Discrimination Power of Short-Term Heart Rate Variability Measures for CHF Assessment
Discrimination Power of Short-Term Heart Rate Variability Measures for CHF Assessment
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DOI：
10.1109/titb.2010.2091647
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发表时间：
2011-01-01
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通讯作者：
Bracale, Marcello
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文献类型：
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作者：
Pecchia, Leandro;Melillo, Paolo;Bracale, Marcello
在这项研究中，我们研究了短期心率变异性（HRV）在区分正常受试者和慢性心力衰竭（CHF）患者方面的区分能力。我们分析了83例CHF患者1914.40 h的心电图，其中54例正常，29例CHF患者，分别为纽约心脏协会（NYHA） I、II、III型，从公共数据库中提取。按照指南，我们进行了时间和频率分析，以测量HRV特征。为了评估HRV特征的识别能力，我们设计了一种基于分类回归树（CART）方法的分类器，CART是一种非参数统计技术，对非正常医疗数据挖掘具有很强的有效性。主题分类的最佳特征子集包括相邻NN区间差的平方和（RMSSD）、总功率、高频功率和低频与高频功率之比（LF/HF）的平均值的平方根。我们开发的分类器的灵敏度和特异性分别为79.3%和100%。此外，我们证明，通过引入两个非标准特征Delta AVNN和Delta LF/HF，可以分别达到89.7%和100%的灵敏度和特异性，这两个特征分别解释了连续正常间隔（AVNN）和LF/HF在24小时内的平均值变化。我们的结果与其他类似的研究相当，但我们使用的方法特别有价值，因为它允许对分类过程进行完全人类可理解的描述，就可理解的“IF…然后……”规则。
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 Delta AVNN and Delta LF/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 humanunderstandable description of classification procedures, in terms of intelligible "IF ... THEN ... " rules.