This paper proposes and implements an intuitive and pervasive solution for neonatal EEG monitoring assisted by sonification and deep learning AI that provides information about neonatal brain health to all neonatal healthcare professionals, particularly those without EEG interpretation expertise. The system aims to increase the demographic of clinicians capable of diagnosing abnormalities in neonatal EEG. The proposed system uses a low-cost and low-power EEG acquisition system. An Android app provides single-channel EEG visualization, traffic-light indication of the presence of neonatal seizures provided by a trained, deep convolutional neural network and an algorithm for EEG sonification, designed to facilitate the perception of changes in EEG morphology specific to neonatal seizures. The multifaceted EEG interpretation framework is presented and the implemented mobile platform architecture is analyzed with respect to its power consumption and accuracy.
@article{arxiv.1806.04037,
title = {Neonatal EEG Interpretation and Decision Support Framework for Mobile Platforms},
author = {Mark O'Sullivan and Sergi Gomez and Alison O'Shea and Eduard Salgado and Kevin Huillca and Sean Mathieson and Geraldine Boylan and Emanuel Popovici and Andriy Temko},
journal= {arXiv preprint arXiv:1806.04037},
year = {2018}
}