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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tsu-Wang Shen Te-Chao Fang Yi-Ling Ou Chih-Hsien Wang |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of Medical Informatics, Tzu-Chi University, Hualien, Taiwan (Tsu-Wang Shen; Yi-Ling Ou) || Division of Nephrology, Buddhist Tzu Chi General Hospital, Hualien, Taiwan (Te-Chao Fang; Chih-Hsien Wang) |
| 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. |
| Starting Page | 561 |
| Ending Page | 564 |
| File Size | 107324 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424473182 |
| ISSN | 02766547 |
| e-ISBN | 9781424473199 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-09-26 |
| Publisher Place | United Kingdom |
| Access Restriction | Subscribed |
| Rights Holder | Creative Commons Attribution License 2.5 (CCAL) |
| Subject Keyword | Heart rate variability Artificial neural networks Cardiology Electrocardiography Neurons Cardiovascular diseases Noise |
| Content Type | Text |
| Resource Type | Article |
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