基于奇异谱分析的心电信号肌电信号伪影去除

YMER Digital Pub Date : 2022-08-11 DOI:10.37896/ymer21.08/36
Animesh Sarangi, Bal Gopal Mishra, Satyabhama Dash
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引用次数: 1

摘要

肌电图(EMG)或肌肉伪影经常影响心电图(ECG)读数。这些伪影使得心电信号中所需要的信息难以被看到。在这项研究中,我们引入了奇异谱分析(SSA),这是一种强大的基于子空间的方法,用于从心电数据中去除肌电信号伪影。为了有效地从受污染的心电数据中提取出需要的分量,我们提出了一种新的分组方法并设置了阈值。首先,一个称为嵌入的过程将单通道信号转换为多个通道的信号或数据。然后使用奇异值分解(SVD)从多通道数据的协方差矩阵中计算正交特征向量。选择一个阈值来定位这些特征向量,利用这些特征向量来生成所需的子空间。在确定子空间后,将多通道数据简单地投影到子空间中,然后使用对角平均方法生成原始时间序列并提取心电信号。关键词:心电图;肌电信号伪影;奇异谱分析;
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Singular Spectrum Analysis Based EMG Artifact Removal from ECG Signal
Electromyogram (EMG) or muscle artifacts frequently affect electrocardiogram (ECG) readings. These artifacts make the required information in the ECG signal difficult to see. In this study, we introduced the singular spectrum analysis (SSA), a powerful subspace-based method for removing EMG artifacts from ECG data. In order to effectively extract the desired component from the tainted ECG data, we presented a new grouping approach and set a threshold. First, a process known as embedding converts a single channel signal into several channels of signals or data. The orthogonal eigenvectors are then calculated using singular value decomposition(SVD) from the multichannel data's covariance matrix. A threshold is selected to locate these eigenvectors, which are utilized to generate the required subspace. After locating the subspace, the multichannel data is simply projected into it, followed by a method called diagonal averaging which will create the original time series and extract the ECG signals. Keywords: Electrocardiogram, EMG artifact, Singular Spectrum Analysis, Embedding, SVD, Mobility.
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