Quality estimation of the electrocardiogram using cross-correlation among leads.
Quality estimation of the electrocardiogram using cross-correlation among leads.
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DOI：
10.1186/s12938-015-0053-1
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发表时间：
2015-06-20
影响因子：
3.9
通讯作者：
Mark R
中科院分区：
文献类型：
--
作者：
Morgado E;Alonso-Atienza F;Santiago-Mozos R;Barquero-Pérez Ó;Silva I;Ramos J;Mark R
心电图（ECG）信号的快速且准确的质量估计是引起科学界相当大的兴趣的相关研究主题，特别是由于其对远程医疗监测系统的影响，其中ECG由未经训练的技术人员收集。近年来，许多研究已经解决了这个问题，显示在临床上可接受和不可接受的ECG记录之间的区分性能差。 本文提出了一种新的，简单，准确的算法来估计的质量，12导联心电图利用不同的导联之间的互协方差矩阵的结构。理想情况下，来自不同导联的ECG信号应该高度相关，因为它们捕获心脏的相同电激活过程。然而，在存在噪声或伪影的情况下，这些信号之间的协方差将受到影响。ECG信号协方差矩阵的特征值被馈送到三个不同的监督二进制分类器。 使用PhysioNet/CinC Challenge 2011数据评估了这些分类器的性能。我们的最佳质量分类器在测试集中达到了0.898的准确度，同时复杂度远低于参加挑战赛的参赛者的结果，因此适合在当前的蜂窝设备中实现。
Fast and accurate quality estimation of the electrocardiogram (ECG) signal is a relevant research topic that has attracted considerable interest in the scientific community, particularly due to its impact on tele-medicine monitoring systems, where the ECG is collected by untrained technicians. In recent years, a number of studies have addressed this topic, showing poor performance in discriminating between clinically acceptable and unacceptable ECG records. This paper presents a novel, simple and accurate algorithm to estimate the quality of the 12-lead ECG by exploiting the structure of the cross-covariance matrix among different leads. Ideally, ECG signals from different leads should be highly correlated since they capture the same electrical activation process of the heart. However, in the presence of noise or artifacts the covariance among these signals will be affected. Eigenvalues of the ECG signals covariance matrix are fed into three different supervised binary classifiers. The performance of these classifiers were evaluated using PhysioNet/CinC Challenge 2011 data. Our best quality classifier achieved an accuracy of 0.898 in the test set, while having a complexity well below the results of contestants who participated in the Challenge, thus making it suitable for implementation in current cellular devices.
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影响因子：
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