用AI实时优化脑电图诊断中风，准确率超98%。
Adaptive EEG-based stroke diagnosis with a GRU-TCN classifier and deep Q-learning thresholding
- 结合GRU-TCN与深度强化学习，动态调整诊断阈值。
- 中风类型识别准确率达98.0%，严重程度和定位精度超96%。
- 适合急诊场景，支持可视化解释，临床可信赖。
急性中风的快速分诊亟需准确、床旁可用的工具；脑电图（EEG）虽具潜力却尚未在首诊中广泛应用。本文提出一种自适应多任务EEG分类器，将32导联信号转换为功率谱密度特征（Welch法），利用循环卷积网络（GRU-TCN）预测中风类型（健康、缺血性、出血性）、半球定位及严重程度，并采用深度Q网络（DQN）实现决策阈值的实时自适应调整。基于乌尔弗汉普顿医院（UCLH）中风EEG数据集的患者级划分（共44例，约26例急性中风、10例对照），主要评估指标为中风类型分类性能，次要指标为严重程度与半球定位。基准GRU-TCN模型在中风类型上达到89.3%准确率（F1 92.8%），严重程度约96.9%（F1 95.9%），半球定位约96.7%（F1 97.4%）。引入DQN阈值自适应后，中风类型准确率提升至约98.0%（F1 97.7%）。此外，在独立的低密度EEG队列（ZJU4H）上验证了模型鲁棒性，并报告配对患者级统计结果。分析遵循STARD 2015指南，索引测试为GRU-TCN+DQN，参考标准为影像学/临床诊断，采用患者级评估。自适应阈值使工作点向临床偏好敏感度-特异度权衡迁移，同时集成头皮图与频谱可视化增强可解释性。
原文摘要 · Abstract (English)
Rapid triage of suspected stroke needs accurate, bedside-deployable tools; EEG is promising but underused at first contact. We present an adaptive multitask EEG classifier that converts 32-channel signals to power spectral density features (Welch), uses a recurrent-convolutional network (GRU-TCN) to predict stroke type (healthy, ischemic, hemorrhagic), hemispheric lateralization, and severity, and applies a deep Q-network (DQN) to tune decision thresholds in real time. Using a patient-wise split of the UCLH Stroke EIT/EEG data set (44 recordings; about 26 acute stroke, 10 controls), the primary outcome was stroke-type performance; secondary outcomes were severity and lateralization. The baseline GRU-TCN reached 89.3% accuracy (F1 92.8%) for stroke type, about 96.9% (F1 95.9%) for severity, and about 96.7% (F1 97.4%) for lateralization. With DQN threshold adaptation, stroke-type accuracy increased to about 98.0% (F1 97.7%). We also tested robustness on an independent, low-density EEG cohort (ZJU4H) and report paired patient-level statistics. Analyses follow STARD 2015 guidance for diagnostic accuracy studies (index test: GRU-TCN+DQN; reference standard: radiology/clinical diagnosis; patient-wise evaluation). Adaptive thresholding shifts the operating point to clinically preferred sensitivity-specificity trade-offs, while integrated scalp-map and spectral visualizations support interpretability.
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