用机器学习帮医生快速找出癫痫手术关键脑区
AI-Driven SEEG Channel Ranking for Epileptogenic Zone Localization
- 结合临床判断与计算特征，用XGBoost模型识别发作期关键电极通道
- 通过SHAP评分排序，5例患者中定位准确率显著提升
- 可发现医生遗漏的可疑病灶区，适合神经外科术前评估
立体脑电图（SEEG）是一种侵入性技术，通过植入深部电极记录大量通道信号，用于术前评估。传统人工查看数百个通道信号耗时低效。本文提出一种机器学习方法，融合临床选择与计算结果对SEEG通道进行重要性排序。采用XGBoost分类模型学习发作期各通道的判别特征，再利用SHAP解释方法量化每个通道对癫痫发作的贡献度并进行排序。同时引入通道扩展策略，扩大搜索范围，识别出超出临床初步选择的可疑致痫区。在5例患者的SEEG数据上验证，该方法在准确性、一致性与可解释性方面均表现优异。
原文摘要 · Abstract (English)
Stereo-electroencephalography (SEEG) is an invasive technique to implant depth electrodes and collect data for pre-surgery evaluation. Visual inspection of signals recorded from hundreds of channels is time consuming and inefficient. We propose a machine learning approach to rank the impactful channels by incorporating clinician's selection and computational finding. A classification model using XGBoost is trained to learn the discriminative features of each channel during ictal periods. Then, the SHapley Additive exPlanations (SHAP) scoring is utilized to rank SEEG channels based on their contribution to seizures. A channel extension strategy is also incorporated to expand the search space and identify suspicious epileptogenic zones beyond those selected by clinicians. For validation, SEEG data for five patients were analyzed showing promising results in terms of accuracy, consistency, and explainability.
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