Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder

•Heart rate based identification of individuals with suspected COVID-19 infection.•Semi-supervised framework using combination of auto-encoder and contrastive loss.•Contrastive convolutional auto-encoder is capable of finding proper latent attributes.•COVID-19 estimation performance declines with da...

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Veröffentlicht in:Pattern recognition 2022-03, Vol.123, p.108403-108403, Article 108403
Hauptverfasser: Liu, Shuo, Han, Jing, Puyal, Estela Laporta, Kontaxis, Spyridon, Sun, Shaoxiong, Locatelli, Patrick, Dineley, Judith, Pokorny, Florian B., Costa, Gloria Dalla, Leocani, Letizia, Guerrero, Ana Isabel, Nos, Carlos, Zabalza, Ana, Sørensen, Per Soelberg, Buron, Mathias, Magyari, Melinda, Ranjan, Yatharth, Rashid, Zulqarnain, Conde, Pauline, Stewart, Callum, Folarin, Amos A, Dobson, Richard JB, Bailón, Raquel, Vairavan, Srinivasan, Cummins, Nicholas, Narayan, Vaibhav A, Hotopf, Matthew, Comi, Giancarlo, Schuller, Björn, Consortium, RADAR-CNS
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Sprache:eng
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Zusammenfassung:•Heart rate based identification of individuals with suspected COVID-19 infection.•Semi-supervised framework using combination of auto-encoder and contrastive loss.•Contrastive convolutional auto-encoder is capable of finding proper latent attributes.•COVID-19 estimation performance declines with data shifted from symptom reported date. 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 95.3%, a sensitivity of 100% and a specificity of 90.6%, an area under the receiver operating characteristic curve (AUC-ROC) of 0.944, indicating a maximum successful detection of symptoms in the given heart rate measurement period, whilst at the same time keeping a low false alarm rate.
ISSN:0031-3203
1873-5142
0031-3203
DOI:10.1016/j.patcog.2021.108403