REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates

• URL: http://arxiv.org/abs/2406.16906v1
• Date: Mon, 3 Jun 2024 16:30:19 GMT
• Title: REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates
• Authors: Arshia Afzal, Grigorios Chrysos, Volkan Cevher, Mahsa Shoaran,
• Abstract summary: This paper introduces a novel graph-based residual state update mechanism (REST) for real-time EEG signal analysis. 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.
• Score: 54.96885726053036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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