ABSTRACT: Epilepsy is a prevalent neurological disorder affecting millions worldwide, in which people experience frequent seizures due to abnormal electrical activity in the brain. Early detection of structural brain changes associated with seizure conditions can enhance diagnosis and treatment planning. While Electroencephalography (EEG) is widely used for real-time seizure detection, Magnetic Resonance Imaging (MRI) provides structural information that can reveal abnormalities linked to seizure disorders. This paper proposes a novel framework for analyzing seizure-associated brain structural patterns in MRI images, combining Convolutional Neural Networks (CNN) with Horizontal Visibility Graph (HVG) construction and attention-based Graph Transformer classification. The preprocessing pipeline includes grayscale conversion, noise removal, and intensity normalization. Region-of-interest (ROI) signals are derived through spatial averaging of pixel intensities. These signals are converted to HVGs, where each data point becomes a node and edges are formed using horizontal visibility criteria. Graph-theoretic features—degree, clustering coefficient, and average shortest path length—are extracted to form a spatial structural feature set. An attention-based Graph Transformer classifier then performs binary classification into abnormal (seizure) and normal cases. The core innovation lies in a hybrid CNN + HVG + Graph Transformer architecture that jointly models spatial and structural properties of MRI data. It is important to note that the dataset uses tumor-affected MRI images as proxies for seizure-associated structural changes; while this is a recognized limitation, it allows exploration of the clinical hypothesis that tumor-induced structural alterations share characteristics with seizure-related atrophy. Experiments demonstrate an overall classification accuracy of 96.08%, a seizure class (Class 1) recall of 1.00, precision of 0.94, and F1-score of 0.97, outperforming traditional machine learning, CNN-only, RNN-based, and standard Graph Neural Network baselines. These results indicate that graph-based structural representation of MRI-derived signals can support neurological diagnosis with strong computational accuracy.
Keywords — Seizure-Associated Abnormalities, MRI, Visibility Graph, Horizontal Visibility Graph (HVG), CNN, Graph Transformer, Structural Representation, Deep Learning.