Govindan R B, Kota Srinivas, Al-Shargabi Tareq, Massaro An N, Chang Taeun, du Plessis Adre
Division of Fetal and Transitional Medicine, Fetal Medicine Institute, Children's National Health System, 111 Michigan Ave., NW, Washington, DC 20010, USA.
Division of Fetal and Transitional Medicine, Fetal Medicine Institute, Children's National Health System, 111 Michigan Ave., NW, Washington, DC 20010, USA.
J Neurosci Methods. 2016 Sep 1;270:76-84. doi: 10.1016/j.jneumeth.2016.06.009. Epub 2016 Jun 9.
Electroencephalogram (EEG) signals are often contaminated by the electrocardiogram (ECG) interference, which affects quantitative characterization of EEG.
We propose null-coherence, a frequency-based approach, to attenuate the ECG interference in EEG using simultaneously recorded ECG as a reference signal. After validating the proposed approach using numerically simulated data, we apply this approach to EEG recorded from six newborns receiving therapeutic hypothermia for neonatal encephalopathy. We compare our approach with an independent component analysis (ICA), a previously proposed approach to attenuate ECG artifacts in the EEG signal. The power spectrum and the cortico-cortical connectivity of the ECG attenuated EEG was compared against the power spectrum and the cortico-cortical connectivity of the raw EEG.
The null-coherence approach attenuated the ECG contamination without leaving any residual of the ECG in the EEG.
We show that the null-coherence approach performs better than ICA in attenuating the ECG contamination without enhancing cortico-cortical connectivity.
Our analysis suggests that using ICA to remove ECG contamination from the EEG suffers from redistribution problems, whereas the null-coherence approach does not. We show that both the null-coherence and ICA approaches attenuate the ECG contamination. However, the EEG obtained after ICA cleaning displayed higher cortico-cortical connectivity compared with that obtained using the null-coherence approach. This suggests that null-coherence is superior to ICA in attenuating the ECG interference in EEG for cortico-cortical connectivity analysis.
脑电图（EEG）信号常被心电图（ECG）干扰污染，这会影响EEG的定量表征。
我们提出了零相干法，这是一种基于频率的方法，以同步记录的ECG作为参考信号来减弱EEG中的ECG干扰。在用数值模拟数据验证了所提出的方法后，我们将此方法应用于六名接受新生儿脑病治疗性低温治疗的新生儿记录的EEG。我们将我们的方法与独立成分分析（ICA）进行比较，ICA是先前提出的一种减弱EEG信号中ECG伪迹的方法。将去除ECG干扰后的EEG的功率谱和皮质-皮质连接性与原始EEG的功率谱和皮质-皮质连接性进行比较。
零相干法减弱了ECG污染，且EEG中未留下任何ECG残余。
我们表明，在减弱ECG污染且不增强皮质-皮质连接性方面，零相干法比ICA表现更好。
我们的分析表明，使用ICA去除EEG中的ECG污染存在重新分布问题，而零相干法不存在此问题。我们表明，零相干法和ICA法都能减弱ECG污染。然而，与使用零相干法获得的EEG相比，ICA清洗后获得的EEG显示出更高的皮质-皮质连接性。这表明在用于皮质-皮质连接性分析的EEG中减弱ECG干扰方面，零相干法优于ICA。
