EEG-X: An Integrated Framework for Automated Quantitative EEG Analysis in Epilepsy Diagnosis.
TL;DR
A novel EEG decoding frame work that employs a graph convolutional neural network to capture the spatial dependencies across EEG channels, integrated with a multiresolution transformer block to capture short-term dynamics while preserving long-term contextual information.
Abstract
Accurate interpretation of electroencephalography (EEG) remains a major challenge in epilepsy diagnosis, particularly given that patient heterogeneity hinders the cross-subject generalization of artificial intelligence based algorithms. To address the complex spatio-temporal characteristics and long-range contextual dependencies of EEG signals, we propose a novel EEG decoding frame work. The framework employs a graph convolutional neural network to capture the spatial dependencies across EEG channels, integrated with a multiresolution transformer block to capture short-term dynamics while preserving long-term contextual information. This framework is used to develop a novel seizure detection algorithm, which has been validated on a large multicenter dataset in a cross subject manner. The algorithm demonstrated superior performance across diverse patient populations, achieving an accuracy of 96.5% and a false alarm rate of 1.61/h. To facilitate downstream clinical and research applications, we establish EEG-X, a cloud-based collaborative frame work that integrates the seizure detection algorithm along with our previously established interictal epileptiform detection and EEG source imaging methods. The framework supports collaborative annotation, enables browser-based multi-platform access without requiring specific software installation, and facilitates continuous monitoring in both clinical and home settings, demonstrating the feasibility and potential efficiency gains toward the practical implementation of quantitative EEG analysis for clinicians and researchers.