用AI自动分析癫痫视频脑电图，提升诊断效率
Automated Video-EEG Analysis in Epilepsy Studies: Advances and Challenges
- 提出基于概念学习的多模态分析新流程
- 实现从视频-脑电数据中量化治疗效果
- 适合神经科学与医疗AI研究者参考
癫痫通常通过脑电图（EEG）和长期视频-脑电图（vEEG）监测诊断。人工分析vEEG耗时费力，亟需自动化工具实现发作检测。近年来机器学习在利用EEG和视频数据进行实时发作检测与预测方面取得进展。然而，发作表现多样性、标注模糊性及多模态数据集稀缺限制了进一步发展。本文综述了自动化vEEG分析的最新进展，探讨了多模态数据融合方法，并提出一种基于概念学习的新流程，用于从vEEG数据中估计治疗效果，为该领域未来研究提供新路径。
原文摘要 · Abstract (English)
Epilepsy is typically diagnosed through electroencephalography (EEG) and long-term video-EEG (vEEG) monitoring. The manual analysis of vEEG recordings is time-consuming, necessitating automated tools for seizure detection. Recent advancements in machine learning have shown promise in real-time seizure detection and prediction using EEG and video data. However, diversity of seizure symptoms, markup ambiguities, and limited availability of multimodal datasets hinder progress. This paper reviews the latest developments in automated video-EEG analysis and discusses the integration of multimodal data. We also propose a novel pipeline for treatment effect estimation from vEEG data using concept-based learning, offering a pathway for future research in this domain.
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