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基于EEG的发作预测方法的研究进展

Zhongpeng Wang^1,2, Xiaoxin Song¹, Long Chen^1,2

¹Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072 China.

Cognitive neurodynamics

|November 18, 2024

概括

使用脑电图 (EEG) 预测发作对于耐火性患者至关重要. 本综述探讨了基于EEG的方法,它们的局限性,以及深度学习的潜力,以改善预测和患者的生活质量.

科学领域:

神经学神经学
生物医学工程生物医学工程
人工智能的人工智能

背景情况:

耐火性影响全球超过30%的患者,不可预测的发作影响生活质量和安全.
早期发作预测和干预对于改善患者的治疗结果和福祉至关重要.

研究的目的:

审查和评估基于电脑电图 (EEG) 的预测方法.
用头皮和内EEG分析预测研究的现状.
确定阻碍临床应用的局限性,并建议未来的研究方向,包括深度学习.

主要方法:

介绍基于EEG的发作预测系统设计.
概述常见的预加工,特征提取,分类和后加工技术.
对头皮和内EEG特征分析方法的审查和全面评估.

主要成果:

基于五种特征分析方法,分析当前发作预测研究状况.
评价头皮EEG与内EEG用于发作预测.
确定当前算法缺乏临床适用性的原因.

结论:

目前的预测算法面临的局限性阻止了临床翻译.
深度学习为预测发作提供了与传统机器学习相比有前途的进展.
需要进一步的研究来克服局限性,并在临床预测方面取得突破.

关键词:

功能提取功能提取内EEG (iEEG) 是一种脑内电图.头皮 EEG 的使用.预测发作的预测

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