Machine-learning competition to grade EEG background patterns in newborns with hypoxic-ischaemic encephalopathy

• URL: http://arxiv.org/abs/2509.09695v2
• Date: Thu, 30 Oct 2025 13:14:13 GMT
• Title: Machine-learning competition to grade EEG background patterns in newborns with hypoxic-ischaemic encephalopathy
• Authors: Fabio Magarelli, Geraldine B. Boylan, Saeed Montazeri, Feargal O'Sullivan, Dominic Lightbody, Minoo Ashoori, Tamara Skoric, John M. O'Toole,
• Abstract summary: We compiled a retrospective dataset containing 353 hours of EEG from 102 individual newborns from a multi-centre study.<n>The data was fully anonymised and divided into training, testing, and held-out validation datasets.<n>Next, we created a web-based competition platform and hosted a machine learning competition to develop ML models for classifying the severity of EEG background patterns in newborns.
• Score: 1.0118253437732931
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Machine learning (ML) has the potential to support and improve expert performance in monitoring the brain function of at-risk newborns. Developing accurate and reliable ML models depends on access to high-quality, annotated data, a resource in short supply. ML competitions address this need by providing researchers access to expertly annotated datasets, fostering shared learning through direct model comparisons, and leveraging the benefits of crowdsourcing diverse expertise. We compiled a retrospective dataset containing 353 hours of EEG from 102 individual newborns from a multi-centre study. The data was fully anonymised and divided into training, testing, and held-out validation datasets. EEGs were graded for the severity of abnormal background patterns. Next, we created a web-based competition platform and hosted a machine learning competition to develop ML models for classifying the severity of EEG background patterns in newborns. After the competition closed, the top 4 performing models were evaluated offline on a separate held-out validation dataset. Although a feature-based model ranked first on the testing dataset, deep learning models generalised better on the validation sets. All methods had a significant decline in validation performance compared to the testing performance. This highlights the challenges for model generalisation on unseen data, emphasising the need for held-out validation datasets in ML studies with neonatal EEG. The study underscores the importance of training ML models on large and diverse datasets to ensure robust generalisation. The competition's outcome demonstrates the potential for open-access data and collaborative ML development to foster a collaborative research environment and expedite the development of clinical decision-support tools for neonatal neuromonitoring.

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