ArticleNPJ digital medicine2025
Scaling convolutional neural networks achieves expert level seizure detection in neonatal EEG.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Differential quadruple pattern: A new EEG signal classification framework.IBRO neuroscience reports · 2026Article
- Amplitude integrated electroencephalography for neonatal seizure detection: an alternative opinion to the 2025 Cochrane review.Pediatric research · 2026Article
- An Automated Classifier of Harmful Brain Activities for Clinical Usage Based on a Vision-Inspired Pre-trained Framework.NPJ digital medicine · 2025Article
- Emerging modalities for neuroprognostication in neonatal encephalopathy: harnessing the potential of artificial intelligence.Pediatric research · 2025Review
- Neonatal seizure detection from EEG using inception ResNetV2 feature extraction and XGBoost optimized with particle swarm optimization.Scientific reports · 2025Article
- Extending the Branches of Neonatal Neurocritical Care.Seminars in perinatology · 2025Review
- Artificial intelligence in pediatric healthcare: current applications, potential, and implementation considerations.Clinical and experimental pediatrics · 2025Article
- Assistive Artificial Intelligence in Epilepsy and Its Impact on Epilepsy Care in Low- and Middle-Income Countries.Brain sciences · 2025Review
- Detection of neurologic changes in critically ill infants using deep learning on video data: a retrospective single center cohort study.EClinicalMedicine · 2024Article
- TATPat based explainable EEG model for neonatal seizure detection.Scientific reports · 2024Article
- Rewiring the brain: the AI revolution in epilepsy treatment.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Neonatal seizures require urgent treatment, but often go undetected without expert EEG monitoring. We have developed and validated a seizure detection model using retrospective EEG data from 332 neonates. A convolutional neural network was trained and tested on over 50,000 hours (n = 202) of annotated single-channel EEG containing 12,402 seizure events. This model was then validated on two independent multi-reviewer datasets (n = 51 and n = 79). Increasing data and model size improved performance: Matthews correlation coefficient (MCC) and Pearson's correlation (r) increased by up to 50% (15%) with data (model) scaling. The largest model (21m parameters) achieved state-of-the-art on an open-access dataset (MCC = 0.764, r = 0.824, and AUC = 0.982). This model also attained expert-level performance on both validation sets, a first in this field, with no significant difference in inter-rater agreement when the model replaces an expert (∣Δκ∣ < 0.094, p > 0.05).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.