  This study investigates the potential of deep learning methods to identify
individuals with suspected COVID-19 infection using remotely collected
heart-rate data. The study utilises data from the ongoing EU IMI RADAR-CNS
research project that is investigating the feasibility of wearable devices and
smart phones to monitor individuals with multiple sclerosis (MS), depression or
epilepsy. Aspart of the project protocol, heart-rate data was collected from
participants using a Fitbit wristband. The presence of COVID-19 in the cohort
in this work was either confirmed through a positive swab test, or inferred
through the self-reporting of a combination of symptoms including fever,
respiratory symptoms, loss of smell or taste, tiredness and gastrointestinal
symptoms. Experimental results indicate that our proposed contrastive
convolutional auto-encoder (contrastive CAE), i. e., a combined architecture of
an auto-encoder and contrastive loss, outperforms a conventional convolutional
neural network (CNN), as well as a convolutional auto-encoder (CAE) without
using contrastive loss. Our final contrastive CAE achieves 95.3% unweighted
average 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 reported
symptoms of COVID-19. Each of these participants was paired with a participant
with MS with no COVID-19 symptoms.
