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
EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany.
Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
Pattern Recognit. 2022 Mar;123:108403. doi: 10.1016/j.patcog.2021.108403. Epub 2021 Oct 26.
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 , a sensitivity of and a specificity of , 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.
本研究提出了一种对比卷积自动编码器（contrastive CAE），它是自动编码器和对比损失的组合架构，旨在利用正在进行的RADAR-CNS移动健康研究项目中多发性硬化症（MS）患者的心率数据来识别疑似感染COVID-19的个体。心率数据通过Fitbit腕带进行远程收集。COVID-19感染要么通过拭子检测呈阳性得到确认，要么通过一组自我报告的该病毒公认症状进行推断。对比CAE的性能优于传统卷积神经网络（CNN）、长短期记忆（LSTM）模型以及没有对比损失的卷积自动编码器（CAE）。在一个由19名有COVID-19症状报告的MS患者组成的测试集中，每个患者都与一名没有COVID-19症状的MS患者配对，对比CAE实现了未加权平均召回率为 （此处原文缺失具体数值），灵敏度为 （此处原文缺失具体数值），特异性为 （此处原文缺失具体数值），受试者工作特征曲线下面积（AUC-ROC）为0.944，这表明在给定的心率测量期间能够最大程度地成功检测症状，同时保持较低的误报率。
