Muscle artifact removal from human sleep EEG by using independent component analysis
Muscle artifact removal from human sleep EEG by using independent component analysis
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DOI：
10.1007/s10439-008-9442-y
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发表时间：
2008-03-01
影响因子：
3.8
通讯作者：
Cantero, Jose L.
中科院分区：
文献类型：
--
作者：
Crespo-Garcia, Maite;Atienza, Mercedes;Cantero, Jose L.
肌肉伪影通常与正常和病理睡眠中的睡眠觉醒和觉醒相关，污染EEG记录并扭曲定量EEG结果。大多数EEG校正技术集中在眼伪影，但很少有研究已经做了从睡眠EEG记录去除肌肉活动。本研究旨在评估四种独立成分分析（伊卡）算法（AMUSE，SOBI，Infomax和JADE）在睡眠期间从EEG中分离肌源性活动的性能，以确定最佳方法。AMUSE、Infomax和SOBI在消除颞区肌肉伪影方面的表现明显优于JADE，但AMUSE与非颞区的信噪比无关，并且明显快于其余算法。当应用于不同睡眠阶段的真实的病例时，AMUSE进一步成功地将肌肉伪影与自发EEG觉醒分离。低计算成本的AMUSE，其出色的性能与EEG觉醒从不同的睡眠阶段支持伊卡算法作为一个有效的选择，以尽量减少肌肉伪影对人类睡眠EEG记录的影响。
Muscle artifacts are typically associated with sleep arousals and awakenings in normal and pathological sleep, contaminating EEG recordings and distorting quantitative EEG results. Most EEG correction techniques focus on ocular artifacts but little research has been done on removing muscle activity from sleep EEG recordings. The present study was aimed at assessing the performance of four independent component analysis (ICA) algorithms (AMUSE, SOBI, Infomax, and JADE) to separate myogenic activity from EEG during sleep, in order to determine the optimal method. AMUSE, Infomax, and SOBI performed significantly better than JADE at eliminating muscle artifacts over temporal regions, but AMUSE was independent of the signal-to-noise ratio over non-temporal regions and markedly faster than the remaining algorithms. AMUSE was further successful at separating muscle artifacts from spontaneous EEG arousals when applied on a real case during different sleep stages. The low computational cost of AMUSE, and its excellent performance with EEG arousals from different sleep stages supports this ICA algorithm as a valid choice to minimize the influence of muscle artifacts on human sleep EEG recordings.