多专家共识在立体EEG中对自发性和刺激诱导的发作的注释
medRxiv : the preprint server for health sciences
|February 6, 2026
概括
这项研究引入了一套用于自动发作检测的新数据集,其中包括多专家对自发发作和刺激诱导的发作的注释. 这些数据可以对专家的性能进行强有力的查获本地化算法的验证.
科学领域:
- 神经科学是一个神经科学.
- 医疗信息学 医疗信息学
- 发病学 (Epileptology) 是一个专业的学科.
背景情况:
- 自动抓获检测和定位依赖于经验证的基准数据集.
- 现有的数据集往往缺乏多专家的共识,并排除了刺激诱导的发作,限制了算法验证.
研究的目的:
- 呈现一个新的数据集的立体EEG记录与多专家注释自发和刺激诱导的发作.
- 为了使自动发作检测和本地化算法的验证与人类专家的性能相比.
- 为了支持对扣押网络的比较分析.
主要方法:
- 收集了32名耐药患者83次发作 (46次自发发作,37次刺激发作) 的立体EEG记录.
- 三位病学家独立地使用标准化协议注释了发作发作,发作道和传播道.
- 通过多数投票建立了共识注释;数据遵循脑成像数据结构 (BIDS) 标准.
主要成果:
- 取得了实质性的评价者间一致性 (κ = 0.64对于发病道, κ = 0.62对于传播道).
- 表明高个体评价者同意与共识 (κ = 0.81开始, κ = 0.80扩散).
- 在自发性和刺激诱导的发作之间,协议指标是可比的.
结论:
- 这一数据集为开发和验证发作检测和定位算法提供了宝贵的资源.
- 数据集使算法性能与专家注释的直接比较更加容易.
- 它支持对自发和刺激诱导事件之间的网络差异的研究.
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