Using Explainable AI for EEG-based Reduced Montage Neonatal Seizure Detection
arxiv(2024)
摘要
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
respectively.
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