Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Liu, S; Han, J; Puyal, EL; Kontaxis, S; Sun, S; Locatelli, P; Dineley, J; Pokorny, FB; Costa, GD; Leocani, L; Guerrero, AI; Nos, C; Zabalza, A; Sørensen, PS; Buron, M; Magyari, M; Ranjan, Y; Rashid, Z; Conde, P; Stewart, C; Folarin, AA; Dobson, RJ; Bailón, R; Vairavan, S; Cummins, N; Narayan, VA; Hotopf, M; Comi, G; Schuller, B; Consortium, RC.
Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder.
Pattern Recognit. 2022; 123:108403
Doi: 10.1016/j.patcog.2021.108403
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- Co-Autor*innen der Med Uni Graz
- Pokorny Florian
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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
- Find related publications in this database (Keywords)
- COVID-19
- Respiratory tract infection
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- Contrastive learning
- Convolutional auto-encoder