Nonlinear analysis of the heartbeats in public patient ECGs using an automated PD2i algorithm for risk stratification of arrhythmic death.
Nonlinear analysis of the heartbeats in public patient ECGs using an automated PD2i algorithm for risk stratification of arrhythmic death.
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DOI：
10.2147/tcrm.s2521
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发表时间：
2008-04
影响因子：
2.8
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中科院分区：
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心率变异性（HRV）反映了心脏自主神经功能和糖尿病死亡（AD）的风险。基于线性随机模型的HRV指标降低是心肌梗死后队列中AD的独立危险因素。基于非线性确定性模型的指数在回顾性数据中预测AD具有显著更高的灵敏度和特异性。需要一种容易被医疗技术人员使用的非线性分析软件。在当前的研究中，一个自动化的非线性算法，时间相关的点关联维数（PD 2 i），进行了评估。心电图（ECG）数据通过美国国立卫生研究院赞助的互联网档案（PhysioBank）提供，包括所有22个恶性心律失常ECG文件（VF/VT）和22个随机选择的心律失常文件作为对照。通过自动化软件（Vicor 2.0，Vicor Technologies，Inc.，博卡拉顿，佛罗里达州），并显示所有可分析的VF/VT文件的PD 2 i < 1.4 and all analyzable controls had PD2i >1.4。排除了5例VF/VT和6例对照，因为替代试验显示RR间期包含噪声，可能是ECG数字化率低所致。敏感性100%，特异性85%，相对危险度> 100; p < 0.01，把握度> 90%.因此，通过时间依赖性非线性PD 2 i算法进行的自动心跳分析可以准确地对可用于算法竞争性测试的公共数据中的AD风险进行分层。
Heart rate variability (HRV) reflects both cardiac autonomic function and risk of arrhythmic death (AD). Reduced indices of HRV based on linear stochastic models are independent risk factors for AD in post-myocardial infarct cohorts. Indices based on nonlinear deterministic models have a significantly higher sensitivity and specificity for predicting AD in retrospective data. A need exists for nonlinear analytic software easily used by a medical technician. In the current study, an automated nonlinear algorithm, the time-dependent point correlation dimension (PD2i), was evaluated. The electrocardiogram (ECG) data were provided through an National Institutes of Health-sponsored internet archive (PhysioBank) and consisted of all 22 malignant arrhythmia ECG files (VF/VT) and 22 randomly selected arrhythmia files as the controls. The results were blindly calculated by automated software (Vicor 2.0, Vicor Technologies, Inc., Boca Raton, FL) and showed all analyzable VF/VT files had PD2i < 1.4 and all analyzable controls had PD2i > 1.4. Five VF/VT and six controls were excluded because surrogate testing showed the RR-intervals to contain noise, possibly resulting from the low digitization rate of the ECGs. The sensitivity was 100%, specificity 85%, relative risk > 100; p < 0.01, power > 90%. Thus, automated heartbeat analysis by the time-dependent nonlinear PD2i-algorithm can accurately stratify risk of AD in public data made available for competitive testing of algorithms.