Fitbeat: COVID-19 Estimation based on Wristband Heart Rate

• URL: http://arxiv.org/abs/2104.09263v1
• Date: Mon, 19 Apr 2021 13:08:53 GMT
• Title: Fitbeat: COVID-19 Estimation based on Wristband Heart Rate
• Authors: Shuo Liu, Jing Han, Estela Laporta Puyal, Spyridon Kontaxis, Shaoxiong Sun, Patrick Locatelli, Judith Dineley, Florian B. Pokorny, Gloria Dalla Costa, Letizia Leocan, Ana Isabel Guerrero, Carlos Nos, Ana Zabalza, Per Soelberg S{\o}rensen, Mathias Buron, Melinda Magyari, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Callum Stewart, Amos A Folarin, Richard JB Dobson, Raquel Bail\'on, Srinivasan Vairavan, Nicholas Cummins, Vaibhav A Narayan, Matthew Hotopf, Giancarlo Comi, Bj\"orn Schuller
• Abstract summary: This study investigates the potential of deep learning methods to identify individuals with suspected COVID-19 infection. Heart-rate data was collected from participants using a Fitbit wristband.
• Score: 15.550897317833996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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