Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure

• Published in: Computers in biology and medicine Vol. 37; no. 10; pp. 1502 - 1510
• Main Authors: İşler, Yalçın, Kuntalp, Mehmet
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.10.2007; Elsevier Limited
• Subjects: Adult; Aged; Algorithms; Analysis of Variance; Case-Control Studies; Congestive heart failure; Databases, Factual; Electrocardiography - statistics & numerical data; Feature selection; Female; Genetic algorithm; Heart Failure - diagnosis; Heart Failure - physiopathology; Heart Rate - physiology; Heart rate variability; Humans; Internal Medicine; k-Nearest neighbor rule; Male; Middle Aged; Other; Sensitivity and Specificity; Software Design; Wavelet entropy; Feature selection; Congestive heart failure; Genetic algorithm; Heart rate variability; Wavelet entropy; k-Nearest neighbor rule
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2007.01.012

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.