通过深度学习使用非线性特征衍生的EEG地形图来诊断意识障碍
Sheng Qu1, Xinchun Wu1, Laigang Huang1
1Department of Rehabilitation, The Second Qilu Hospital of Shandong University, No. 247, Beiyuan Avenue, Jinan, 250033, Shandong, China.
Scientific reports
|February 5, 2026
概括
非线性EEG特征,特别是近似 (ApEn),可以准确地检测意识障碍 (DOC) 患者的意识. 使用ApEn的深度学习模型显示出高的诊断性能,有助于在植物状态 (VS) /不响应的清醒综合征 (UWS) 和最小意识状态 (MCS) 中进行评估.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 医疗信息学 医疗信息学
背景情况:
- 在沟通有限的患者中评估意识障碍 (DOC) 是具有挑战性的.
- 当前的方法可能无法完全捕捉到意识的微妙迹象.
- 来自脑电图 (EEG) 信号的非线性特征为改进评估提供了潜力.
研究的目的:
- 评估非线性EEG特征的有用性,特别是近似 (ApEn),用于评估DOC患者.
- 将深度学习模型 (CNN) 与传统机器学习模型 (SVM,GRNN) 的诊断性能进行比较.
- 在休息和音乐刺激条件下调查ApEn与临床分数的相关性.
主要方法:
- 收集了来自104名参与者的EEG数据 (56名VS/UWS,48名MCS).
- 在休息状态和首选音乐刺激期间使用滑动窗口方法计算通道智能近似 (ApEn).
- 使用卷积神经网络 (CNN) 进行分类,将性能与支持向量机 (SVM) 和通用回归神经网络 (GRNN) 进行比较.
主要成果:
- ApEn与昏迷恢复量表修订分数相关,在不同条件下显示区域差异.
- 在区分患者状态时,CNN模型实现了90.00%的准确性和0.902的AUC.
- 在区分植物状态/不反应的清醒综合征 (VS/UWS) 和最小意识状态 (MCS) 方面,CNN的表现超过了SVM和GRNN.
结论:
- 非线性EEG特征 (ApEn) 与深度学习 (CNN) 结合,提供了一种准确且方便的方法来检测DOC患者的意识.
- 这种方法促进了对VS/UWS和MCS状态患者的评估.
- 这些发现支持使用静止状态和音乐刺激的EEG与深度学习进行客观意识评估.
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