Automated Removal of EKG Artifact From EEG Data Using Independent Component Analysis and Continuous Wavelet Transformation

被引：78
|
作者：
Hamaneh, Mehdi Bagheri [1 ]
Chitravas, Numthip [2 ]
Kaiboriboon, Kitti [2 ]
Lhatoo, Samden D. [2 ]
Loparo, Kenneth A. [1 ]
机构：
[1] Case Western Reserve Univ, Dept Elect Engn & Comp Sci, Cleveland, OH 44106 USA
[2] Univ Hosp Case Med Ctr, Epilepsy Ctr, Cleveland, OH 44106 USA
来源：
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | 2014年 / 61卷 / 06期
关键词：
Continuous wavelet transformation (CWT); electrocardiographic (EKG) artifact; electroencephalogram (EEG); independent component analysis (ICA); SOURCE SEPARATION; ELIMINATION; RECORDINGS; ICA; IDENTIFICATION; ALGORITHMS; HISTOGRAM; EMG;
D O I：
10.1109/TBME.2013.2295173
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
The electrical potential produced by the cardiac activity sometimes contaminates electroencephalogram (EEG) recordings, resulting in spiky activities that are referred to as electrocardiographic (EKG) artifact. For a variety of reasons it is often desirable to automatically detect and remove these artifacts. Especially, for accurate source localization of epileptic spikes in an EEG recording from a patient with epilepsy, it is of great importance to remove any concurrent artifact. Due to similarities in morphology between the EKG artifacts and epileptic spikes, any automated artifact removal algorithm must have an extremely low false-positive rate in addition to a high detection rate. In this paper, an automated algorithm for removal of EKG artifact is proposed that satisfies such criteria. The proposed method, which uses combines independent component analysis and continuous wavelet transformation, uses both temporal and spatial characteristics of EKG related potentials to identify and remove the artifacts. The method outperforms algorithms that use general statistical features such as entropy and kurtosis for artifact rejection.
引用
收藏
页码：1634 / 1641
页数：8
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