Identification of Post-Ictal Generalised EEG Suppression with Two-Channel EEG

This study investigates the feasibility of using a two-channel subcutaneous EEG device (SubQ) to detect and monitor PGES. The SubQ device, developed by UNEEG Medical A/S, offers a minimally invasive alternative to scalp EEG, enabling ultra-long-term monitoring and remote data analysis. We used annotated scalp EEG data and data from the SubQ device. The pre-processing pipeline included channel reduction, resampling, filtering, and feature extraction. A Variational Auto-Encoder (VAE) was employed for anomaly detection, trained to identify PGES instances, and post-processing was applied to predict their duration. The VAE achieved a 100% detection rate for PGES in both scalp and SubQ datasets. However, the predicted durations had an average offset of 35.67 s for scalp EEG and 26.42 s for SubQ data. The model's false positive rate (FPR) was 59% for scalp EEG and 56% for SubQ data, indicating a need for further refinement to reduce false alarms. This study demonstrates the potential of subcutaneous EEG as a valuable tool in the study of epilepsy and the monitoring of PGES, ultimately contributing to a better understanding and management of SUDEP risk.