Application Progress of Deep Learning in Epilepsy Detection

doi:10.3778/j.issn.1002-8331.2207-0062

Abstract

Abstract: With the increasing number of epileptic patients year by year, it is of great practical significance to detect epileptic diseases timely and accurately. Nowadays, deep learning develops rapidly and is widely used in the medical field. Epilepsy detection task based on deep learning has also become a research hotspot. After combing the relevant literature in recent years, this paper systematically summarizes the application of deep learning algorithm in epilepsy detection. Firstly, the pathogenesis, etiology and treatment of epilepsy are introduced. Secondly, the EEG used in epilepsy detection and the division of the overall process of epilepsy are explained. Then, the differences between traditional machine learning and deep learning in this field are simply compared. This paper focuses on the research progress of detecting EEG signals in various stages of epilepsy by deep learning, including two-stage, three-stage and multi-stage EEG detection, and compares the detection algorithms in various stages of epilepsy. Finally, this paper summarizes and prospects the research status and future development direction in this field.

Key words: epilepsy, electroencephalogram signals, automated detection, deep learning

摘要： 随着癫痫患者数量的逐年增加，及时准确地检测出癫痫疾病具有重要的现实意义。如今深度学习发展迅速，被广泛用于医疗领域，基于深度学习的癫痫检测任务也成为目前的研究热点。通过梳理近几年的相关文献后，对深度学习在癫痫检测中的算法应用进行了系统概述。介绍了癫痫的发病原理、病因和治疗方法等；讲解了癫痫检测时所使用的脑电图和癫痫发作的整体过程划分；简单对比了传统机器学习和深度学习在此领域应用的不同之处；重点综述了利用深度学习检测癫痫各阶段脑电信号的研究进展，包括癫痫双阶段、三阶段和多阶段的脑电检测，并对癫痫各阶段的检测算法进行了比较；最后对该领域的研究现状和未来发展方向进行了总结和展望。

关键词: 癫痫, 脑电信号, 自动检测, 深度学习

ZHANG Hanming, MA Jingang, ZHANG Ningning, ZHAO Zhenzhen, LI Ming. Application Progress of Deep Learning in Epilepsy Detection[J]. Computer Engineering and Applications, 2023, 59(10): 35-47.

张汉明, 马金刚, 张宁宁, 赵珍珍, 李明. 深度学习在癫痫检测中的应用进展[J]. 计算机工程与应用, 2023, 59(10): 35-47.

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