Evaluation of EEG Foundation Models for Event-Based Burst-Suppression Detection in ICU

📄 arXiv: 2606.20074v1 📥 PDF

作者: Elisa Vasta, Thorir Mar Ingolfsson, Andrea Cossettini, Luca Benini, Tilman Beck, Emanuela Keller, Una Pale

分类: eess.SP, cs.AI, cs.LG

发布日期: 2026-06-18

备注: 4 pages, 1 figure. Code available upon publication

💡 一句话要点

评估EEG基础模型以解决ICU中事件驱动的突发抑制检测问题

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture) 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 脑电图 突发抑制 重症监护 自动检测 基础模型 事件驱动评估 深度学习

📋 核心要点

  1. 现有的自动突发检测方法面临挑战，主要由于突发抑制模式在不同患者之间存在显著差异，且标注数据集稀缺。
  2. 本文提出了评估EEG基础模型在无患者特定校准情况下进行突发检测的方案，结合事件驱动的突发检测评估。
  3. 实验结果表明，REVE-base模型在事件驱动的F1-score上表现最佳，显著提高了突发检测的准确性和效率。

📝 摘要（中文）

突发抑制（BS）是一种临床相关的脑电图（EEG）模式，用于监测重症患者的镇静深度和脑活动，尤其是在重症监护病房（ICU）中的诱导昏迷期间。自动突发检测仍然具有挑战性，因为BS模式在患者之间差异显著且标注数据集稀缺。本文首次评估EEG基础模型（FMs）在无患者特定校准的情况下进行突发检测的有效性。我们比较了REVE-base、LUNA-large和LuMamba-Tiny与自适应阈值基线及任务特定的EEGNet基线。结果显示，REVE-base模型在事件驱动的F1-score上达到了最高值（$0.868 ext{±} 0.167$），并且相较于EEGNet和自适应阈值分别减少了52.1%和36.2%的突发每分钟误差，支持FMs在ICU中进行可扩展的EEG监测。

🔬 方法详解

问题定义：本文旨在解决在重症监护病房中，自动检测突发抑制（BS）模式的挑战。现有方法由于患者间BS模式差异大和标注数据稀缺，导致检测准确性不足。

核心思路：论文提出利用EEG基础模型（FMs）进行突发检测，特别是在没有患者特定校准的情况下，探索其在减少标注数据需求下的有效性。

技术框架：研究采用了多种EEG基础模型（如REVE-base、LUNA-large和LuMamba-Tiny），并与自适应阈值和EEGNet基线进行比较。通过事件驱动的突发检测评估，增强了传统的窗口分类方法。

关键创新：最重要的创新在于首次将EEG基础模型应用于突发抑制检测，并通过事件驱动评估减少了标注变异性的影响。

关键设计：在实验中，采用了全微调作为适应策略，显示出相较于冷冻骨干训练、两步微调和LoRA适应的优势。REVE-base在25%标注数据下的预训练表现优于随机初始化，提升了事件驱动F1-score。

🖼️ 关键图片

fig_0

📊 实验亮点

实验结果显示，REVE-base模型在事件驱动的F1-score上达到了$0.868 ext{±} 0.167$，相较于EEGNet和自适应阈值分别减少了52.1%和36.2%的突发每分钟误差，展现了EEG基础模型在突发检测中的优越性。

🎯 应用场景

该研究的潜在应用领域包括重症监护病房的脑电监测，能够为临床医生提供更准确的镇静深度监测工具，改善患者管理和治疗效果。未来，该技术有望推广至其他需要实时脑电监测的医疗场景。

📄 摘要（原文）

Burst suppression (BS) is a clinically relevant electroencephalographic (EEG) pattern used to monitor sedation depth and brain activity in critically ill patients, particularly during induced coma in Intensive Care Units (ICUs). Automatic burst detection remains challenging because BS patterns vary substantially between patients and annotated datasets are scarce. Recently, EEG Foundation Models (FMs) have shown promise across several downstream EEG applications, but their usefulness for BS detection remains unexplored. We present the first study to evaluate EEG FMs for burst detection in reduced-montage ICU EEG without patient-specific calibration. We compare REVE-base, LUNA-large and LuMamba-Tiny with an adaptive thresholding baseline and a task-specific EEGNet baseline. Additionally, we complement conventional EEG window-based classification with event-based burst detection evaluation. This helps assessing clinically whether burst episodes are correctly detected, reducing the impact of expected annotation variability. The best model, REVE-base, achieved the highest event-based F1-score ($0.868 \pm 0.167$) and reduced burst-per-minute error by 52.1% and 36.2% compared to EEGNet and adaptive thresholding respectively, supporting FMs for scalable EEG monitoring in ICU. Ablation experiments showed that full fine-tuning was the most effective adaptation strategy with respect to frozen-backbone training, two-step fine-tuning, and LoRA-based adaptation, improving event-based F1-score over frozen-backbone training by up to $+0.102$ for LUNA-large. With reduced labeled datasets, pretrained REVE-base outperformed random initialization by $+0.723$ event-based F1 points at 25% of the cohort, demonstrating the benefit of pretraining FM representations when adapted to burst detection with limited labeled data.