PhysioTag: An Open-Source Platform for Collaborative Annotation of Physiological Waveforms

L. McCullum, Hasan Saeed, Benjamin Moody, D. Perry, Eric Gottlieb, T. Pollard, Xavier Borrat Frigola, Qiao Li, Gari D. Clifford, R. Mark, Li-wei H. Lehman
{"title":"PhysioTag: An Open-Source Platform for Collaborative Annotation of Physiological Waveforms","authors":"L. McCullum, Hasan Saeed, Benjamin Moody, D. Perry, Eric Gottlieb, T. Pollard, Xavier Borrat Frigola, Qiao Li, Gari D. Clifford, R. Mark, Li-wei H. Lehman","doi":"10.22489/CinC.2022.335","DOIUrl":null,"url":null,"abstract":"To develop robust algorithms for automated diagnosis of medical conditions such as cardiac arrhythmias, researchers require large collections of data with human expert annotations. Currently, there is a lack of accessible, open-source platforms for human experts to collaboratively develop these annotated datasets through a web interface. In this work, we developed a flexible, generalizable, web-based framework to enable multiple users to create and share annotations on multi-channel physiological waveforms. Using the developed annotation platform, we carried out a pilot study to assess the validity of ventricular tachycardia (VT) alarms from multiple commercial monitors. Thus far, four clinical experts have used this annotation tool to annotate a total of 5,658 VT alarm events, among which approximately 44%(N=2,468) have been labeled by two independent annotators.","PeriodicalId":117840,"journal":{"name":"2022 Computing in Cardiology (CinC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Computing in Cardiology (CinC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22489/CinC.2022.335","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

To develop robust algorithms for automated diagnosis of medical conditions such as cardiac arrhythmias, researchers require large collections of data with human expert annotations. Currently, there is a lack of accessible, open-source platforms for human experts to collaboratively develop these annotated datasets through a web interface. In this work, we developed a flexible, generalizable, web-based framework to enable multiple users to create and share annotations on multi-channel physiological waveforms. Using the developed annotation platform, we carried out a pilot study to assess the validity of ventricular tachycardia (VT) alarms from multiple commercial monitors. Thus far, four clinical experts have used this annotation tool to annotate a total of 5,658 VT alarm events, among which approximately 44%(N=2,468) have been labeled by two independent annotators.
生理波形协同标注的开源平台PhysioTag
为了开发用于心律失常等医疗条件自动诊断的强大算法,研究人员需要大量带有人类专家注释的数据集。目前,缺乏可访问的开源平台,供人类专家通过web界面协作开发这些带注释的数据集。在这项工作中,我们开发了一个灵活的、通用的、基于web的框架,使多个用户能够创建和共享多通道生理波形的注释。利用开发的注释平台,我们进行了一项初步研究,以评估来自多个商业监视器的室性心动过速(VT)警报的有效性。迄今为止,共有4位临床专家使用该标注工具标注了5658个VT报警事件,其中约44%(N= 2468)由两个独立的标注者标注。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信