Epileptic seizures diagnosis and prognosis from EEG signals using heterogeneous graph neural network.

ABSTRACT: Epilepsy, often associated with neurodegenerative disorders following brain strokes, manifests as abnormal electrical activity bursts in the cerebral cortex, disrupting regular brain function. Electroencephalogram (EEG) recordings capture these distinctive brain signals, offering crucial insights into seizure detection and management. This study presents a novel approach leveraging a graph neural network (GNN) model with a heterogeneous graph representation to detect epileptic seizures from EEG data. Utilizing the well-established CHB-MIT EEG dataset for training and evaluation, the proposed method includes preprocessing steps such as signal segmentation, resampling, label encoding, normalization, and exploratory data analysis. We employed a standard train-test split with stratified sampli

SUBMITTER: Alasiry A 

PROVIDER: S-EPMC12190335 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature