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 language | English |
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Title of host publication | Computing in Cardiology |
Pages | 561-564 |
Number of pages | 4 |
Volume | 37 |
Publication status | Published - 2010 |
Externally published | Yes |
Event | Computing in Cardiology 2010, CinC 2010 - Belfast, United Kingdom Duration: Sept 26 2010 → Sept 29 2010 |
Other
Other | Computing in Cardiology 2010, CinC 2010 |
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Country/Territory | United Kingdom |
City | Belfast |
Period | 9/26/10 → 9/29/10 |
ASJC Scopus subject areas
- Computer Science Applications
- Cardiology and Cardiovascular Medicine