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This study investigates the potential of deep learning methods to identifyindividuals with suspected COVID-19 infection using remotely collectedheart-rate data. The study utilises data from the ongoing EU IMI RADAR-CNSresearch project that is investigating the feasibility of wearable devices andsmart phones to monitor individuals with multiple sclerosis (MS), depression orepilepsy. Aspart of the project protocol, heart-rate data was collected fromparticipants using a Fitbit wristband. The presence of COVID-19 in the cohortin this work was either confirmed through a positive swab test, or inferredthrough the self-reporting of a combination of symptoms including fever,respiratory symptoms, loss of smell or taste, tiredness and gastrointestinalsymptoms. Experimental results indicate that our proposed contrastiveconvolutional auto-encoder (contrastive CAE), i. e., a combined architecture ofan auto-encoder and contrastive loss, outperforms a conventional convolutionalneural network (CNN), as well as a convolutional auto-encoder (CAE) withoutusing contrastive loss. Our final contrastive CAE achieves 95.3% unweightedaverage recall, 86.4% precision, anF1 measure of 88.2%, a sensitivity of 100%and a specificity of 90.6% on a testset of 19 participants with MS who reportedsymptoms of COVID-19. Each of these participants was paired with a participantwith MS with no COVID-19 symptoms.

Fitbeat: COVID-19 Estimation based on Wristband Heart Rate