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.

[1]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[2]  M. Vecchierini,et al.  Normal EEG of premature infants born between 24 and 30 weeks gestational age: Terminology, definitions and maturation aspects , 2007, Neurophysiologie Clinique/Clinical Neurophysiology.

[3]  Patrick Flandrin,et al.  A complete ensemble empirical mode decomposition with adaptive noise , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[4]  B. Boashash,et al.  Preprocessing and time-frequency analysis of newborn EEG seizures , 2001, IEEE Engineering in Medicine and Biology Magazine.

[5]  Joydeep Bhattacharya,et al.  Correction of blink artifacts using independent component analysis and empirical mode decomposition. , 2010, Psychophysiology.

[6]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[7]  Thierry Dutoit,et al.  Cancelling ECG Artifacts in EEG Using a Modified Independent Component Analysis Approach , 2008, EURASIP J. Adv. Signal Process..

[8]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .