Neonatal EEG Interpretation and Decision Support Framework for Mobile Platforms
This addresses the challenge of diagnosing neonatal brain abnormalities for healthcare professionals lacking EEG expertise, though it appears incremental in applying existing methods to a new domain.
The paper tackles the problem of neonatal EEG interpretation by developing a mobile platform that combines deep learning, sonification, and visualization to detect seizures, achieving analysis with low power consumption and unspecified accuracy metrics.
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.