Design and implementation of a multi-sensor newborn EEG seizure and background model with inter-channel field characterization

This paper presents a novel multi-sensor non-stationary EEG model; it is obtained by combining state of the art mono-sensor newborn EEG simulators, a multilayer newborn head model comprised of four homogeneous concentric spheres, a multi-sensor propagation scheme based on array processing and optical dispersion to calculate inter-channel attenuation and delay, and lastly, a multi-variable optimization paradigm using particle swarm optimization and Monte-Carlo simulations to validate the model for optimal conditions. Multi-sensor EEG of 7 newborns, comprised of seizure and background epochs, are analyzed using time-space, time-frequency, power maps and multi-sensor causality techniques. The outcomes of these methods are validated by medical insights and serve as a backbone for any assumptions and as performance benchmarks for the model to be evaluated against. The results obtained with the developed model show 85.7% averaged time-frequency correlation (which is the selected measure for similarity with real EEG) with 5.9% standard deviation, and the averaged error obtained is 34.6% with 8% standard deviation. The resulting performances indicate that the proposed model provides a suitable matching fit with real EEG in terms of their probability density function, inter-sensor attenuation and translation, and multi-sensor causality. They also demonstrate the model flexibility to generate new unseen samples by utilizing user-defined parameters, making it suitable for other relevant applications.