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CovidEnvelope: A Fast Automated Approach to Diagnose COVID-19 from Cough Signals
Md Zakir Hossain; Md. Bashir Uddin; Khandaker Asif Ahmed.
Affiliation
  • Md Zakir Hossain; The Australian National University
  • Md. Bashir Uddin; Khulna University of Engineering & Technology
  • Khandaker Asif Ahmed; The Commonwealth Science and Industrial Research Organization
Preprint in English | medRxiv | ID: ppmedrxiv-21255630
ABSTRACT
The COVID-19 pandemic has a devastating impact on the health and well-being of global population. Cough audio signals classification showed potential as a screening approach for diagnosing people, infected with COVID-19. Recent approaches need costly deep learning algorithms or sophisticated methods to extract informative features from cough audio signals. In this paper, we propose a low-cost envelope approach, called CovidEnvelope, which can classify COVID-19 positive and negative cases from raw data by avoiding above disadvantages. This automated approach can pre-process cough audio signals by filter-out back-ground noises, generate an envelope around the audio signal, and finally provide outcomes by computing area enclosed by the envelope. It has been seen that reliable datasets are also important for achieving high performance. Our approach proves that human verbal confirmation is not a reliable source of information. Finally, the approach reaches highest sensitivity, specificity, accuracy, and AUC of 0.92, 0.87, 0.89, and 0.89 respectively. The automatic approach only takes 1.8 to 3.9 minutes to compute these performances. Overall, this approach is fast and sensitive to diagnose the people living with COVID-19, regardless of having COVID-19 related symptoms or not, and thus have vast applicability in human well-being by designing HCI devices incorporating this approach.
License
cc_by_nc_nd
Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2021 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2021 Document type: Preprint
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