Patient-Specific Non-Invasive Epileptogenic Zone Localization via High-Resolution Time-Frequency Representations and CNN Deep Learning.

Stereoelectroencephalography (SEEG)-guided radiofrequency thermocoagulation is the mainstream treatment for drug-resistant epilepsy (DRE), yet non-invasive patient-specific localization of potential epileptogenic zone (EZ) prior to SEEG electrode implantation remains a critical unmet clinical need, hindered by limited automation, suboptimal accuracy, and poor cross-patient generalizability. To address these gaps, we developed an automated non-invasive EZ localization framework that integrates scalp EEG source imaging, high-resolution time-frequency analysis, and deep learning. This multicenter retrospective study included 97 seizure episodes from 37 surgically confirmed DRE patients with Engel Class I post-operative seizure freedom. Three time-frequency spectrograms were generated based on the source-reconstructed signal, and fed into four deep learning architectures (ResNet, VGG, DenseNet, Swin Transformer) for channel-level EZ binary classification with patient-level leave one-out cross-validation to validate personalized localization for unseen patients. The ResNet18-Superlet combination achieved optimal performance (accuracy = 81.15% ± 4.83%, AUC = 84.82% ± 5.27%) in the adult cohort, with robust generalization to pediatric and mixed cohorts, significantly outperforming conventional high-frequency oscillation (HFO)-based methods. This framework enables accurate personalized pre-surgical EZ map ping to optimize SEEG implantation planning, with open-source code available at https://github.com/wyl1994/Source-code-for Patient-Specific-Non-Invasive-Epileptogenic-Zone-Localization to ensure reproducibility and clinical translation.