Effect of electrocardiogram interference on cortico-cortical connectivity analysis and a possible solution

- A null-coherence approach is introduced to attenuate the ECG contamination in EEG.
- The method is validated on simulated data.
- It is applied to EEG of infants on therapeutic hypothermia for neonatal encephalopathy.
- The null-coherence approach proved superior to ICA.

BackgroundElectroencephalogram (EEG) signals are often contaminated by the electrocardiogram (ECG) interference, which affects quantitative characterization of EEG.New methodWe 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.ResultsThe null-coherence approach attenuated the ECG contamination without leaving any residual of the ECG in the EEG.Comparison with existing methodWe show that the null-coherence approach performs better than ICA in attenuating the ECG contamination without enhancing cortico-cortical connectivity.ConclusionOur 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.