Low-power real-time seizure monitoring via AI-assisted sonification of neonatal EEG

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dc.contributor.advisorPopovici, Emanuel
dc.contributor.advisorTemko, Andriy
dc.contributor.authorNguyen, Tien Vanen
dc.contributor.funderQualcomm
dc.date.accessioned2025-02-04T14:59:27Z
dc.date.available2025-02-04T14:59:27Z
dc.date.issued2024
dc.date.submitted2024
dc.description.abstractSeizures in newborns are most frequently provoked by an acute brain injury or systemic insult, making them predominantly acute symptomatic in nature. Early identification and intervention are essential to reducing the risks of mortality and long-term disabilities. Continuous electroencephalogram (EEG) monitoring remains the gold standard for accurate neonatal seizure detection. This procedure is expensive, time-consuming, and requires a high level of expertise. Recent advancements in artificial intelligence (AI) and machine learning (ML) have shown substantial promise in expediting seizure diagnosis. These algorithms can provide decision assistance with expert-level accuracy and thus are increasingly used for EEG analysis. However, the lack of transparency and explainability in the decision-making process of ML algorithms hinders their widespread adoption in medical settings. Researchers at the Embedded.Systems@UCC Group proposed a novel method that integrates machine learning with sonification, an alternative EEG interpretation approach by converting the signals into sound, to address the black-box problem while simultaneously improving seizure detection performance. The study also found that human listeners achieved better performance using AI-assisted audio analysis compared to using the ML seizure detection algorithm alone. This study presents a real-time processing design that allows the AI-integrated sonification method to operate in parallel with EEG acquisition. The new design eliminates the operational time delay associated with offline processing in the original study. This is particularly beneficial when the diagnosis is time-critical or in a busy environment where medical professionals have a tight schedule. A low-power implementation of the proposed real-time system is also presented. This implementation, featuring a system-on-chip with a deep neural networks accelerator, has an average power consumption of 13mW. It can be powered using a battery or a general USB port, allowing for a compact design, suitable for deployment in space-constrained environments like the neonatal intensive care units (NICU).en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationNguyen, T. V. 2024. Low-power real-time seizure monitoring via AI-assisted sonification of neonatal EEG. MRes Thesis, University College Cork.
dc.identifier.endpage84
dc.identifier.urihttps://hdl.handle.net/10468/16958
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2024, Tien Van Nguyen.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectSonification
dc.subjectReal-time systems
dc.subjectInternet of Medical Things
dc.subjectBiomedical monitoring
dc.subjectElectroencephalography
dc.subjectEmbedded software
dc.subjectArtificial intelligence
dc.subjectEdge AI
dc.subjectMachine learning
dc.subjectBiomedical signal processing
dc.subjectSignal processing algorithms
dc.subjectMicroprocessors
dc.titleLow-power real-time seizure monitoring via AI-assisted sonification of neonatal EEG
dc.typeMasters thesis (Research)en
dc.type.qualificationlevelMastersen
dc.type.qualificationnameMRes - Master of Researchen
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