{"title":"心电图语法模式识别诊断依据","authors":"P. Flasinski","doi":"10.22630/mgv.2014.23.3.4","DOIUrl":null,"url":null,"abstract":"A novel hybrid structural-parametric model for ECG diagnostic justification is presented in the paper. In order to distinguish between specific subclasses of heart dysfunction phenomena both grammars and automata are enhanced with a formalism of dynamic programming. It allows one to construct a system, which is feasible for aiding a process of teaching and evaluating medical students' diagnostic reasoning in the area of electrocardiography.","PeriodicalId":39750,"journal":{"name":"Machine Graphics and Vision","volume":"112 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2012-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Syntactic pattern recognition of ECG for diagnostic justification\",\"authors\":\"P. Flasinski\",\"doi\":\"10.22630/mgv.2014.23.3.4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A novel hybrid structural-parametric model for ECG diagnostic justification is presented in the paper. In order to distinguish between specific subclasses of heart dysfunction phenomena both grammars and automata are enhanced with a formalism of dynamic programming. It allows one to construct a system, which is feasible for aiding a process of teaching and evaluating medical students' diagnostic reasoning in the area of electrocardiography.\",\"PeriodicalId\":39750,\"journal\":{\"name\":\"Machine Graphics and Vision\",\"volume\":\"112 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-01-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Machine Graphics and Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22630/mgv.2014.23.3.4\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Machine Graphics and Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22630/mgv.2014.23.3.4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Syntactic pattern recognition of ECG for diagnostic justification
A novel hybrid structural-parametric model for ECG diagnostic justification is presented in the paper. In order to distinguish between specific subclasses of heart dysfunction phenomena both grammars and automata are enhanced with a formalism of dynamic programming. It allows one to construct a system, which is feasible for aiding a process of teaching and evaluating medical students' diagnostic reasoning in the area of electrocardiography.
期刊介绍：
Machine GRAPHICS & VISION (MGV) is a refereed international journal, published quarterly, providing a scientific exchange forum and an authoritative source of information in the field of, in general, pictorial information exchange between computers and their environment, including applications of visual and graphical computer systems. The journal concentrates on theoretical and computational models underlying computer generated, analysed, or otherwise processed imagery, in particular: - image processing - scene analysis, modeling, and understanding - machine vision - pattern matching and pattern recognition - image synthesis, including three-dimensional imaging and solid modeling
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