Automatic Motion and Noise Artifact Detection in Holter ECG Data Using Empirical Mode Decomposition and Statistical Approaches


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We present a real-time method for the detection of motion and noise (MN) artifacts, which frequently interferes with accurate rhythm assessment when ECG signals are collected from Holter monitors. Our MN artifact detection approach involves two stages. The first stage involves the use of the first-order intrinsic mode function (F-IMF) from the empirical mode decomposition to isolate the artifacts’ dynamics as they are largely concentrated in the higher frequencies. The second stage of our approach uses three statistical measures on the F-IMF time series to look for characteristics of randomness and variability, which are hallmark signatures of MN artifacts: the Shannon entropy, mean, and variance. We then use the receiver–operator characteristics curve on Holter data from 15 healthy subjects to derive threshold values associated with these statistical measures to separate between the clean and MN artifacts’ data segments. With threshold values derived from 15 training data sets, we tested our algorithms on 30 additional healthy subjects. Our results show that our algorithms are able to detect the presence of MN artifacts with sensitivity and specificity of 96.63% and 94.73%, respectively. In addition, when we applied our previously developed algorithm for atrial fibrillation (AF) detection on those segments that have been labeled to be free from MN artifacts, the specificity increased from 73.66% to 85.04% without loss of sensitivity (74.48%–74.62%) on six subjects diagnosed with AF. Finally, the computation time was less than 0.2 s using a MATLAB code, indicating that real-time application of the algorithms is possible for Holter monitoring.
机译:我们提出了一种检测运动和噪声(MN)伪像的实时方法,当从Holter监视器收集ECG信号时,该方法经常会干扰准确的节奏评估。我们的MN伪影检测方法涉及两个阶段。第一阶段涉及从经验模式分解中使用一阶本征模式函数(F-IMF),以分离出人工产物的动力学,因为它们主要集中在较高的频率上。我们方法的第二阶段在F-IMF时间序列上使用三种统计量度来寻找随机性和变异性的特征,这是MN伪像的标志性特征:香农熵,均值和方差。然后,我们使用来自15位健康受试者的Holter数据的接收者-操作者特征曲线,得出与这些统计量度相关的阈值,以区分干净和MN伪像的数据段。利用从15个训练数据集中得出的阈值,我们在30个其他健康受试者上测试了我们的算法。我们的结果表明,我们的算法能够以96.63%和94.73%的灵敏度和特异性检测MN伪影的存在。此外,当我们将先前开发的算法用于心房纤颤(AF)检测的那些已标记为无MN伪影的节段时,特异性从73.66%提高至85.04%,而不会降低灵敏度(74.48%–74.62% )诊断为AF的六个对象。最后,使用MATLAB代码的计算时间不到0.2秒,这表明算法的实时应用对于Holter监视是可能的。



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