REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates
<|reference_start|>REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates: EEG-based seizure detection models face challenges in terms of inference speed and memory efficiency, limiting their real-time implementation in clinical devices. This paper introduces a novel graph-based residual state update mechanism (REST) for real-time EEG signal analysis in applications such as epileptic seizure detection. By leveraging a combination of graph neural networks and recurrent structures, REST efficiently captures both non-Euclidean geometry and temporal dependencies within EEG data. Our model demonstrates high accuracy in both seizure detection and classification tasks. Notably, REST achieves a remarkable 9-fold acceleration in inference speed compared to state-of-the-art models, while simultaneously demanding substantially less memory than the smallest model employed for this task. These attributes position REST as a promising candidate for real-time implementation in clinical devices, such as Responsive Neurostimulation or seizure alert systems.<|reference_end|>
@article{afzal2024rest:,
    title={REST: Efficient and Accelerated EEG Seizure Analysis through Residual
  State Updates},
    author={Arshia Afzal, Grigorios Chrysos, Volkan Cevher, Mahsa Shoaran},
    journal={arXiv preprint arXiv:2406.16906},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16906},
    primaryClass={eess.SP cs.AI cs.LG}
}