{"title":"心电信号分析的独立分量法","authors":"U. Nimitha, P. Supriya","doi":"10.1109/PACC.2011.5979049","DOIUrl":null,"url":null,"abstract":"Automated analysis of electrocardiogram (ECG) has got great attention for cardiac diagnosis in the recent years. This paper describes two different ECG analysis algorithms using Independent Component Analysis (ICA) algorithm. ICA refers to set of algorithms for blind source separation (BSS). The underlying principle is to separate N signals from a mix of different source contributions, into signals of independent components. The simulation is proposed to be done in MATLAB.","PeriodicalId":403612,"journal":{"name":"2011 International Conference on Process Automation, Control and Computing","volume":"216 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Independent Component Approach for the Analysis of ECG Signals\",\"authors\":\"U. Nimitha, P. Supriya\",\"doi\":\"10.1109/PACC.2011.5979049\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automated analysis of electrocardiogram (ECG) has got great attention for cardiac diagnosis in the recent years. This paper describes two different ECG analysis algorithms using Independent Component Analysis (ICA) algorithm. ICA refers to set of algorithms for blind source separation (BSS). The underlying principle is to separate N signals from a mix of different source contributions, into signals of independent components. The simulation is proposed to be done in MATLAB.\",\"PeriodicalId\":403612,\"journal\":{\"name\":\"2011 International Conference on Process Automation, Control and Computing\",\"volume\":\"216 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-07-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 International Conference on Process Automation, Control and Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PACC.2011.5979049\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Process Automation, Control and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PACC.2011.5979049","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Independent Component Approach for the Analysis of ECG Signals
Automated analysis of electrocardiogram (ECG) has got great attention for cardiac diagnosis in the recent years. This paper describes two different ECG analysis algorithms using Independent Component Analysis (ICA) algorithm. ICA refers to set of algorithms for blind source separation (BSS). The underlying principle is to separate N signals from a mix of different source contributions, into signals of independent components. The simulation is proposed to be done in MATLAB.
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