Urrestarazu Elena, Iriarte Jorge, Alegre Manuel, Valencia Miguel, Viteri César, Artieda Julio
Clinical Neurophysiology Section, Department of Neurology, Clinica Universitaria/Foundation for Applied Medical Research, School of Medicine, University of Navarra, Navarra, Spain.
Epilepsia. 2004 Sep;45(9):1071-8. doi: 10.1111/j.0013-9580.2004.12104.x.
Independent component analysis (ICA) is a novel algorithm able to separate independent components from complex signals. Studies in interictal EEG demonstrate its usefulness to eliminate eye, muscle, 50-Hz, electrocardiogram (ECG), and electrode artifacts. The goal of this study was to evaluate the usefulness of ICA in removing artifacts in ictal recordings with a known EEG onset.
We studied 20 seizures of nine patients with focal epilepsy monitored in our video-EEG monitoring unit. ICA was applied to remove obvious artifacts in segments at the beginning of the seizure. The final EEGs were exported to the original format and were compared with the original EEG by two blinded examiners. We compared original recordings and the samples cleaned by digital filters (DFs), ICA and ICA plus digital filters (ICA + DFs), evaluating the possibility of finding an ictal pattern, the localization of the onset in area and time, and the global quality of the sample.
All the recordings except one (95%) improved after the use of ICA for the elimination of blinking and other artifacts. Three seizures were found in which in the original recordings did not permit us to detect an ictal pattern, and after ICA + DFs, an ictal onset was evident; in two of them, ICA alone was able to show this pattern. The best results in all the scores were obtained with ICA + DF. ICA was better than DFs. The agreement between the two reviewers was highly significant.
ICA is useful to remove artifacts from ictal recordings. When applied to ictal recordings, it increases the quality of the recording. In some cases, ICA may be useful to show ictal onsets obscured by artifacts. ICA + DFs obtained the best results regarding removal of the artifacts.
独立成分分析（ICA）是一种能够从复杂信号中分离出独立成分的新型算法。发作间期脑电图研究表明，它有助于消除眼电、肌电、50赫兹干扰、心电图（ECG）和电极伪迹。本研究的目的是评估ICA在去除已知脑电图起始的发作期记录中的伪迹方面的有效性。
我们研究了在我们的视频脑电图监测单元中监测的9例局灶性癫痫患者的20次发作。应用ICA去除发作开始时各段中的明显伪迹。最终的脑电图被导出为原始格式，并由两名不知情的检查人员与原始脑电图进行比较。我们比较了原始记录以及经数字滤波器（DFs）、ICA和ICA加数字滤波器（ICA + DFs）处理后的样本，评估发现发作期模式的可能性、发作起始在区域和时间上的定位以及样本的整体质量。
除一例（95%）外，所有记录在使用ICA消除眨眼和其他伪迹后均有改善。发现有3次发作，在原始记录中无法检测到发作期模式，而在使用ICA + DFs后，发作起始明显；其中2次发作仅使用ICA就能显示出这种模式。在所有评分中，使用ICA + DF获得了最佳结果。ICA比DFs更好。两位审阅者之间的一致性非常显著。
ICA有助于从发作期记录中去除伪迹。应用于发作期记录时，它可提高记录质量。在某些情况下，ICA可能有助于显示被伪迹掩盖的发作起始。在去除伪迹方面，ICA + DFs取得了最佳效果。
