Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling

Understand

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.