A method for AI assisted human interpretation of neonatal EEG

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dc.contributor.authorGómez-Quintana, Sergi
dc.contributor.authorO'Shea, Alison
dc.contributor.authorFactor, Andreea
dc.contributor.authorPopovici, Emanuel
dc.contributor.authorTemko, Andriy
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2022-08-03T13:59:06Z
dc.date.available2022-08-03T13:59:06Z
dc.date.issued2022-06-29
dc.date.updated2022-08-03T13:28:05Z
dc.description.abstractThe study proposes a novel method to empower healthcare professionals to interact and leverage AI decision support in an intuitive manner using auditory senses. The methodâ s suitability is assessed through acoustic detection of the presence of neonatal seizures in electroencephalography (EEG). Neurophysiologists use EEG recordings to identify seizures visually. However, neurophysiological expertise is expensive and not available 24/7, even in tertiary hospitals. Other neonatal and pediatric medical professionals (nurses, doctors, etc.) can make erroneous interpretations of highly complex EEG signals. While artificial intelligence (AI) has been widely used to provide objective decision support for EEG analysis, AI decisions are not always explainable. This work developed a solution to combine AI algorithms with a human-centric intuitive EEG interpretation method. Specifically, EEG is converted to sound using an AI-driven attention mechanism. The perceptual characteristics of seizure events can be heard using this method, and an hour of EEG can be analysed in five seconds. A survey that has been conducted among targeted end-users on a publicly available dataset has demonstrated that not only does it drastically reduce the burden of reviewing the EEG data, but also the obtained accuracy is on par with experienced neurophysiologists trained to interpret neonatal EEG. It is also shown that the proposed communion of a medical professional and AI outperforms AI alone by empowering the human with little or no experience to leverage AI attention mechanisms to enhance the perceptual characteristics of seizure events.en
dc.description.sponsorshipScience Foundation Ireland (Centre for Research Training in Artificial Intelligence under Grant No 18/CRT/6223); INSIGHT Centre for Data Analytics, Grant No 2/RC/2289-P2)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid10932en
dc.identifier.citationGómez-Quintana, S., O'Shea, A., Factor, A., Popovici, E. and Temko, A. (2022) 'A method for AI assisted human interpretation of neonatal EEG', Scientific Reports, 12, 10932 (13pp). doi: 10.1038/s41598-022-14894-4en
dc.identifier.doi10.1038/s41598-022-14894-4en
dc.identifier.eissn2045-2322
dc.identifier.endpage13en
dc.identifier.journaltitleScientific Reportsen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/13449
dc.identifier.volume12en
dc.language.isoenen
dc.publisherSpringer Nature Switzerland AGen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/
dc.relation.urihttps://doi.org/10.5281/zenodo.2547147
dc.rights© 2022, the Authors. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectAIen
dc.subjectSeizureen
dc.subjectAuditory sensesen
dc.titleA method for AI assisted human interpretation of neonatal EEGen
dc.typeArticle (peer-reviewed)en
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