Covid-19 Automatic Test through Human Breathing
2021 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1874330
ABSTRACT
A classifier using a Long Short-Term Memory (LSTM) network to identify human beings infected with Covid-19 is proposed in this work. This classifier has significant advantages over current testing methods:
it is fast, contactless, and requires few monetary resources. The data considered for this study was extracted from the Coswara dataset using 140 individuals (70 healthy and 70 infected with Covid-19). This dataset contains respiratory signals, such as people counting numbers, coughing, or breathing. The classifier uses non-linear time sequence features extracted from the signals after a preprocessing stage. The classifier was able to discriminate whether a human is infected with Covid-19 with an accuracy of 92.1%, specificity of 85.7%, and sensitivity of 98.6% using 5-fold Cross-Validation. Based on the results obtained, the classifier can be used as an alternative for the Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests. © 2021 IEEE.
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Database:
Scopus
Language:
English
Journal:
2021 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2021
Year:
2021
Document Type:
Article
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