Localizing the Epileptogenic Zone Using SEEG-Based Excitation-Inhibition Dynamics and Spectral Features in Drug-Resistant Epilepsy: A Multicenter Retrospective Study

Drug-resistant epilepsy (DRE) affects millions of people worldwide and remains a major therapeutic challenge, largely due to the difficulty in precisely localizing the epileptogenic zone (EZ). Current electrophysiological biomarkers often lack robustness across ictal-interictal states and clinical centers. Furthermore, neuronal excitation-inhibition (E/I) dynamics - a key pathophysiological mechanism - has not yet been systematically characterized for clinical translation. In this multicenter retrospective study, we introduce a computationally grounded framework leveraging stereoelectroencephalography (SEEG) -derived 1/f spectral signatures as a proxy for E/I ratio to enable machine learning-guided EZ identification. We analyzed SEEG recordings capturing a total of 122 seizures from 38 patients with DRE who achieved complete seizure freedom (Engel Class I) post-surgery. Cohort-level analyses revealed that the EZ exhibited a more negative E/I ratio compared to non-epileptogenic zones (NEZ) across both interictal and ictal states (p < 0.001 , after FDR correction), indicating a significant imbalance of E/I dynamics. These findings were corroborated at the individual level, where 84.2% (32/38) and 68.4% (26/38) of patients showed significant EZ-NEZ separation during ictal and interictal periods, respectively (p < 0.05 , after FDR correction). This discriminative capacity was consistent across surgical modalities (resection/ablation) and clinical centers. By incorporating E/I dynamics and multi-band average power spectral density (PSD) as features to train 11 machine learning models (e.g. SVM, Random Forest), we found that the Random Forest classifier achieved 0.84 accuracy (AUC = 0.90) in EZ localization, demonstrating robust generalizability. The study established the E/I dynamics as a clinically translatable generalizable framework for refining surgical targeting in DRE. To promote reproducibility and community validation, the implementation code is publicly available at: https://github.com/wyl1994/Source-code-and-dataset/tree/main.