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

arXiv: https://arxiv.org/abs/2406.16906 · HTML: https://mlsanity.com/paper/2406.16906/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 47 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Deep Double Descent: Where Bigger Models and More Data Hurt](https://mlsanity.com/paper/1912.02292/) (2019)
- [The Temple University Hospital Seizure Detection Corpus](https://mlsanity.com/paper/1801.08085/) (2018)
- [Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis](https://mlsanity.com/paper/2104.08336/) (2021)
- [Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting](https://mlsanity.com/paper/1707.01926/) (2017)
- [Temporal Graph Convolutional Networks for Automatic Seizure Detection](https://mlsanity.com/paper/1905.01375/) (2019)
- [Neural Memory Networks for Seizure Type Classification](https://mlsanity.com/paper/1912.04968/) (2019)
- [DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata](https://mlsanity.com/paper/2211.11417/) (2022)
- [Learning Robust Features using Deep Learning for Automatic Seizure Detection](https://mlsanity.com/paper/1608.00220/) (2016)
- [ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification](https://mlsanity.com/paper/2006.07900/) (2020)
- [Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture](https://mlsanity.com/paper/2105.13854/) (2021)
- [Convolutional neural network for detection and classification of seizures in clinical data](https://mlsanity.com/paper/1903.08864/) (2019)
- [Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting](https://mlsanity.com/paper/2201.08780/) (2022)
- [NeuralTree: A 256-Channel 0.227-μJ/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC](https://mlsanity.com/paper/2205.06090/) (2022)
- [Closed-Loop Neural Prostheses with On-Chip Intelligence: A Review and A Low-Latency Machine Learning Model for Brain State Detection](https://mlsanity.com/paper/2109.05848/) (2021)

## Built on by (most cited first, 12 of 12)

- [TimeLDM: Latent Diffusion Model for Unconditional Time Series Generation](https://mlsanity.com/paper/2407.04211/) (2024)
- [BioMamba: Leveraging Spectro-Temporal Embedding in Bidirectional Mamba for Enhanced Biosignal Classification](https://mlsanity.com/paper/2503.11741/) (2025)
- [MT-NAM: An Efficient and Adaptive Model for Epileptic Seizure Detection](https://mlsanity.com/paper/2503.08251/) (2025)
- [Machine-Learning-Powered Neural Interfaces for Smart Prosthetics and Diagnostics](https://mlsanity.com/paper/2505.02516/) (2025)
- [Personalization on a Budget: Minimally-Labeled Continual Learning for Resource-Efficient Seizure Detection](https://mlsanity.com/paper/2509.13974/) (2025)
- [Neurocircuitry-Inspired Hierarchical Graph Causal Attention Networks for Explainable Depression Identification](https://mlsanity.com/paper/2511.17622/) (2025)
- [The Powers of Precision: Structure-Informed Detection in Complex Systems -- From Customer Churn to Seizure Onset](https://mlsanity.com/paper/2601.21170/) (2026)
- [NeuroCanvas: VLLM-Powered Robust Seizure Detection by Reformulating Multichannel EEG as Image](https://mlsanity.com/paper/2602.04769/) (2026)
- [Atoms of Thought: Universal EEG Representation Learning with Microstates](https://mlsanity.com/paper/2605.20182/) (2026)
- [Revitalizing Medical Time Series with Vision-Informed Retrieval: A Vision-Language Perspective](https://mlsanity.com/paper/2609.34652/) (2026)
- [GRFBrain: Graph-Structured Rectified Flows for EEG Dynamic Modeling](https://mlsanity.com/paper/2609.37934/) (2026)
- [MontageFlow: Physically Valid High Fidelity EEG Generation](https://mlsanity.com/paper/2610.14507/) (2026)

## Abstract (arXiv)

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.