Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, USA.
Physiol Meas. 2012 Sep;33(9):1535-48. doi: 10.1088/0967-3334/33/9/1535. Epub 2012 Aug 17.
The 12-lead electrocardiography (ECG) is the gold standard for diagnosis of abnormalities of the heart. However, the ECG is susceptible to artifacts, which may lead to wrong diagnosis and thus mistreatment. It is a clinical challenge of great significance differentiating ECG artifacts from patterns of diseases. We propose a computational framework, called the matrix of regularity, to evaluate the quality of ECGs. The matrix of regularity is a novel mechanism to fuse results from multiple tests of signal quality. Moreover, this method can produce a continuous grade, which can more accurately represent the quality of an ECG. When tested on a dataset from the Computing in Cardiology/PhysioNet Challenge 2011, the algorithm achieves up to 95% accuracy. The area under the receiver operating characteristic curve is 0.97. The developed framework and computer program have the potential to improve the quality of ECGs collected using conventional and portable devices.
12 导联心电图（ECG）是诊断心脏异常的金标准。然而，心电图容易受到干扰，这可能导致误诊和不当治疗。因此，区分心电图干扰与疾病模式是一个具有重要临床意义的挑战。我们提出了一种计算框架，称为正则矩阵，用于评估心电图的质量。正则矩阵是一种融合多个信号质量测试结果的新机制。此外，这种方法可以产生一个连续的等级，更准确地表示心电图的质量。在 2011 年计算心脏病学/生理网络挑战赛的数据集上进行测试时，该算法的准确率高达 95%。接收器操作特性曲线下的面积为 0.97。所开发的框架和计算机程序有可能提高使用传统和便携式设备采集的心电图的质量。
