{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/slimseiz-efficient-channel-adaptive-seizure","title":"SlimSeiz: Efficient Channel-Adaptive Seizure Prediction Using a Mamba-Enhanced Network","arxiv_id":"2410.09998","date":"2024-10-13","proceeding":null,"authors":["Guorui Lu","Jing Peng","Bingyuan Huang","Chang Gao","Todor Stefanov","Yong Hao","Qinyu Chen"],"abstract":"Epileptic seizures cause abnormal brain activity, and their unpredictability can lead to accidents, underscoring the need for long-term seizure prediction. Although seizures can be predicted by analyzing electroencephalogram (EEG) signals, existing methods often require too many electrode channels or larger models, limiting mobile usability. This paper introduces a SlimSeiz framework that utilizes adaptive channel selection with a lightweight neural network model. SlimSeiz operates in two states: the first stage selects the optimal channel set for seizure prediction using machine learning algorithms, and the second stage employs a lightweight neural network based on convolution and Mamba for prediction. On the Children's Hospital Boston-MIT (CHB-MIT) EEG dataset, SlimSeiz can reduce channels from 22 to 8 while achieving a satisfactory result of 94.8% accuracy, 95.5% sensitivity, and 94.0% specificity with only 21.2K model parameters, matching or outperforming larger models' performance. We also validate SlimSeiz on a new EEG dataset, SRH-LEI, collected from Shanghai Renji Hospital, demonstrating its effectiveness across different patients. The code and SRH-LEI dataset are available at https://github.com/guoruilu/SlimSeiz.","url_abs":"https://arxiv.org/abs/2410.09998v1","url_pdf":"https://arxiv.org/pdf/2410.09998v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"slimseiz-efficient-channel-adaptive-seizure","repo_url":"https://github.com/guoruilu/slimseiz","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"mamba","task_name":"Mamba"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"seizure-prediction","task_name":"Seizure prediction"},{"task_slug":"specificity","task_name":"Specificity"},{"task_slug":"channel-selection","task_name":"channel selection"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"mamba","method_name":"Mamba"},{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}