TY - CHAP
T1 - An implementation of an AI-assisted sonification algorithm for neonatal EEG seizure detection on an edge device
AU - O'sullivan, Feargal
AU - Quintana, Sergi Gomez
AU - Temko, Andriy
AU - Popovici, Emanuel
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Fast and accurate seizure detection is a challenging problem for neonates. This is due to a severe shortage of specialized medical professionals for EEG analysis, especially in disadvantaged communities. Fast artificial intelligence (AI) techniques have been proposed to compensate for this lack of expertise. However, such models lack explainability, which is a key feature for these models to be adopted by clinicians. AI-assisted sonification adds additional explainability to any such automated methodology, empowering the medical professional to take accurate decisions regardless of the level of expertise in EEG analysis. The feasibility of an implementation of such an algorithm on an edge device is presented and analyzed. A lightweight derived algorithm for resource-constrained implementation scenarios is also evaluated and presented, suggesting suitability for further ultra-low power, mobile and wearables implementations.
AB - Fast and accurate seizure detection is a challenging problem for neonates. This is due to a severe shortage of specialized medical professionals for EEG analysis, especially in disadvantaged communities. Fast artificial intelligence (AI) techniques have been proposed to compensate for this lack of expertise. However, such models lack explainability, which is a key feature for these models to be adopted by clinicians. AI-assisted sonification adds additional explainability to any such automated methodology, empowering the medical professional to take accurate decisions regardless of the level of expertise in EEG analysis. The feasibility of an implementation of such an algorithm on an edge device is presented and analyzed. A lightweight derived algorithm for resource-constrained implementation scenarios is also evaluated and presented, suggesting suitability for further ultra-low power, mobile and wearables implementations.
KW - AI-assisted sonification
KW - edge devices
KW - EEG
KW - fast EEG review
KW - low-cost embedded systems
UR - https://www.scopus.com/pages/publications/85143051958
U2 - 10.1109/BHI56158.2022.9926876
DO - 10.1109/BHI56158.2022.9926876
M3 - Chapter
AN - SCOPUS:85143051958
T3 - BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, Symposium Proceedings
BT - BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, Symposium Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2022
Y2 - 27 September 2022 through 30 September 2022
ER -