This study aims to identify the epileptogenic zone (EZ) during the interictal period in epilepsy patients using electrocorticography data from four individuals. The proposed localization method, which constructs two brain connectivity networks: autoregressive and directed transfer function networks, holds significant potential. Network features are extracted using graph theory techniques employed in machine learning models to classify electrode locations as either part of the EZ. Six node features from the directed graph are selected: indegree, outdegree, cluster coefficient, PageRank, hubs, and community. A balanced support vector machine (SVM) addressed data imbalance. The balanced SVM method achieves the accuracy, precision, and recall of 0.775, 0.475, and 0.554, respectively. The results suggest that the node features of the epileptic network may provide critical information for clinical EZ localization, offering a promising avenue for future research and clinical practice.
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