机器学习在癫痫方面的应用进展  

The progress of the application of machine learning in epilepsy

作　　者：董志远 阳勇 于跃 王雁[1] Dong Zhiyuan;Yang Yong;Yu Yue;Wang Yan(Department of Neurology,Affiliated Hospital of Qingdao University,Qingdao 266000,China;Department of Rehabilitation,Qingdao Municipal Hospital,Qingdao 266011,China)

机构地区：[1]青岛大学附属医院神经内科,青岛266000 [2]青岛市市立医院康复科,青岛266011

出　　处：《中华神经科杂志》2024年第2期185-191,共7页Chinese Journal of Neurology

基　　金：国家自然科学基金(81901323)。

摘　　要：癫痫是由脑部神经元高度同步化异常放电引起的发作性、短暂性、刻板性的脑功能失调,发作时机难以预测,目前主要治疗方式为抗癫痫药物与手术治疗。其诊断和治疗需要大量的辅助手段与临床经验。在癫痫的发作预测、药物治疗预后、手术治疗评估等多个临床方面,机器学习可以通过对数据的深层次挖掘、纳入多个临床和影像因素、建立对应的学习模型,提高癫痫的诊断效率与准确性,实现抗癫痫药物的个体化应用,改善癫痫患者的术前评估与预后情况。Epilepsy is an episodic,transient,stereotypic brain dysfunction caused by highly synchronized abnormal neuronal discharges in the brain,with unpredictable timing of seizures,for which the main treatment modalities are antiepileptic drugs and surgery.Its diagnosis and treatment require a large number of aids and clinical experience.For multiple clinical aspects of epilepsy,such as seizure prediction,drug therapy prognosis,and surgical treatment evaluation,machine learning can incorporate multiple clinical and imaging factors through deep mining of data,establish corresponding learning models,improve the efficiency and accuracy of epilepsy diagnosis,realize individualized application of antiepileptic drugs,and improve the preoperative evaluation and prognosis of epilepsy patients.

关 键 词：癫痫 脑电图描记术 机器学习 抗发作药物 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] R742.1[自动化与计算机技术—控制科学与工程]