In Journal of medical virology
One of the effective ways to minimize the spread of COVID-19 infection is to diagnose it as early as possible before the onset of symptoms. In addition, if the infection can be simply diagnosed using a smartwatch, the effectiveness of preventing the spread will be greatly increased. In this study, we aimed to develop a deep learning model to diagnose COVID-19 before the onset of symptoms using heart rate (HR) data obtained from a smartwatch. In the deep learning model for the diagnosis, we proposed a transformer model that learns heart rate variability patterns in pre-symptom by tracking relationships in sequential HR data. In the cross-validation results from the COVID-19 unvaccinated patients, our proposed deep learning model exhibited high accuracy metrics: sensitivity of 84.38%, specificity of 85.25%, accuracy of 84.85%, balanced accuracy of 84.81%, and AUROC of 0.8778. Furthermore, we validated our model using external multiple datasets including healthy subjects, COVID-19 patients, as well as vaccinated patients. In the external healthy subject group, our model also achieved high specificity of 77.80%. In the external COVID-19 unvaccinated patient group, our model also provided similar accuracy metrics to those from the cross-validation: balanced accuracy of 87.23% and AUROC of 0.8897. In the COVID-19 vaccinated patients, the balanced accuracy and AUROC dropped by 66.67% and 0.8072, respectively. The first finding in this study is that our proposed deep learning model can simply and accurately diagnose COVID-19 patients using HRs obtained from a smartwatch before the onset of symptoms. The second finding is that the model trained from unvaccinated patients may provide less accurate diagnosis performance compared to the vaccinated patients. The last finding is that the model trained in a certain period of times may provide degraded diagnosis performances as the virus continues to mutate. This article is protected by copyright. All rights reserved.
Chung Heewon, Ko Hoon, Lee Hooseok, Yon Dong Keon, Lee Won Hee, Kim Tae-Seong, Kim Kyung Won, Lee Jinseok
COVID-19, deep learning, early diagnosis, heart rate, heart rate variability, smartwatch, transformer model