Automated Seizure Detection in Animal EEG Signals

Automated seizure detection in animal electroencephalography (EEG) is crucial for accelerating epilepsy research. While machine learning (ML) and deep learning (DL) techniques have shown promise in seizure detection for human EEG and some rodent models, their application to chemically diverse animal data remains limited. In particular, no prior work has explored deep learning approaches for EEG data where seizure/epilepsy was induced by exposure to Soman (GD), a potent agent known to generate complex and variable seizure dynamics. In this work, we evaluate deep recurrent neural networks--Gated Recurrent Units (GRU) and Long Short-Term Memory networks (LSTM)--for seizure detection in EEG signals acquired by single channel (bipotential electrodes) from the rats exposed to either kainate or GD, which later animals developed epilepsy (measured by spontaneously recurring seizures). We benchmark these models against classical baselines including Random Forest and XGBoost, using intracranial EEG data from eight animals. Our results show that GRU and LSTM substantially outperform other shallow models in both accuracy and robustness across EEG traces.