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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 12·5 min read
CLSP-REQA: A Real-Time Quality-Aware Closed-Loop Seizure Prediction Framework with Mamba-BiLSTM and Confidence-Gated Intervention
TL;DR
CLSP-REQA is a novel framework for real-time seizure prediction that integrates a lightweight EEG signal quality estimator with a Mamba-BiLSTM model. It achieves significant performance improvements in seizure prediction accuracy across multiple datasets, demonstrating strong generalization capabilities.
✦ Why It Matters
Consider integrating CLSP-REQA into your seizure prediction systems to enhance accuracy and reliability.
Key Takeaways
- Implement CLSP-REQA for improved seizure prediction accuracy.
- Utilize the integrated quality assessment for better signal handling.
- Leverage the structured output for seamless neurostimulator integration.
- Explore cross-dataset generalization capabilities in your applications.
How It Works
CLSP-REQA combines a Mamba-BiLSTM model with a Real-time EEG Quality Assessment module. The REQA module evaluates the quality of EEG signals in real-time, producing a scalar score that adjusts the confidence of the seizure predictions.
This integration allows the framework to adaptively respond to varying signal conditions, enhancing prediction reliability.