# Scaling convolutional neural networks achieves expert-level seizure detection in neonatal EEG

arXiv: https://arxiv.org/abs/2405.09911 · HTML: https://mlsanity.com/paper/2405.09911/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 16 May 2024.

## Builds on (most cited first, 10 of 10)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Deep Double Descent: Where Bigger Models and More Data Hurt](https://mlsanity.com/paper/1912.02292/) (2019)
- [Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture](https://mlsanity.com/paper/2105.13854/) (2021)
- [Validating an SVM-based neonatal seizure detection algorithm for generalizability, non-inferiority and clinical efficacy](https://mlsanity.com/paper/2202.12023/) (2022)
- [Identifying trace alternant activity in neonatal EEG using an inter-burst detection approach](https://mlsanity.com/paper/2005.05559/) (2020)

## Built on by (most cited first, 5 of 5)

- [Honest and Reliable Evaluation and Expert Equivalence Testing of Automated Neonatal Seizure Detection](https://mlsanity.com/paper/2508.04899/) (2025)
- [Generalised Label-free Artefact Cleaning for Real-time Medical Pulsatile Time Series](https://mlsanity.com/paper/2504.21209/) (2025)
- [SPOTR: Spatio-temporal Pooling One-Token Reconstruction for Universal Physiological Signal Self-supervised Learning](https://mlsanity.com/paper/2606.21973/) (2026)
- [Large language models in medical time series analysis](https://mlsanity.com/paper/2609.22262/) (2026)
- [Expert-level detection of low-voltage intervals in EEG as a robust biomarker of neonatal encephalopathy](https://mlsanity.com/paper/2610.12680/) (2026)

## Abstract (arXiv)

Background: Neonatal seizures are a neurological emergency that require urgent treatment. They are hard to diagnose clinically and can go undetected if EEG monitoring is unavailable. EEG interpretation requires specialised expertise which is not widely available. Algorithms to detect EEG seizures can address this limitation but have yet to reach widespread clinical adoption. Methods: Retrospective EEG data from 332 neonates was used to develop and validate a seizure-detection model. The model was trained and tested with a development dataset (n=202) that was annotated with over 12k seizure events on a per-channel basis. This dataset was used to develop a convolutional neural network (CNN) using a modern architecture and training methods. The final model was then validated on two independent multi-reviewer datasets (n=51 and n=79). Results: Increasing dataset and model size improved model performance: Matthews correlation coefficient (MCC) and Pearson's correlation (r) increased by up to 50% with data scaling and up to 15% with model scaling. Over 50k hours of annotated single-channel EEG was used for training a model with 21 million parameters. State-of-the-art was achieved on an open-access dataset (MCC=0.764, r=0.824, and AUC=0.982). The CNN attains expert-level performance on both held-out validation sets, with no significant difference in inter-rater agreement among the experts and among experts and algorithm (Delta kappa < -0.095, p>0.05). Conclusion: With orders of magnitude increases in data and model scale we have produced a new state-of-the-art model for neonatal seizure detection. Expert-level equivalence on completely unseen data, a first in this field, provides a strong indication that the model is ready for further clinical validation.