In this study, we investigated the discrimination power of short-term Heart Rate Variability (HRV) for discriminating normal subjects versus Chronic Heart Failure (CHF) patients. We analyzed 1,914.40 hours of ECG of 83 patients of which 54 are normal and 29 are suffering from CHF with New York Heart Classification (NYHA) I, II, III, extracted by public databases. Following guidelines, we performed time and frequency analysis in order to measure HRV features. To assess the discrimination power of HRV features we designed a classifier based on the Classification and Regression Tree (CART) method, which is a non-parametric statistical technique, strongly effective on non-normal medical data mining. The best subset of features for subject classification includes RMSSD, total power, high frequencies power and the ratio between low and high Frequencies power (LF/HF). The classifier we developed achieved specificity and sensitivity values of 79.31% and 100% respectively. Moreover, we demonstrated that it is possible to achieve specificity and sensitivity of 89.7% and 100% respectively, by introducing two non-standard features AVNN and LF/HF, which account respectively for variation over the 24 hours of the average of consecutive normal intervals (AVNN) and LF/HF. Our results are comparable with other similar studies, but the method we used is particularly valuable because it allows a fully human-understandable description of classification procedures, in terms of intelligible “if … then …” rules
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