As a common disease in nervous system, epilepsy is possessed of characteristics of high incidence, suddenness and recurrent seizures. Timely prediction with corresponding rescues and treatments can be regarded as effective countermeasure to epilepsy emergencies, while most accidental injuries can thus be avoided. Currently, how to use electroencephalogram (EEG) signals to predict seizure is becoming a highlight topic in epilepsy researches. In spite of significant progress that made, more efforts are still to be made before clinical applications. This paper reviews past epilepsy studies, including research records and critical technologies. Contributions of machine learning (ML) and deep learning (DL) on seizure predictions have been emphasized. Since feature selection and model generalization limit prediction ratings of conventional ML measures, DL based seizure predictions predominate future epilepsy studies. Consequently, more exploration may be vitally important for promoting clinical applications of epileptic seizure prediction.
癫痫作为一种神经系统常见疾病，具有发病率高、突发性和反复性的特点。及时预测癫痫发作并进行干预治疗，可以显著减少患者的意外伤害。当前，基于脑电信号的癫痫发作预测正成为癫痫研究的热点，虽然相关研究取得很多进展，但距临床应用仍有一定距离。本文就该领域的研究进行综述，阐述了其发展历程及关键技术，着重介绍和分析基于机器学习和深度学习进行癫痫发作预测的研究进展。传统机器学习方法面临特征选取和浅层模型泛化能力弱等制约，采用深度学习进行癫痫预测逐渐成为当前发展趋势，需要开展更加深入的探索，以促进癫痫发作预测技术的临床应用。.
Keywords: deep learning; electroencephalogram signals; epilepsy; machine learning; seizure prediction.