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

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

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  • NSTL
  • Elsevier
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. (c) 2021 Elsevier Ltd. All rights reserved.

COVID-19Respiratory tract infectionAnomaly detectionContrastive learningConvolutional auto-encoder

Liu, Shuo、Han, Jing、Puyal, Estela Laporta、Kontaxis, Spyridon、Sun, Shaoxiong、Locatelli, Patrick、Dineley, Judith、Pokorny, Florian B.、Dalla Costa, Gloria、Leocani, Letizia、Guerrero, Ana Isabel、Nos, Carlos、Zabalza, Ana、Sorensen, Per Soelberg、Buron, Mathias、Magyari, Melinda、Ranjan, Yatharth、Rashid, Zulqarnain、Conde, Pauline、Stewart, Callum、Folarin, Amos A.、Dobson, Richard J. B.、Vairavan, Srinivasan、Cummins, Nicholas、Narayan, Vaibhav A.、Hotopf, Matthew、Comi, Giancarlo、Schuller, Bjoern、RADAR-CNS Consortium、Bailon, Raquel

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Univ Augsburg

Univ Zaragoza

Kings Coll London

Univ Bergamo

Sci Inst Hosp San Raffaele

Univ Autonoma Barcelona

Copenhagen Univ Hosp Rigshosp

Janssen Res & Dev LLC

Univ Vita Salute San Raffaele

RADAR CNS Consortium

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2022

10.1016/j.patcog.2021.108403

Pattern Recognition

Pattern Recognition

EISCI
ISSN：0031-3203
年,卷(期)：2022.123
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