Pecchia Leandro, Melillo Paolo, Sansone Mario, Bracale Marcello
Department of Biomedical, Electronic, and Telecommunication Engineering, University of Naples Federico II, Naples 80128, Italy. leandro.pecchia@ unina.it
IEEE Trans Inf Technol Biomed. 2011 Jan;15(1):40-6. doi: 10.1109/TITB.2010.2091647. Epub 2010 Nov 11.
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 1914.40 h of ECG of 83 patients of which 54 are normal and 29 are suffering from CHF with New York Heart Association (NYHA) classification I, II, and 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 nonparametric statistical technique, strongly effective on nonnormal medical data mining. The best subset of features for subject classification includes square root of the mean of the sum of the squares of differences between adjacent NN intervals (RMSSD), total power, high-frequencies power, and the ratio between low- and high-frequencies power (LF/HF). The classifier we developed achieved sensitivity and specificity values of 79.3 % and 100 %, respectively. Moreover, we demonstrated that it is possible to achieve sensitivity and specificity of 89.7 % and 100 %, respectively, by introducing two nonstandard features ΔAVNN and ΔLF/HF, which account, respectively, for variation over the 24 h 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.
在本研究中，我们调查了短期心率变异性（HRV）对区分正常受试者与慢性心力衰竭（CHF）患者的辨别能力。我们分析了83例患者的1914.40小时心电图，这些心电图由公共数据库提取，其中54例为正常受试者，29例为患有纽约心脏协会（NYHA）I、II和III级CHF的患者。按照指南，我们进行了时域和频域分析以测量HRV特征。为了评估HRV特征的辨别能力，我们基于分类与回归树（CART）方法设计了一个分类器，CART是一种非参数统计技术，对非正态医学数据挖掘非常有效。用于受试者分类的最佳特征子集包括相邻NN间期差值平方和的均值的平方根（RMSSD）、总功率、高频功率以及低频与高频功率之比（LF/HF）。我们开发的分类器的灵敏度和特异度值分别为79.3%和100%。此外，我们证明通过引入两个非标准特征ΔAVNN和ΔLF/HF，分别表示连续正常间期平均值（AVNN）和LF/HF在24小时内的变化，可以分别达到89.7%和100%的灵敏度和特异度。我们的结果与其他类似研究相当，但我们使用的方法特别有价值，因为它能够以可理解的“如果……那么……”规则，对分类过程进行完全可人为理解的描述。
