COVID-19 Recognition Based on Patient's Coughing and Breathing Patterns Analysis: Deep Learning Approach

Lazhar Khriji, Ahmed Ammari, Seifeddine Messaoud, Soulef Bouaafia, Amna Maraoui, Mohsen MacHhout

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 29th Conference of Open Innovations Association FRUCT, FRUCT 2021
EditorsSergey Balandin, Yevgeni Koucheryavy, Tatiana Tyutina
PublisherIEEE Computer Society
Pages185-191
Number of pages7
ISBN (Electronic)9789526924458
DOIs
Publication statusPublished - May 12 2021
Event29th Conference of Open Innovations Association FRUCT, FRUCT 2021 - Virtual, Tampere, Finland
Duration: May 12 2021May 14 2021

Publication series

NameConference of Open Innovation Association, FRUCT
Volume2021-May
ISSN (Print)2305-7254

Conference

Conference29th Conference of Open Innovations Association FRUCT, FRUCT 2021
Country/TerritoryFinland
CityVirtual, Tampere
Period5/12/215/14/21

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

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