Z. Cserjes, V. Szathmáry, M. Tiňová, G. Kozmann, M. Tysler
{"title":"基于特征提取的心电图反解方法","authors":"Z. Cserjes, V. Szathmáry, M. Tiňová, G. Kozmann, M. Tysler","doi":"10.1109/CIC.1997.647938","DOIUrl":null,"url":null,"abstract":"The paper shows results of numerical and biological model studies related to a new inverse computation approach in electrocardiography. The method is based on linear prediction, which ables one to select the diagnostically informative moments from the heart electric activation. The information content of the measured body surface potential field at these moments is much higher than at the other times, so the algorithm can be counted as a feature extraction method.","PeriodicalId":228649,"journal":{"name":"Computers in Cardiology 1997","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Feature extraction based method for the inverse solution of electrocardiography\",\"authors\":\"Z. Cserjes, V. Szathmáry, M. Tiňová, G. Kozmann, M. Tysler\",\"doi\":\"10.1109/CIC.1997.647938\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper shows results of numerical and biological model studies related to a new inverse computation approach in electrocardiography. The method is based on linear prediction, which ables one to select the diagnostically informative moments from the heart electric activation. The information content of the measured body surface potential field at these moments is much higher than at the other times, so the algorithm can be counted as a feature extraction method.\",\"PeriodicalId\":228649,\"journal\":{\"name\":\"Computers in Cardiology 1997\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers in Cardiology 1997\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIC.1997.647938\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in Cardiology 1997","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIC.1997.647938","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Feature extraction based method for the inverse solution of electrocardiography
The paper shows results of numerical and biological model studies related to a new inverse computation approach in electrocardiography. The method is based on linear prediction, which ables one to select the diagnostically informative moments from the heart electric activation. The information content of the measured body surface potential field at these moments is much higher than at the other times, so the algorithm can be counted as a feature extraction method.
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