Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure
Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure
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DOI：
10.1016/j.compbiomed.2007.01.012
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发表时间：
2007-10-01
影响因子：
7.7
通讯作者：
Kuntalp, Mehmet
中科院分区：
文献类型：
--
作者：
Isler, Yalcin;Kuntalp, Mehmet
在这项研究中，寻找短期心率变异性(HRV)测量的最佳组合，以区分29例充血性心力衰竭(CHF)患者和54名健康对照组。在进行的分析中，除了标准的HRV度量外，还使用了小波熵度量。遗传算法被用来从这些测量的所有可能组合中选择最好的。使用k近邻分类器来评估特征组合在分类这两组时的性能。结果表明，所有HRV指标的两种组合，都包括小波熵，在敏感度和特异度方面具有最高的识别力。(C)2007爱思唯尔有限公司。保留所有权利。
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. (c) 2007 Elsevier Ltd. All rights reserved.