{"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。
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.