Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling
This work addresses the critical need for automated seizure detection in newborn infants in NICUs, offering a domain-specific solution that improves upon existing methods.
The paper tackles automated neonatal seizure detection by proposing STATENet, a deep learning framework designed to address challenges like dynamic seizure onset and distribution shifts, achieving significantly better performance on a real-world neonatal EEG dataset.
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.