RPS/GMM定位心肌梗死的方法

M.A. Mneimneh, R. Povinelli
{"title":"RPS/GMM定位心肌梗死的方法","authors":"M.A. Mneimneh, R. Povinelli","doi":"10.1109/CIC.2007.4745452","DOIUrl":null,"url":null,"abstract":"Due to the lack between clinical methods and applications used to diagnose ischemic heart disease, the 2007 Physionet/Computers in Cardiology challenge focuses on the ability to identify the segments, extent, and centroid of infarcts through ECG signals and body surface maps. The results from the participants are compared to a gold standard that consists of expert analysis of gadolinium-enhanced MRI data. The main hypothesis in this work is that the ordinary 12 ordinary leads contain the necessary information to identify the segment of the infarct. This hypothesis is tested using a reconstructed phase space and Gaussian Mixture Model approach in order to identify the infarcted segments. Since the challenge dataset consists of only two records for training and two for testing, the RPS/GMM approach is trained on the infarcted records from the PTB Diagnostics database and tested on the challenge data. The final score for the classification method was 1.15 out of maximum of 2.","PeriodicalId":406683,"journal":{"name":"2007 Computers in Cardiology","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"RPS/GMM Approach toward the localization of myocardial infarction\",\"authors\":\"M.A. Mneimneh, R. Povinelli\",\"doi\":\"10.1109/CIC.2007.4745452\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Due to the lack between clinical methods and applications used to diagnose ischemic heart disease, the 2007 Physionet/Computers in Cardiology challenge focuses on the ability to identify the segments, extent, and centroid of infarcts through ECG signals and body surface maps. The results from the participants are compared to a gold standard that consists of expert analysis of gadolinium-enhanced MRI data. The main hypothesis in this work is that the ordinary 12 ordinary leads contain the necessary information to identify the segment of the infarct. This hypothesis is tested using a reconstructed phase space and Gaussian Mixture Model approach in order to identify the infarcted segments. Since the challenge dataset consists of only two records for training and two for testing, the RPS/GMM approach is trained on the infarcted records from the PTB Diagnostics database and tested on the challenge data. The final score for the classification method was 1.15 out of maximum of 2.\",\"PeriodicalId\":406683,\"journal\":{\"name\":\"2007 Computers in Cardiology\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 Computers in Cardiology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIC.2007.4745452\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 Computers in Cardiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIC.2007.4745452","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15

摘要

由于缺乏用于诊断缺血性心脏病的临床方法和应用,2007年Physionet/Computers in Cardiology挑战赛侧重于通过ECG信号和体表图识别梗死段、范围和质心的能力。参与者的结果与金标准进行比较,金标准由专家分析钆增强MRI数据组成。这项工作的主要假设是,普通的12个普通导联包含必要的信息,以确定梗塞的部分。为了识别梗死段,使用重构相空间和高斯混合模型方法对这一假设进行了检验。由于挑战数据集仅由两条用于训练的记录和两条用于测试的记录组成,RPS/GMM方法在PTB诊断数据库中的梗死记录上进行训练,并在挑战数据上进行测试。该分类方法的最终得分为1.15分,满分为2分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
RPS/GMM Approach toward the localization of myocardial infarction
Due to the lack between clinical methods and applications used to diagnose ischemic heart disease, the 2007 Physionet/Computers in Cardiology challenge focuses on the ability to identify the segments, extent, and centroid of infarcts through ECG signals and body surface maps. The results from the participants are compared to a gold standard that consists of expert analysis of gadolinium-enhanced MRI data. The main hypothesis in this work is that the ordinary 12 ordinary leads contain the necessary information to identify the segment of the infarct. This hypothesis is tested using a reconstructed phase space and Gaussian Mixture Model approach in order to identify the infarcted segments. Since the challenge dataset consists of only two records for training and two for testing, the RPS/GMM approach is trained on the infarcted records from the PTB Diagnostics database and tested on the challenge data. The final score for the classification method was 1.15 out of maximum of 2.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Implementation and use of a patient data management system in the intensive care unit: A two-year experience Modelling effects of sotalol on T-wave morphology Vulnerability to reentry in a 3D regionally ischemic ventricular slab preparation: A simulation study Evaluation of multi-component Electrocardiogram beat detection algorithms: Implications of three different noise artifacts Dynamic 4D blood flow representation in the aorta and analysis from cine-MRI in patients
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1