PCovNet: A presymptomatic COVID-19 detection framework using deep learning model using wearables data.
Comput Biol Med
; 147: 105682, 2022 08.
Article
in English
| MEDLINE | ID: covidwho-1944683
ABSTRACT
While 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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Wearable Electronic Devices
/
Deep Learning
/
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
/
Prognostic study
Topics:
Long Covid
Limits:
Humans
Language:
English
Journal:
Comput Biol Med
Year:
2022
Document Type:
Article
Affiliation country:
J.compbiomed.2022.105682
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