Işler Y, Kuntalp M
Department of Electrical and Electronics Engineering, Engineering Faculty, Dokuz Eylül University, Buca, Izmir, Turkey.
Proc Inst Mech Eng H. 2010;224(3):453-63. doi: 10.1243/09544119JEIM642.
In this study, the effects of heart rate (HR) normalization in the analysis of the heart rate variability (HRV) were investigated to distinguish 29 patients with congestive heart failure from 54 healthy subjects in the control group. In the analysis performed, the best accuracy performances of optimal combination of standard short-term HRV measures and of HR-normalized short-term HRV measures are compared. A genetic algorithm is used to select the best features from among all possible combinations of these measures. A k-nearest-neighbour (KNN) classifier is used to evaluate the performances of the feature combinations in classifying these two data groups. The results imply that using both min-max and HR normalization improves the performance of the classification. The maximum accuracy is achieved as 93.98 per cent using k = 3 and k = 5 for the KNN classifier with the perfect positive predictivity values.
在本研究中，对心率变异性（HRV）分析中进行心率（HR）归一化的效果进行了调查，以区分29例充血性心力衰竭患者和54例作为对照组的健康受试者。在进行的分析中，比较了标准短期HRV测量值和HR归一化短期HRV测量值的最佳组合的最佳准确性表现。使用遗传算法从这些测量值的所有可能组合中选择最佳特征。使用k近邻（KNN）分类器评估特征组合对这两个数据组进行分类的性能。结果表明，同时使用最小-最大归一化和HR归一化可提高分类性能。对于具有完美阳性预测值的KNN分类器，当k = 3和k = 5时，最大准确率达到93.98%。
