TY - GEN
T1 - COVID-19 Recognition Based on Patient's Coughing and Breathing Patterns Analysis
T2 - 29th Conference of Open Innovations Association FRUCT, FRUCT 2021
AU - Khriji, Lazhar
AU - Ammari, Ahmed
AU - Messaoud, Seifeddine
AU - Bouaafia, Soulef
AU - Maraoui, Amna
AU - MacHhout, Mohsen
N1 - Funding Information:
This work was funded by OMANTEL under grant number “EG/SQU-OT/18/01”. The authors, therefore, acknowledge OMANTEL and Sultan Qaboos University for their financial support.
Publisher Copyright:
© 2021 FRUCT.
PY - 2021/5/12
Y1 - 2021/5/12
N2 - The World Health Organization has declared that the new Coronavirus disease (Covid-19) has become a pandemic since March 2020. It consists of an emerging viral infection with respiratory swelling that can progress to atypical pneumonia. In fact, experts stress the early detection importance of those infected with COVID-19 virus. In this way, the infected patients will be isolated from others, and then prevent the virus spread. However, prompt assessment of breathing patterns is important for many medical emergencies. We present, in this paper, a deep learning technique-based COVID-19 cough and breath analysis that can recognize positive COVID-19 cases from both negative and healthy COVID-19 cough and breath recorded on smartphones or wearable sensors. Firstly, audio signals, as well as cough and breath, will be preprocessed to remove noise. After that, deep features will be extracted using the deep Long Term Short Memory (LSTM) model. Finally, the recognition step will be performed exploiting extracted audio features. Numerical results prove the efficiency of the proposed deep model in terms of high accuracy level and low loss value compared to the other techniques.
AB - The World Health Organization has declared that the new Coronavirus disease (Covid-19) has become a pandemic since March 2020. It consists of an emerging viral infection with respiratory swelling that can progress to atypical pneumonia. In fact, experts stress the early detection importance of those infected with COVID-19 virus. In this way, the infected patients will be isolated from others, and then prevent the virus spread. However, prompt assessment of breathing patterns is important for many medical emergencies. We present, in this paper, a deep learning technique-based COVID-19 cough and breath analysis that can recognize positive COVID-19 cases from both negative and healthy COVID-19 cough and breath recorded on smartphones or wearable sensors. Firstly, audio signals, as well as cough and breath, will be preprocessed to remove noise. After that, deep features will be extracted using the deep Long Term Short Memory (LSTM) model. Finally, the recognition step will be performed exploiting extracted audio features. Numerical results prove the efficiency of the proposed deep model in terms of high accuracy level and low loss value compared to the other techniques.
UR - http://www.scopus.com/inward/record.url?scp=85107420779&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85107420779&partnerID=8YFLogxK
U2 - 10.23919/FRUCT52173.2021.9435454
DO - 10.23919/FRUCT52173.2021.9435454
M3 - Conference contribution
AN - SCOPUS:85107420779
T3 - Conference of Open Innovation Association, FRUCT
SP - 185
EP - 191
BT - Proceedings of the 29th Conference of Open Innovations Association FRUCT, FRUCT 2021
A2 - Balandin, Sergey
A2 - Koucheryavy, Yevgeni
A2 - Tyutina, Tatiana
PB - IEEE Computer Society
Y2 - 12 May 2021 through 14 May 2021
ER -