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Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder

Liu, S; Han, J; Puyal, EL; Kontaxis, S; Sun, S; Locatelli, P; Dineley, J; ... Consortium, R-C; + view all (2022) Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder. Pattern Recognit , 123 , Article 108403. 10.1016/j.patcog.2021.108403. Green open access

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Abstract

This study proposes a contrastive convolutional auto-encoder (contrastive CAE), a combined architecture of an auto-encoder and contrastive loss, to identify individuals with suspected COVID-19 infection using heart-rate data from participants with multiple sclerosis (MS) in the ongoing RADAR-CNS mHealth research project. Heart-rate data was remotely collected using a Fitbit wristband. COVID-19 infection was either confirmed through a positive swab test, or inferred through a self-reported set of recognised symptoms of the virus. The contrastive CAE outperforms a conventional convolutional neural network (CNN), a long short-term memory (LSTM) model, and a convolutional auto-encoder without contrastive loss (CAE). On a test set of 19 participants with MS with reported symptoms of COVID-19, each one paired with a participant with MS with no COVID-19 symptoms, the contrastive CAE achieves an unweighted average recall of 95.3 % , a sensitivity of 100 % and a specificity of 90.6 % , an area under the receiver operating characteristic curve (AUC-ROC) of 0.944, indicating a maximum successful detection of symptoms in the given heart rate measurement period, whilst at the same time keeping a low false alarm rate.

Type: Article
Title: Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.patcog.2021.108403
Publisher version: https://doi.org/10.1016/j.patcog.2021.108403
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Anomaly detection, COVID-19, Contrastive learning, Convolutional auto-encoder, Respiratory tract infection
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology
URI: https://discovery.ucl.ac.uk/id/eprint/10138886
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