{"title":"Evaluation of the analytic representation of long-record ECG and its HRV signals for congestive heart failure classification","authors":"Mohamed Omar, Abdalla S. A. Mohamed","doi":"10.1109/NRSC.2011.5873616","DOIUrl":null,"url":null,"abstract":"Differential diagnosis of cardiac diseases is considered a real problem in cardiology. Moreover congestive heart disease [CHF] is one of the most life-threatening where it is characterized by neurologic complications, and decreased pulmonary flow. Analysis of long-record ECG trace and/or the extracted HRV signal need to consider the presence of non-stationary. In this work, Hilbert transform is applied to get the analytic representation of these signals. Instantaneous amplitude (envelop); phase; and frequency were calculated. K-means algorithm was applied on these outputs to classify CHF. Classification results were promising with ECG (92.1%) more than HRV (75.85).","PeriodicalId":438638,"journal":{"name":"2011 28th National Radio Science Conference (NRSC)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 28th National Radio Science Conference (NRSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NRSC.2011.5873616","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Differential diagnosis of cardiac diseases is considered a real problem in cardiology. Moreover congestive heart disease [CHF] is one of the most life-threatening where it is characterized by neurologic complications, and decreased pulmonary flow. Analysis of long-record ECG trace and/or the extracted HRV signal need to consider the presence of non-stationary. In this work, Hilbert transform is applied to get the analytic representation of these signals. Instantaneous amplitude (envelop); phase; and frequency were calculated. K-means algorithm was applied on these outputs to classify CHF. Classification results were promising with ECG (92.1%) more than HRV (75.85).
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