Lanquart J-P, Dumont M, Linkowski P
Sleep Laboratory, Department of Psychiatry, Erasme Academic Hospital Free University of Brussels, Belgium.
Med Eng Phys. 2006 Mar;28(2):156-65. doi: 10.1016/j.medengphy.2005.04.017. Epub 2005 Jun 6.
Spectral analysis is now a standard procedure for analyzing the electroencephalograms (EEG) obtained by polysomnographic recordings. These numerical methods assume an artifact-free EEG since artifacts create spurious spectral components. Our aim was the development of a QRS artifact removal technique that might be applied to full night EEG with a minimal human intervention. This technique should handle one EEG channel, with or without use of one ECG channel. Variance minimization, independent component analysis (ICA), morphological filters (MF) have been implemented. Careful attention has been given to define the MF structuring element. The tests on artifact-simulated and real data were checked on the residual ECG spectral components present in the cleaned EEG. The best results are obtained by the MF when the structuring element is an artifact template defined either directly on the EEG or on the ICA ECG component. Further developments are required to identify and subtract the T-wave artifacts.
频谱分析如今是分析通过多导睡眠图记录获得的脑电图（EEG）的标准程序。这些数值方法假定脑电图无伪迹，因为伪迹会产生虚假的频谱成分。我们的目标是开发一种QRS波伪迹去除技术，该技术可在最少人工干预的情况下应用于整夜脑电图。此技术应能处理一个脑电图通道，无论是否使用一个心电图通道。已实施了方差最小化、独立成分分析（ICA）、形态学滤波器（MF）。在定义形态学滤波器的结构元素时给予了仔细关注。对人工模拟数据和真实数据的测试是根据清洁后的脑电图中存在的残余心电图频谱成分进行检查的。当结构元素是直接在脑电图上或ICA心电图成分上定义的伪迹模板时，形态学滤波器能获得最佳结果。还需要进一步改进以识别和减去T波伪迹。
