@inproceedings{f0fb025270334631a4b18fb138afafda,
title = "An EEG analysis framework through AI and sonification on low power IoT edge devices",
abstract = "This study explores the feasibility of implementation of an analysis framework of neonatal EEG, including ML, sonification and intuitive visualization, on a low power IoT edge device. Electroencephalography (EEG) analysis is a very important tool to detect brain disorders. Neonatal seizure detection is a known, challenging problem. Under-resourced communities across the globe are particularly affected by the cost associated with EEG analysis and interpretation. Machine learning (ML) techniques have been successfully utilized to automate seizure detection in neonatal EEG, in order to assist a healthcare professional in visual analysis. Several usage scenarios are reviewed in this study. It is shown that both sonification and ML can be efficiently implemented on low-power edge platforms without any loss of accuracy. The developed platform can be easily expanded to address EEG analysis applications in neonatal and adult population.",
keywords = "AI, CNN, edge devices, EEG, FM/AM sonification, IoT, low power, real-time EEG analysis",
author = "Sergi Gomez-Quintana and Grainne Cowhig and Marco Borzacchi and Alison O'Shea and Andriy Temko and Emanuel Popovici",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 ; Conference date: 01-11-2021 Through 05-11-2021",
year = "2021",
doi = "10.1109/EMBC46164.2021.9630253",
language = "English",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "277--280",
booktitle = "43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021",
address = "United States",
}