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arXiv 2506.09255eess.SPcs.LG
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基于AI的SEEG通道排名用于癫痫灶定位

AI-Driven SEEG Channel Ranking for Epileptogenic Zone Localization

Department of Electrical and Computer Engineering, University of Texas at Dallas（德克萨斯大学达拉斯分校电气与计算机工程系）；Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center（德克萨斯西南医学中心神经病学与神经治疗学系）

Saeed Hashemi, Genchang Peng, Mehrdad Nourani, Omar Nofal, Jay Harvey

备注：
Accepted to be presented at the 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2025). This version is submitted to arXiv prior to final IEEE formatting and publication
发表信息：
Proceedings of the 2025 IEEE Engineering in Medicine and Biology Conference (EMBC)
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AI总结：

本文提出利用机器学习对SEEG通道进行排名，结合临床选择和计算发现，通过XGBoost模型学习ictal期通道特征，并利用SHAP评分对通道贡献进行排序，以提高癫痫灶定位的准确性与一致性。

摘要：

立体电极脑电图（SEEG）是一种侵入性技术，用于植入深度电极并收集数据以进行术前评估。对数百个通道记录的信号进行视觉检查是耗时且低效的。我们提出了一种机器学习方法，通过结合临床医生的选择和计算发现来对重要通道进行排序。使用XGBoost训练的分类模型用于学习ictal期每个通道的判别特征。然后利用SHapley Additive exPlanations（SHAP）评分对SEEG通道进行排名，基于其对癫痫发作的贡献。还采用通道扩展策略来扩展搜索空间，并识别出超出临床医生选择的可疑癫痫灶区域。为了验证，对五名患者的SEEG数据进行了分析，结果显示在准确性、一致性和可解释性方面具有良好的表现。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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