REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates
Authors: Arshia Afzal, Grigorios Chrysos, Volkan Cevher, Mahsa Shoaran
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. |
| Researcher Affiliation | Academia | 1INL, EPFL, Switzerland 2LIONS, EPFL, Switzerland 3Department of Electrical and Computer Engineering, University of Wisconsin-Madison, USA. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Visit our web site at https://arshiaafzal.github.io/REST/ |
| Open Datasets | Yes | We used two extensive publicly available datasets for the seizure detection and classification task: the Temple University Hospital EEG Seizure Corpus (TUSZ) (Obeid & Picone, 2016; Shah et al., 2018) and the Children s Hospital Boston (Goldberger et al., 2000) dataset. |
| Dataset Splits | Yes | The original TUSZ Train-set was randomly split into training and validation sets with a ratio of 90/10. For the CHB-MIT dataset, since predefined splits for training, evaluation, and testing are not provided, we randomly selected 80% of the data for training, 10% for evaluation, and 10% for testing. |
| Hardware Specification | Yes | We conducted training on a single NVIDIA A100 GPU with a batch size of 128 EEG clips. |
| Software Dependencies | No | The paper mentions using 'Py Torch' but does not provide specific version numbers for software libraries or frameworks. |
| Experiment Setup | Yes | We optimized the following hyperparameters for REST based on the lowest validation error: a) Number of neurons in each graph convolution layer within the range [16, 32, 64]; b) Initial learning rate within the range [5e-4, 1e-4]; c) Success probability of the random binary mask within [0.1, 0.3, 0.5, 0.7, 1]. For multi-update REST, the number of updates for each time point was randomly selected an inteager from the interval [1, 10]. We conducted training for 500 epochs using a Multistep learning rate scheduler. Five experiments were run in Py Torch with different random seeds. |