实时评估脑电质量，提升癫痫预测在真实场景下的可靠性。
CLSP-REQA: A Real-Time Quality-Aware Closed-Loop Seizure Prediction Framework with Mamba-BiLSTM and Confidence-Gated Intervention
- 将轻量级信号质量评估模块嵌入预测流程，动态调节输出置信度。
- 跨患者测试下AUC达0.7426，优于此前方法且仅用16通道数据。
- 输出结构化结果，可直接对接闭环神经刺激器，适合临床部署。
可靠的癫痫预测是闭环神经刺激治疗的前提，但现有方法很少考虑真实场景中脑电（EEG）信号质量的波动，且多数采用宽松的评估协议，高估了泛化性能。本文提出CLSP-REQA框架，将实时脑电质量评估（REQA）模块嵌入预测主干网络，与Mamba-BiLSTM并行运行，输出[0,1]范围的质量评分q，通过分层非线性融合函数（ECLO）调控预测置信度。在CHB-MIT头皮脑电数据库（n=23名受试者，198次发作）上，跨患者严格评估下达到AUC-ROC 0.7426±0.0199，优于Jemal等人报告的未适配跨患者基线0.69，且仅使用16个脑电通道（前人用23通道），无需目标患者数据或领域自适应。在SIENA数据库（n=14名受试者，47次发作）上，AUC达0.7012±0.0249，显著超越该数据集上最佳领域自适应结果0.61，体现强跨数据集泛化能力。框架输出结构化四元组（p, q, c, Phi_SHAP），可直接接入闭环神经刺激器接口。
原文摘要 · Abstract (English)
Reliable seizure prediction is a prerequisite for closed-loop neurostimulation therapy, yet existing methods rarely account for the variability in EEG signal quality encountered in real-world deployment, and the overwhelming majority adopt non-strict evaluation protocols that overestimate generalisation performance. We propose CLSP-REQA (Closed-Loop Seizure Prediction with Real-time EEG Quality Assessment), a unified framework that embeds a lightweight signal quality estimator directly within the prediction pipeline. A Real-time EEG Quality Assessment (REQA) module runs in parallel with a Mamba-BiLSTM backbone, producing a scalar quality score q in [0,1] that modulates output confidence through a tiered non-linear fusion function (ECLO). Under strict cross-patient evaluation on the CHB-MIT Scalp EEG Database (n = 23 subjects, 198 seizures), CLSP-REQA achieves an AUC-ROC of 0.7426 +- 0.0199, outperforming the unadapted cross-patient baseline of 0.69 reported by Jemal et al., using only 16 EEG channels compared to 23 in prior work, and without requiring any target-patient data or domain adaptation. On the SIENA Scalp EEG Database (n = 14 subjects, 47 seizures), CLSP-REQA achieves AUC 0.7012 +- 0.0249, substantially surpassing the best domain-adapted cross-patient result of 0.61 on the same dataset, demonstrating strong cross-dataset generalisation. The framework outputs a structured four-tuple (p, q, c, Phi_SHAP) directly compatible with closed-loop neurostimulator interfaces.
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