Pediatric and Adolescent Seizure Detection: A Machine Learning Approach Exploring the Influence of Age and Sex in Electroencephalogram Analysis

Background
Automated seizure detection methods remain limited in children and adolescents compared with adults. Developmental factors may influence EEG patterns and seizure detection performance.

Research

This study developed machine learning models using paediatric and adolescent EEG datasets and investigated the influence of age and sex on seizure detection performance.

Potential Impact

The findings highlight the importance of considering developmental characteristics when designing AI-based EEG analysis methods for younger populations.