Analysis of rule-based and shallow statistical models for COVID-19 cough detection for a preliminary diagnosis

Authors

Arshia Arif, National University of Sciences and Technology, Islamabad.Follow
Eisa Alanazi, Umm Al-Qura UniversityFollow
Ayesha Zeb, National University of Sciences and Technology, Islamabad.Follow
Waqar Shahid Qureshi, Technological University DublinFollow

Author ORCID Identifier

https://orcid.org/0000-0003-0176-8145

Document Type

Conference Paper

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

1.2 COMPUTER AND INFORMATION SCIENCE, Computer Sciences, Information Science, Bioinformatics

Publication Details

The 13^th Asian Control Conference (ASCC 2022) will be held with a hybrid style in Jeju Island, Korea. 4th to 7th May 2022. The article has been accepted and the registration and final submission have been made.

https://doi.org/10.21427/awy5-3w92

Abstract

Coronavirus pandemic that has spread all over the world, is one of its kind in the recent past, that has mobilized researchers in areas such as (not limited to) pre-screening solutions, contact tracing, vaccine developments, and crowd estimation. Pre-screening using symptoms identification, cough classification, and contact tracing mobile applications gained significant popularity during the initial outbreak of the pandemic. Audio recordings of coughing individuals are one of the sources that can help in the pre-screening of COVID-19 patients. This research focuses on quantitative analysis of covid cough classification using audio recordings of coughing individuals. For analysis, we used three different publicly available datasets i.e., COUGHVID, NoCoCoDa, and a self-collected dataset through a web application. We observed that wet cough has more correlation with covid cough as opposed to dry cough. However, the classification model trained with wet and dry coughs, both, has similar test performance as that of the model trained with wet cough samples only. We conclude that audio-signal recordings of coughing individuals have the potential as a pre-screening test for COVID-19.

DOI

https://doi.org/10.21427/awy5-3w92

Recommended Citation

Arif, A., Alanazi, E., Zeb, A., & Qureshi, W. S. (2022). Analysis of rule-based and shallow statistical models for COVID-19 cough detection for a preliminary diagnosis. Technological University Dublin. DOI: 10.21427/AWY5-3W92

Funder

King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.