The neonatal period is the most vulnerable time for the development of
seizures. Seizures in the immature brain lead to detrimental consequences,
therefore require early diagnosis. The gold-standard for neonatal seizure
detection currently relies on continuous video-EEG monitoring; which involves
recording multi-channel electroencephalogram (EEG) alongside real-time video
monitoring within a neonatal intensive care unit (NICU). However, video-EEG
monitoring technology requires clinical expertise and is often limited to
technologically advanced and resourceful settings. Cost-effective new
techniques could help the medical fraternity make an accurate diagnosis and
advocate treatment without delay. In this work, a novel explainable deep
learning model to automate the neonatal seizure detection process with a
reduced EEG montage is proposed, which employs convolutional nets, graph
attention layers, and fully connected layers. Beyond its ability to detect
seizures in real-time with a reduced montage, this model offers the unique
advantage of real-time interpretability. By evaluating the performance on the
Zenodo dataset with 10-fold cross-validation, the presented model achieves an
absolute improvement of 8.31% and 42.86% in area under curve (AUC) and recall,
respectively.

基于可解释深度学习模型的新生儿癫痫自动检测方法，具有实时可解释性，对于 Zenodo 数据集的性能评估中，该模型在曲线下面积（AUC）和召回率方面分别实现了 8.31% 和 42.86% 的绝对改进。