EpiLENS：基于多中心颅内脑电图的患者相对致痫区定位
EpiLENS: Patient-Relative Epileptogenic Zone Localization from Multi-Center Intracranial EEG
浏览论文内容
中文总结 AI 辅助
本研究提出EpiLENS框架，通过保守双证据定位策略结合PRQ-Net与BCR-Net分支，在多中心iEEG数据上实现更准确的患者相对致痫区定位，性能优于基线方法且泛化性良好。
中文摘要 AI 辅助
耐药性癫痫仍是重大临床挑战，成功的神经外科手术高度依赖准确的致痫区（EZ）定位，需考虑患者、发作、电极植入布局、记录系统及临床中心间的显著差异。现有颅内脑电图（iEEG）方法通常依赖全局训练的通道级分类器，往往掩盖患者特异性电生理异常，且在严重类别不平衡和嘈杂的临床标注下表现不稳定。为解决这些局限，我们提出EpiLENS，一种用于患者相对致痫区定位的初级引导不对称双分支框架。其定位策略为保守双证据定位（CDEL），在推理时不对称结合独立训练但互补的分支：患者相对分位数网络（PRQ-Net），捕捉与每位患者内部电生理基线一致的发作偏差；边界覆盖排序网络（BCR-Net），强调模糊的致痫区或非致痫区边界并恢复患者特异性致痫集合。CDEL保留PRQ-Net作为初级分支，同时纳入来自BCR-Net的互补患者级排序证据。对异质性四中心队列的实验表明，相较于经典基于特征和原始iEEG的神经基线方法，其平衡定位性能有所提升；组件消融和患者内置换分析支持患者相对归一化、低尾发作聚合及边界覆盖排序的贡献。跨发作和留一中心泛化实验进一步证实，所提出的患者相对证据在重复记录中保持稳健，并可有效迁移至未见过的临床中心。
英文摘要
Drug-resistant epilepsy remains a major clinical challenge, as successful neurosurgery depends critically on accurate epileptogenic zone (EZ) localization accounting for substantial variability across patients, seizures, implantation layouts, recording systems and clinical centers. Existing intracranial electroencephalogram (iEEG) methods typically rely on channel-wise classifiers trained globally, which often obscure patient-specific electrophysiological abnormalities and exhibit instability under severe class imbalance and noisy clinical annotations. To address these limitations, we present EpiLENS, a primary-guided asymmetric dual-branch framework for patient-relative epileptogenic localization. Its localization strategy, Conservative Dual-Evidence Localization (CDEL), asymmetrically combines independently trained but complementary branches at inference: the Patient-Relative Quantile Network (PRQ-Net), which captures seizure-consistent deviations from each patient's internal electrophysiological baseline, and the Boundary-Coverage Ranking Network (BCR-Net), which emphasizes ambiguous epileptogenic zone or non-epileptogenic zone boundaries and recovery of the patient-specific epileptogenic set. CDEL retains PRQ-Net as the primary branch while incorporating complementary patient-wise ranking evidence from BCR-Net. Experiments on a heterogeneous four-center cohort demonstrate improved balanced localization over classical feature-based and raw-iEEG neural baselines, while component ablations and within-patient permutation analyses support the contributions of patient-relative normalization, lower-tail seizure aggregation, and boundary-coverage ranking. Cross-seizure and leave-one-center-out generalization experiments further confirm that the proposed patient-relative evidence remains robust across repeated recordings and transfers effectively to unseen clinical centers.
