论文标题
机器学习方法应用于皮层皮质诱发的潜力，有助于定位癫痫发作区域
Machine Learning Methods Applied to Cortico-Cortical Evoked Potentials Aid in Localizing Seizure Onset Zones
论文作者
论文摘要
癫痫会影响数百万人，降低生活质量并增加死亡的风险。三分之一的癫痫病例是耐药性的，需要进行手术进行治疗，这需要将癫痫发作区（SOZ）定位在大脑中。已经尝试使用皮质皮质诱发的电位（CCEP）来改善SOZ定位，但没有一个成功用于临床采用。在这里，我们比较了从CCEP数据定位SOZ的十个机器学习分类器的性能。这项初步研究验证了机器学习的新应用，结果将我们的方法确立为有希望的研究线，需要进一步研究。这项工作还有助于与机器学习和/或癫痫研究人员进行讨论和协作。
Epilepsy affects millions of people, reducing quality of life and increasing risk of premature death. One-third of epilepsy cases are drug-resistant and require surgery for treatment, which necessitates localizing the seizure onset zone (SOZ) in the brain. Attempts have been made to use cortico-cortical evoked potentials (CCEPs) to improve SOZ localization but none have been successful enough for clinical adoption. Here, we compare the performance of ten machine learning classifiers in localizing SOZ from CCEP data. This preliminary study validates a novel application of machine learning, and the results establish our approach as a promising line of research that warrants further investigation. This work also serves to facilitate discussion and collaboration with fellow machine learning and/or epilepsy researchers.
