ECG removal in preterm EEG combining empirical mode decomposition and adaptive filtering

In neonatal electroencephalography (EEG) heart activity is a major source of artifacts which can lead to misleading results in automated analysis if they are not properly eliminated. In this work we propose a combination of empirical mode decomposition (EMD) and adaptive filtering (AF) to cancel electrocardiogram (ECG) noise in a simplified EEG montage for preterm infants. The introduction of EMD prior to AF allows to selectively remove ECG preserving at maximum the original characteristics of EEG. Cleaned signals improved up to 17% the correlation coefficient with original datasets in comparison with signals denoised solely with AF.