V. Atanasoski, M. Ivanovic, M. Marinković, G. Gligoric, B. Bojovic, A. Shvilkin, J. Petrovic
{"title":"基于RR间期和心跳形态的室性早搏无监督分类","authors":"V. Atanasoski, M. Ivanovic, M. Marinković, G. Gligoric, B. Bojovic, A. Shvilkin, J. Petrovic","doi":"10.1109/NEUREL.2018.8586997","DOIUrl":null,"url":null,"abstract":"Accurate automated detection of premature ventricular contractions from electrocardiogram requires a training set or expert intervention. We propose a fully automated unsupervised detection method. The algorithm first clusters morphologically similar heartbeats and then performs classification based on RR intervals and morphology. Tests on clinically recorded datasets show sensitivity of 94.7%, specificity of 99.6% and accuracy of 99.5%.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Unsupervised Classification of Premature Ventricular Contractions Based on RR Interval and Heartbeat Morphology\",\"authors\":\"V. Atanasoski, M. Ivanovic, M. Marinković, G. Gligoric, B. Bojovic, A. Shvilkin, J. Petrovic\",\"doi\":\"10.1109/NEUREL.2018.8586997\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurate automated detection of premature ventricular contractions from electrocardiogram requires a training set or expert intervention. We propose a fully automated unsupervised detection method. The algorithm first clusters morphologically similar heartbeats and then performs classification based on RR intervals and morphology. Tests on clinically recorded datasets show sensitivity of 94.7%, specificity of 99.6% and accuracy of 99.5%.\",\"PeriodicalId\":371831,\"journal\":{\"name\":\"2018 14th Symposium on Neural Networks and Applications (NEUREL)\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 14th Symposium on Neural Networks and Applications (NEUREL)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NEUREL.2018.8586997\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NEUREL.2018.8586997","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Unsupervised Classification of Premature Ventricular Contractions Based on RR Interval and Heartbeat Morphology
Accurate automated detection of premature ventricular contractions from electrocardiogram requires a training set or expert intervention. We propose a fully automated unsupervised detection method. The algorithm first clusters morphologically similar heartbeats and then performs classification based on RR intervals and morphology. Tests on clinically recorded datasets show sensitivity of 94.7%, specificity of 99.6% and accuracy of 99.5%.