识别和验证可解释的基于EEG的机器学习模型,用于诊断中风后认知障碍
Xinyang Wang1,2, Jian Song1, Weicheng Kong1
1The Affiliated Rehabilitation Hospital, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
Frontiers in aging neuroscience
|January 28, 2026
概括
这项研究开发了一种可解释的机器学习模型,使用脑电图 (EEG) 早期检测中风后认知障碍 (PSCI). 基于EEG的模型准确地识别PSCI,有助于个性化的中风护理.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 脑卒中后认知障碍 (PSCI) 是中风的常见和衰弱的结果.
- 目前用于早期PSCI识别的方法缺乏客观工具,延迟及时干预.
- 开发可靠的生物标志物用于早期PSCI检测对于有效的患者管理至关重要.
研究的目的:
- 开发和验证可解释的机器学习 (ML) 模型,使用脑电图 (EEG) 早期检测PSCI.
- 为了确定脑电图的关键特征,预测中风后认知障碍.
- 创建一个可访问的工具来预测PSCI风险.
主要方法:
- 获得了174名参与者的静止状态EEG数据 (脑卒中患者有/没有认知障碍,健康对照).
- 提取了多维EEG特征,包括功率光谱比率和微态参数.
- 利用LASSO回归,随机森林和Boruta进行特征选择,并评估了五个ML模型.
- 采用SHAP来测试模型的可解释性,并在外部队列中验证最佳模型.
主要成果:
- 确定了七个关键的EEG特征,包括三角形加三角形到α+β比率 (DTABR) 和微态参数 (A-MMD,B-MMD,D-MFO,A-MC),用于预测PSCI.
- 一个随机森林模型实现了高性能 (AUC=0.91,精度=0.83) 并得到了外部验证 (AUC=0.97,精度=0.90).
- 为个性化PSCI风险预测开发了一个交互式Web应用程序.
结论:
- 一个可解释的基于EEG的ML模型为PSCI提供了准确的早期查.
- 这种方法可以提高临床工作流程,支持个性化康复和中风后护理.
- 建议在更大的多中心研究中进一步验证.
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