TY - GEN
T1 - 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
AU - Shen, Tsu Wang
AU - Fang, Te-Chao
AU - Ou, Yi Ling
AU - Wang, Chih Hsien
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:79953817214
SN - 9781424473182
VL - 37
SP - 561
EP - 564
BT - Computing in Cardiology
T2 - Computing in Cardiology 2010, CinC 2010
Y2 - 26 September 2010 through 29 September 2010
ER -
