Towards Automated Neonatal EEG Analysis: Multi-Center Validation of a Reliable Deep Learning Pipeline
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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 an 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.
Conclusions
NeoNaid demonstrated improved reliability through integrated quality control and robust generalization across recording setups.
Significance
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.
