脑电信号在癫痫检测中的应用及其研究进展

The applications and research progress of electroencephalographic (EEG) signals in epilepsy detection

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DOI 10.12208/j.aics.20250050
刊名
Advances in International Computer Science
年,卷(期) 2025, 5(3)
作者
作者单位

江西理工大学软件工程学院 江西南昌

摘要
癫痫是一种常见的慢性神经系统病症,其核心特点是大脑神经元异常过度放电所导致的突发性、反复性发作,严重危害患者的生命安全和生活质量。脑电图(EEG)作为生理检测里最常用的一种手段,凭借其非侵入性、低成本等长处,成为癫痫诊断、分型、病灶定位以及治疗评估的重要工具。本文旨在系统整理基于EEG信号的癫痫分析检测应用及其研究进展,探究从传统机器学习方法到现代深度学习架构的发展,核心特征提取与分类技术,数据采集和专用数据集的运用,最后结合现存的挑战,展望未来的研究方向。
Abstract
Epilepsy is a common chronic neurological disorder whose core characteristic is sudden and recurrent seizures caused by abnormal and excessive discharge of brain neurons, seriously endangering patients' life safety and quality of life. Electroencephalography (EEG), as one of the most commonly used methods in physiological detection, has become an important tool for epilepsy diagnosis, classification, lesion localization, and treatment evaluation with its advantages of non-invasiveness and low cost. This paper aims to systematically sort out the applications and research progress of epilepsy analysis and detection based on EEG signals, explore the development from traditional machine learning methods to modern deep learning architectures, core feature extraction and classification technologies, and the application of data collection and specialized datasets. Finally, combined with existing challenges, future research directions are prospected.
关键词
癫痫;脑电信号;特征提取;机器学习
KeyWord
Epilepsy; EEG; Feature extraction; Machine learning
基金项目
页码 5-13
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黄宇凯, 刘述民. 脑电信号在癫痫检测中的应用及其研究进展 [J]. 国际计算机科学进展. 2025; 5; (3). 5 - 13.

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