CLSP-REQA: A Real-Time Quality-Aware Closed-Loop Seizure Prediction Framework with Mamba-BiLSTM and Confidence-Gated Intervention
arXiv:2606.00074v1 Announce Type: cross Abstract: 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 Ass
The continuous advancements in AI and machine learning, coupled with the increasing demand for reliable real-time medical interventions, are driving innovations in closed-loop neurostimulation.
This development enhances the reliability and effectiveness of seizure prediction systems, moving closer to practical and safer personalized medical devices, potentially improving quality of life for epilepsy patients.
The integration of real-time EEG quality assessment directly into seizure prediction pipelines addresses a critical real-world limitation, significantly improving the accuracy and trustworthiness of such systems.
- · Epilepsy patients
- · Medical device manufacturers
- · Neuroscience researchers
- · AI healthcare providers
- · Developers of non-strict evaluation protocols
- · Traditional EEG monitoring solutions
Improved seizure prediction leads to more effective and less disruptive neurostimulation therapies.
Increased adoption of closed-loop neurostimulation technologies in clinical settings and patient homes.
Enhanced trust in AI-driven medical devices paves the way for further integration of AI into critical healthcare functions.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG