Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Standard
Fitbeat : COVID-19 estimation based on wristband heart rate using a contrastive convolutional 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.; Bailon, Raquel; Vairavan, Srinivasan; Cummins, Nicholas; Narayan, Vaibhav A.; Hotopf, Matthew; Comi, Giancarlo; Schuller, Bjoern; RADAR-CNS Consortium.
I: Pattern Recognition, Bind 123, 108403, 03.2022.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Fitbeat
T2 - COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder
AU - Liu, Shuo
AU - Han, Jing
AU - Puyal, Estela Laporta
AU - Kontaxis, Spyridon
AU - Sun, Shaoxiong
AU - Locatelli, Patrick
AU - Dineley, Judith
AU - Pokorny, Florian B.
AU - Dalla Costa, Gloria
AU - Leocani, Letizia
AU - Guerrero, Ana Isabel
AU - Nos, Carlos
AU - Zabalza, Ana
AU - Sorensen, Per Soelberg
AU - Buron, Mathias
AU - Magyari, Melinda
AU - Ranjan, Yatharth
AU - Rashid, Zulqarnain
AU - Conde, Pauline
AU - Stewart, Callum
AU - Folarin, Amos A.
AU - Dobson, Richard J. B.
AU - Bailon, Raquel
AU - Vairavan, Srinivasan
AU - Cummins, Nicholas
AU - Narayan, Vaibhav A.
AU - Hotopf, Matthew
AU - Comi, Giancarlo
AU - Schuller, Bjoern
AU - RADAR-CNS Consortium
PY - 2022/3
Y1 - 2022/3
N2 - 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.
AB - 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.
KW - COVID-19
KW - Respiratory tract infection
KW - Anomaly detection
KW - Contrastive learning
KW - Convolutional auto-encoder
U2 - 10.1016/j.patcog.2021.108403
DO - 10.1016/j.patcog.2021.108403
M3 - Journal article
C2 - 34720200
VL - 123
JO - Pattern Recognition
JF - Pattern Recognition
SN - 0031-3203
M1 - 108403
ER -
ID: 315406012