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

Authors:
Yalçın İşler;Mehmet Kuntalp
Affiliations:
Electrical and Electronics Engineering Department, Dokuz Eylül University, İzmir 35160, Turkey;Electrical and Electronics Engineering Department, Dokuz Eylül University, İzmir 35160, Turkey
Venue:
Computers in Biology and Medicine
Year:
2007

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Abstract

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.