ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Brain Imaging Methods
This article is part of the Research TopicInnovations in neonatal and infant neuroimaging: multi-modal approaches, and developmental insightsView all articles
Towards Automated Neonatal EEG Analysis: Multi-Center Validation of a Reliable Deep Learning Pipeline
Provisionally accepted
Tim Hermans1*
Anneleen Dereymaeker2,3
Katrien Lemmens2,3
Katrien Jansen2,4
Fatima Usman5Shellie Robinson6
Gunnar Naulaers2,3
Maarten De Vos1
Caroline Hartley5- 1Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
- 2Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- 3Neonatal Intensive Care Unit, University Hospitals Leuven, Leuven, Belgium
- 4Child Neurology, University Hospitals Leuven, Leuven, Belgium
- 5Department of Paediatrics, University of Oxford, Oxford, United Kingdom
- 6Department of Paediatrics, University of Oxford, Oxford, Belgium
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Objective: To evaluate the reliability and generalization of NeoNaid, a fully automated software tool for neonatal EEG analysis, based on functional brain age (FBA) estimation and sleep staging. Methods: NeoNaid combines a multi-task deep learning model with proposed quality control routines detecting artefacts, out-of-distribution inputs, and uncertain predictions. Based on a raw EEG input, it outputs one global FBA estimate and a continuous 2-state hypnogram. We validated performance on two independent hospital settings: an internal dataset (33 EEGs, 17 infants, median 900 minutes/recording) and an external dataset (38 EEGs, 24 infants, median 124 minutes/recording). Results: Quality control rejected comparable number of segments in the internal and external datasets, reducing extreme errors in FBA estimation, and modestly improving sleep staging accuracy. Across the internal and external data, NeoNaid achieved median absolute FBA errors of 0.50 and 0.55 weeks and Cohen's Kappa values of 0.89 and 0.87 for quiet sleep detection, respectively. Discussion: NeoNaid demonstrated improved reliability through integrated quality control and maintained performance across two independent datasets. By focusing on validation and trustworthiness, this work takes an essential step toward clinical adoption of automated neonatal EEG analysis and supports its utility for both NICU practice and large-scale research.
Keywords: automated analysis, clinical validation, Functional brain age, Neonatal EEG, Quality control, Sleep staging
Received: 19 Nov 2025; Accepted: 05 Feb 2026.
Copyright: © 2026 Hermans, Dereymaeker, Lemmens, Jansen, Usman, Robinson, Naulaers, De Vos and Hartley. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Tim Hermans
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