CLSP-REQA: A Real-Time Quality-Aware Closed-Loop Seizure Prediction Framework with Mamba-BiLSTM and Confidence-Gated Intervention

Researchers introduce CLSP-REQA, a machine learning framework for seizure prediction that integrates real-time EEG quality assessment with a Mamba-BiLSTM neural network. The system achieves superior cross-patient and cross-dataset generalization on medical benchmarks while requiring fewer EEG channels than prior approaches, with direct compatibility for closed-loop neurostimulation devices.

CLSP-REQA addresses a critical gap in medical AI deployment where real-world EEG signal quality varies significantly yet existing seizure prediction models rarely account for this variability. The framework's innovation lies in embedding a lightweight quality assessment module directly within the prediction pipeline, producing a scalar quality score that modulates confidence through a non-linear fusion function. This architectural choice reflects a maturation in clinical AI thinking—moving beyond accuracy metrics toward robustness and reliability in deployment scenarios.

The research demonstrates strong empirical results on two established EEG databases using rigorous cross-patient evaluation protocols. On CHB-MIT data, the framework achieves 0.7426 AUC-ROC while using only 16 channels compared to 23 in prior work, suggesting improved efficiency without sacrificing performance. The cross-dataset validation on SIENA data (0.7012 AUC) substantially outperforms previous domain-adapted baselines (0.61), indicating genuine generalization rather than overfitting to specific populations.

For the neurotechnology sector, this work bridges the persistent gap between research performance and clinical viability. Seizure prediction systems face skepticism in medical practice partly due to deployment robustness concerns and unclear confidence calibration. CLSP-REQA's structured output format—directly compatible with neurostimulator interfaces—suggests pathway toward regulatory approval and clinical adoption. The explicit quality assessment component addresses FDA concerns about model reliability across diverse patient populations.

Investors tracking AI applications in medtech should monitor whether this methodology influences future neurostimulation device design and whether clinical trials emerge validating closed-loop interventions with quality-aware prediction systems.

• →CLSP-REQA integrates real-time EEG quality assessment into seizure prediction, improving cross-patient generalization to 0.7426 AUC-ROC on CHB-MIT dataset.
• →Framework outperforms domain-adapted baselines on cross-dataset validation, achieving 0.7012 AUC on SIENA versus 0.61 previously reported.
• →System requires only 16 EEG channels versus 23 in prior work, reducing hardware complexity while maintaining predictive performance.
• →Structured output format directly compatible with closed-loop neurostimulator interfaces accelerates potential clinical deployment pathway.
• →Quality-aware architecture addresses key deployment concerns about model reliability across diverse patient populations and signal conditions.