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AuthorAbir, Farhan Fuad
AuthorAlyafei, Khalid
AuthorChowdhury, Muhammad E.H.
AuthorKhandakar, Amith
AuthorAhmed, Rashid
AuthorHossain, Muhammad Maqsud
AuthorMahmud, Sakib
AuthorRahman, Ashiqur
AuthorAbbas, Tareq O.
AuthorZughaier, Susu M.
AuthorNaji, Khalid Kamal
Available date2023-04-17T06:57:42Z
Publication Date2022
Publication NameComputers in Biology and Medicine
ResourceScopus
URIhttp://dx.doi.org/10.1016/j.compbiomed.2022.105682
URIhttp://hdl.handle.net/10576/41948
AbstractWhile the advanced diagnostic tools and healthcare management protocols have been struggling to contain the COVID-19 pandemic, the spread of the contagious viral pathogen before the symptom onset acted as the Achilles' heel. Although reverse transcription-polymerase chain reaction (RT-PCR) has been widely used for COVID-19 diagnosis, they are hardly administered before any visible symptom, which provokes rapid transmission. This study proposes PCovNet, a Long Short-term Memory Variational Autoencoder (LSTM-VAE)-based anomaly detection framework, to detect COVID-19 infection in the presymptomatic stage from the Resting Heart Rate (RHR) derived from the wearable devices, i.e., smartwatch or fitness tracker. The framework was trained and evaluated in two configurations on a publicly available wearable device dataset consisting of 25 COVID-positive individuals in the span of four months including their COVID-19 infection phase. The first configuration of the framework detected RHR abnormality with average Precision, Recall, and F-beta scores of 0.946, 0.234, and 0.918, respectively. However, the second configuration detected aberrant RHR in 100% of the subjects (25 out of 25) during the infectious period. Moreover, 80% of the subjects (20 out of 25) were detected during the presymptomatic stage. These findings prove the feasibility of using wearable devices with such a deep learning framework as a secondary diagnosis tool to circumvent the presymptomatic COVID-19 detection problem. 2022 Elsevier Ltd
SponsorThis work was supported by the Qatar National Research Grant: UREP28-144-3-046. The statements made herein are solely the responsibility of the authors.
Languageen
PublisherElsevier
SubjectAnomaly detection
COVID-19
Long short-term memory
Presymptomatic
Resting heart rate
Smartwatch
Variational autoencoder
TitlePCovNet: A presymptomatic COVID-19 detection framework using deep learning model using wearables data
TypeArticle
Volume Number147
dc.accessType Abstract Only


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