کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
505968 | 864550 | 2007 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
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چکیده انگلیسی
In this study, best combination of short-term heart rate variability (HRV) measures are sought for to distinguish 29 patients with congestive heart failure (CHF) from 54 healthy subjects in the control group. In the analysis performed, in addition to the standard HRV measures, wavelet entropy measures are also used. A genetic algorithm is used to select the best ones from among all possible combinations of these measures. A k-nearest neighbor classifier is used to evaluate the performance of the feature combinations in classifying these two groups. The results imply that two combinations of all HRV measures, both of which include wavelet entropy measures, have the highest discrimination power in terms of sensitivity and specificity values.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Biology and Medicine - Volume 37, Issue 10, October 2007, Pages 1502–1510
Journal: Computers in Biology and Medicine - Volume 37, Issue 10, October 2007, Pages 1502–1510
نویسندگان
Yalçın İşler, Mehmet Kuntalp,