarantee that the virus will shortly disappear, even if a large part of the population is vaccinated. Therefore, non-invasive methods, with low cost and real-time results, are important to detect infected individuals and enable earlier adequate treatment, in addition to preventing the spread of the virus. An alternative is using forced cough sounds and medical information to distinguish a healthy person from those infected with COVID-19 via artificial intelligence. An additional challenge is the unbalancing of these data, as there are more samples of healthy individuals than contaminated ones. We propose here a Deep Neural Network model to classify people as healthy or sick concerning COVID-19. We used here a model composed by an Convolutional Neural Network and two other Neural Networks with two full-connected layers, each one trained with different data from the same individual. To evaluate the performance of the proposed method, we combined two datasets from the literature: COUGHVID and Coswara. That dataset contains clinical information regarding previous respiratory conditions, symptoms (fever or muscle pain), and a cough record. The results show that our model is simpler (with fewer parameters) than those from the literature and generalizes better the prediction of infected individuals. The proposal presents an average Area Under the ROC Curve (AUC) equal to 0.885 with a confidence interval (0.881 - 0.888), while the literature reports 0.771 with (0.752 - 0.783).

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References

AZAM, M. A. et al. Smartphone based human breath analysis from respiratory sounds. In: 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). USA: IEEE, 2018. p. 445–448.

SIVIC, J.; ZISSERMAN, A. Efficient visual search of videos cast as text retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, USA, v. 31, n. 4, p. 591–606, April 2009.

BRAGA, D. et al. Automatic detection of parkinson's disease based on acoustic analysis of speech. Engineering Applications of Artificial Intelligence, United Kingdom, v. 77, p. 148–158, January 2019.

VASHKEVICH, M.; PETROVSKY, A.; RUSHKEVICH, Y. Bulbar als detection based on analysis of voice perturbation and vibrato. In: 2019 Signal Processing: Algorithms,

Architectures, Arrangements, and Applications (SPA). USA: IEEE, 2019. p. 267–272.

VHADURI, S. Nocturnal cough and snore detection using smartphones in presence of multiple background-noises. In: Proc. of the SIGCAS Conference on Computing and Sustainable Societies. New York, USA: ACM, 2020. p. 174–186.

BOTHA, G. et al. Detection of tuberculosis by automatic cough sound analysis. Physiological measurement, United Kingdom, v. 39, n. 4, p. 045005, April 2018.

CHAUDHARI, G. et al. Virufy: Global applicability of crowdsourced and clinical datasets for ai detection of covid-19 from cough. arXiv preprint arXiv:2011.13320, New York, USA, November 2020.

BROWN, C. et al. 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. USA: ACM, 2020. p. 3474–3484.

ORLANDIC, L.; TEIJEIRO, T.; ATIENZA, D. The coughvid crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms. Scientific Data, London, United Kingdom, v. 8, n. 1, p. 1–10, June 2021.

SHARMA, N. et al. Coswara–a database of breathing, cough, and voice sounds for covid-19 diagnosis. arXiv preprint arXiv:2005.10548, New York, USA, 2020.

HAN, J. et al. Sounds of covid-19: exploring realistic performance of audio-based digital testing. npj Digital Medicine, USA, v. 5, n. 1, p. 1–9, January 2022.

LAGUARTA, J.; HUETO, F.; SUBIRANA, B. Covid-19 artificial intelligence diagnosis using only cough recordings. IEEE Open Journal of Engineering in Medicine and Biology, New York, USA, v. 1, p. 275–281, September 2020.

MCFEE, B. et al. librosa: Audio and music signal analysis in python. In: Proceedings of the 14th python in science conference. USA: Citeseer, 2015. v. 8, p. 18–25.

PRECHELT, L. Early stopping-but when? In: Neural Networks: Tricks of the trade. Berlin, Germany: Springer, 1998. p. 55–69.

BRADLEY, A. P. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition, United Kingdom, v. 30, n. 7, p. 1145–1159, July 1997.

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Published

2023-01-30

How to Cite

de Souza, L., Bernardino, H., de Souza, J., & Vieira, A. (2023). COVID-19 Detection Using Forced Cough Sounds and Medical Information. Revista De Informática Teórica E Aplicada, 30(1), 44–52. https://doi.org/10.22456/2175-2745.126016

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Section

Regular Papers