CLSP-REQA: a real-time quality-aware closed-loop seizure prediction framework with Mamba-BiLSTM and confidence-gated intervention.

Introduction: 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 generalization performance. Methods: 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 Results: On CHB-MIT, CLSP-REQA achieved an AUC-ROC of 0.7426 ± 0.0199, exceeding 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 SIENA, CLSP-REQA achieved an AUC of 0.7012 ± 0.0249, exceeding the best domain-adapted cross-patient result of 0.61 on the same dataset under a strictly harder information constraint, since domain adaptation methods are given access to unlabelled target-patient data that CLSP-REQA does not use. Discussion: The framework outputs a structured four-tuple 〈