This review highlights that while state-of-the-art EEG-based seizure prediction models achieve high performance, their clinical application requires careful optimization of post-processing techniques.
This review clarifies conceptual and methodological issues to improve the performance and generalization of EEG-based seizure prediction models for future wearable devices.
The recurrent and unpredictable nature of seizures can lead to unintentional injuries and even death. The rapid development of electroencephalogram (EEG) and Artificial Intelligence (AI) technologies has made it possible to predict seizures in real-time through brain-machine interfaces (BCI), allowing advanced intervention. To date, there is still much room for improvement in predictive seizure models constructed by EEG using machine learning (ML) and deep learning (DL). But, the most critical issue is how to improve the performance and generalization of the model, which involves some confusing conceptual and methodological issues. This review focuses on analyzing several factors affecting the performance of seizure prediction models, focusing on the aspects of post-processing, seizure occurrence period (SOP), seizure prediction horizon (SPH), and algorithms. Furthermore, this study presents some new directions and suggestions for building high-performance prediction models in the future. We aimed to clarify the concept for future research in related fields and improve the performance of prediction models to provide a theoretical basis for future applications of wearable seizure detection devices.
Ren et al. (Thu,) conducted a review in Epilepsy. EEG-based seizure prediction models was evaluated. This review highlights that while state-of-the-art EEG-based seizure prediction models achieve high performance, their clinical application requires careful optimization of post-processing techniques.
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