Detecting abnormality in heart dynamics from multifractal analysis of ECG signals.
Detecting abnormality in heart dynamics from multifractal analysis of ECG signals.
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DOI：
10.1038/s41598-017-15498-z
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发表时间：
2017-11-09
影响因子：
4.6
通讯作者：
Ambika G
中科院分区：
文献类型：
--
作者：
Shekatkar SM;Kotriwar Y;Harikrishnan KP;Ambika G
为了区分异常行为和正常行为，心脏动力学的特征是临床科学中一个有趣的话题。在这里，我们提出了一个分析的心电信号从健康和不健康的几个人的框架下，动力系统方法的多重分形分析。我们的分析与传统的非线性分析的不同之处在于，提取和量化了信号幅度变化中包含的信息。结果表明，心脏动力学的吸引子具有多重分形结构，多重分形谱的变化可以清楚地区分健康和不健康的受试者。我们使用有监督的机器学习方法建立了一个模型，该模型基于多重分形参数以非常高的精度预测新主题的组标签。通过将多重分形谱中的计算指数与来自同一心电的节拍复制数据的指数进行比较，我们展示了如何检查每个心电自身的变化。在不健康病例的测量中观察到的变异性增加可以作为检测心脏异常动力学的临床有意义的指标。
The characterization of heart dynamics with a view to distinguish abnormal from normal behavior is an interesting topic in clinical sciences. Here we present an analysis of the Electro-cardiogram (ECG) signals from several healthy and unhealthy subjects using the framework of dynamical systems approach to multifractal analysis. Our analysis differs from the conventional nonlinear analysis in that the information contained in the amplitude variations of the signal is being extracted and quantified. The results thus obtained reveal that the attractor underlying the dynamics of the heart has multifractal structure and the variations in the resultant multifractal spectra can clearly separate healthy subjects from unhealthy ones. We use supervised machine learning approach to build a model that predicts the group label of a new subject with very high accuracy on the basis of the multifractal parameters. By comparing the computed indices in the multifractal spectra with that of beat replicated data from the same ECG, we show how each ECG can be checked for variations within itself. The increased variability observed in the measures for the unhealthy cases can be a clinically meaningful index for detecting the abnormal dynamics of the heart.
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影响因子：
4.3
作者：
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通讯作者：
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影响因子：
1.8
作者：
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通讯作者：
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作者：
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通讯作者：
PROCACCIA, I
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作者：
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