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.
|主出版物標題||Computing in Cardiology|
|出版狀態||已發佈 - 2010|
|事件||Computing in Cardiology 2010, CinC 2010 - Belfast, 英国|
持續時間: 九月 26 2010 → 九月 29 2010
|其他||Computing in Cardiology 2010, CinC 2010|
|期間||9/26/10 → 9/29/10|
ASJC Scopus subject areas
- Computer Science Applications
- Cardiology and Cardiovascular Medicine
Shen, T. W., Fang, T-C., Ou, Y. L., & Wang, C. H. (2010). 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. 於 Computing in Cardiology (卷 37, 頁 561-564).