Neonatal EEG Interpretation and Decision Support Framework for Mobile Platforms

TL;DR

This paper introduces a mobile platform that combines deep learning and sonification to assist non-expert clinicians in neonatal EEG interpretation, aiming to improve early detection of brain abnormalities.

Contribution

It presents a low-cost, low-power mobile system integrating EEG visualization, seizure detection via deep learning, and sonification for enhanced neonatal brain monitoring.

Findings

The system achieves accurate seizure detection with low power consumption.

Sonification improves perception of EEG changes related to seizures.

The platform increases accessibility of EEG interpretation for non-specialists.

Abstract

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.