Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance

Alison O'Shea; Gordon Lightbody; Geraldine Boylan; Andriy Temko

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arXiv · 1806.03044

Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance

Alison O'Shea·Gordon Lightbody·Geraldine Boylan·Andriy Temko

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Abstract

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.

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Alison O'Shea, Gordon Lightbody, Geraldine Boylan, Andriy Temko. 2018-06-08. Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance. https://arxiv.org/abs/1806.03044

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