New algorithms study findings recently were reported by researchers at Dokuz Eylul University
2008 JAN 18 -- "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," scientists in Izmir, Turkey report. "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," wrote Y. Isler and colleagues, Dokuz Eylul University. The researchers concluded: "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." Isler and colleagues published their study in Computers in Biology and Medicine (Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure. Computers in Biology and Medicine, 2007;37(10):1502-1510). For more information, contact Y. Isler, Dokuz Eylul University, Dept. of Electrical & Electrical Engineering, TR-35160 Izmir, Turkey. Publisher contact information for the journal Computers in Biology and Medicine is: Pergamon-Elsevier Science Ltd., the Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. Keywords: Turkey, Izmir, Algorithms, Biotechnology, Cardiology, Congestive Heart Failure, Coronary Artery Disease, Genetic Algorithm, Heart Disease, Medical Device, Dokuz Eylul University. This article was prepared by Genomics & Genetics Weekly editors from staff and other reports. Copyright 2008, Genomics & Genetics Weekly via NewsRx.com.
|
