Towards Deeper Neural Networks for Neonatal Seizure Detection

Machine learning and more recently deep learning have become valuable tools in clinical decision making for neonatal seizure detection. This work proposes a deep neural network architecture which is capable of extracting information from long segments of EEG. Residual connections as well as data augmentation and a more robust optimizer are efficiently exploited to train a deeper architecture with an increased receptive field and longer EEG input. The proposed system is tested on a large clinical dataset of 4,570 hours of duration and benchmarked on a publicly available Helsinki dataset of 112 hours duration. The performance has improved from an AUC of 95.41% to an AUC of 97.73% when compared to a deep learning baseline.