Automated analysis of postictal generalized electroencephalogram suppression for SUDEP risk stratification

Area of Science:

• Neuroscience
• Epilepsy Research
• Biomarker Development

Background:

• Sudden unexpected death in epilepsy (SUDEP) is a significant concern, affecting over 3000 individuals annually.
• Current biomarkers for SUDEP risk are limited, and postictal generalized electroencephalogram suppression (PGES) quantification is often subjective.
• Objective and scalable methods are needed to assess SUDEP risk and understand its underlying mechanisms.

Purpose of the Study:

• To develop and validate an automated algorithm for objective quantification of PGES duration.
• To investigate the relationship between injury severity, PGES duration, and SUDEP timing in traumatic brain injury (TBI) mouse models of epilepsy.
• To evaluate the potential of an HDAC inhibitor (SAHA) in modulating PGES duration and its translational relevance for SUDEP.

Main Methods:

• Development of a Cumulative Sum (CuSUM)-based automated algorithm to quantify PGES duration.
• Testing the algorithm in mouse models with varying degrees of TBI (1 mm and 2 mm) and SAHA treatment.
• Utilizing Bland-Altman analysis to compare automated measurements with expert assessments.

Main Results:

• The automated algorithm objectively quantified PGES duration with strong agreement compared to expert measurements.
• Injury severity influenced SUDEP timing and PGES duration; more severe TBI correlated with earlier SUDEP and shorter PGES.
• SAHA treatment further reduced PGES duration, indicating potential therapeutic implications for SUDEP.

Conclusions:

• Automated PGES quantification provides a reproducible and objective framework for analyzing EEG dynamics in epilepsy models.
• This scalable tool facilitates mechanistic studies and treatment evaluations for SUDEP.
• The findings support the development of clinically translatable SUDEP research and risk assessment strategies.