A timely detection of seizures for newborn infants with electroencephalogram
(EEG) has been a common yet life-saving practice in the Neonatal Intensive Care
Unit (NICU). However, it requires great human efforts for real-time monitoring,
which calls for automated solutions to neonatal seizure detection. Moreover,
the current automated methods focusing on adult epilepsy monitoring often fail
due to (i) dynamic seizure onset location in human brains; (ii) different
montages on neonates and (iii) huge distribution shift among different
subjects. In this paper, we propose a deep learning framework, namely STATENet,
to address the exclusive challenges with exquisite designs at the temporal,
spatial and model levels. The experiments over the real-world large-scale
neonatal EEG dataset illustrate that our framework achieves significantly
better seizure detection performance.

本文提出了一种深度学习框架 STATENet，针对新生儿脑电图的动态癫痫发作位置、不同构架和巨大的分布变化等挑战，进行革新性的设计，针对大数据集进行实验，并在新生儿癫痫检测性能方面取得了显著的成果。