# Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance

arXiv: https://arxiv.org/abs/1806.03044 · HTML: https://mlsanity.com/paper/1806.03044/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2018.

## Built on by (most cited first, 3 of 3)

- [Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture](https://mlsanity.com/paper/2105.13854/) (2021)
- [Ensemble learning using individual neonatal data for seizure detection](https://mlsanity.com/paper/2204.07043/) (2022)
- [Deep Learning for EEG Seizure Detection in Preterm Infants](https://mlsanity.com/paper/2106.00611/) (2021)

## Abstract (arXiv)

This study presents a novel, deep, fully convolutional architecture which is optimized for the task of EEG-based neonatal seizure detection. Architectures of different depths were designed and tested; varying network depth impacts convolutional receptive fields and the corresponding learned feature complexity. Two deep convolutional networks are compared with a shallow SVM-based neonatal seizure detector, which relies on the extraction of hand-crafted features. On a large clinical dataset, of over 800 hours of multichannel unedited EEG, containing 1389 seizure events, the deep 11-layer architecture significantly outperforms the shallower architectures, improving the AUC90 from 82.6% to 86.8%. Combining the end-to-end deep architecture with the feature-based shallow SVM further improves the AUC90 to 87.6%. The fusion of classifiers of different depths gives greatly improved performance and reduced variability, making the combined classifier more clinically reliable.