Objective. To develop an automated algorithm to quantify background EEG abnormalities in full-term neonates with hypoxic ischemic encephalopathy.Approach. The algorithm classifies 1 h of continuous neonatal EEG (cEEG) into a mild, moderate or severe background abnormality grade. These classes are well established in the literature and a clinical neurophysiologist labeled 272 1 h cEEG epochs selected from 34 neonates. The algorithm is based on adaptive EEG segmentation and mapping of the segments into the so-called segments' feature space. Three features are suggested and further processing is obtained using a discretized three-dimensional distribution of the segments' features represented as a 3-way data tensor. Further classification has been achieved using recently developed tensor decomposition/classification methods that reduce the size of the model and extract a significant and discriminative set of features.Main results. Effective parameterization of cEEG data has been achieved resulting in high classification accuracy (89%) to grade background EEG abnormalities.Significance. For the first time, the algorithm for the background EEG assessment has been validated on an extensive dataset which contained major artifacts and epileptic seizures. The demonstrated high robustness, while processing real-case EEGs, suggests that the algorithm can be used as an assistive tool to monitor the severity of hypoxic insults in newborns.

background EEG classification, EEG quantification, multi-linear algebra, perinatal asphyxia, tensor classification
dx.doi.org/10.1088/1741-2560/11/6/066007, hdl.handle.net/1765/88132
Journal of Neural Engineering
This work was funded by the European Commission 7th Framework Programme; grant id erc/339804 - Biomedical Data Fusion using Tensor based Blind Source Separation (BIOTENSORS)
Department of Pediatrics

Matić, V, Cherian, P.J, Koolen, N, Naulaers, G, Swarte, R.M.C, Govaert, P, … de Vos, M. (2014). Holistic approach for automated background EEG assessment in asphyxiated full-term infants. Journal of Neural Engineering, 11(6). doi:10.1088/1741-2560/11/6/066007