Low-cost detection of cardiovascular disease on chronic kidney disease and dialysis patients based on hybrid heterogeneous ECG features including T-wave alternans and heart rate variability

Tsu Wang Shen, Te-Chao Fang, Yi Ling Ou, Chih Hsien Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Accumulating evidence shows that cardiovascular disease (CVD) contributes substantial burden to dialysis patients, accounting for almost 50 percent of mortality in dialysis population. Traditional clinical risk factors may not totally explain and predict CVD high mortality. The aim of this research is to develop a non-invasive, low-cost method for dialysis patients to evaluate their risks on cardiovascular disease (CVD) by hybrid heterogeneous ECG features including T-wave alternans and heart rate variability. A decision-based neural network (DBNN) structure is used for feature fusion and it provides overall 71.07% accuracy for CVD identification.

Original languageEnglish
Title of host publicationComputing in Cardiology
Pages561-564
Number of pages4
Volume37
Publication statusPublished - 2010
Externally publishedYes
EventComputing in Cardiology 2010, CinC 2010 - Belfast, United Kingdom
Duration: Sep 26 2010Sep 29 2010

Other

OtherComputing in Cardiology 2010, CinC 2010
CountryUnited Kingdom
CityBelfast
Period9/26/109/29/10

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ASJC Scopus subject areas

  • Computer Science Applications
  • Cardiology and Cardiovascular Medicine

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