Epilepsy is one of the most common neurological disorders typically characterized by recurrent and uncontrollable seizures, which seriously affects the quality of life of epilepsy patients. The effective tool utilized in the clinical diagnosis of epilepsy is the Electroencephalogram (EEG). The emergence of machine learning promotes the development of automated epilepsy detection techniques. New algorithms are continuously introduced to shorten the detection time and improve classification accuracy. This minireview summarized the latest research of epilepsy detection techniques that focused on acquiring, preprocessing, feature extraction, and classification of epileptic EEG signals. The application of seizure prediction and localization based on EEG signals in the diagnosis of epilepsy was also introduced. And then, the future development trend of epilepsy detection technology has prospected at the end of the article.

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基于脑电图信号的癫痫检测技术综述

癫痫病是最常见的神经系统疾病之一，通常表现为反复发作和无法控制的癫痫发作，严重影响癫痫患者的生活质量。在癫痫的临床诊断中使用的有效工具是脑电图（EEG）。机器学习的出现促进了自动癫痫检测技术的发展。不断引入新算法以缩短检测时间并提高分类准确性。这份小型回顾总结了癫痫检测技术的最新研究，这些技术专注于癫痫EEG信号的获取，预处理，特征提取和分类。介绍了基于脑电信号的癫痫发作预测和定位在癫痫诊断中的应用。然后，