[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120767-en":3,"doc-seo-120767-105":29,"detail-sidebar-cat-0-en-105":89},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":11},120767,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Lightweight Machine Learning for Seizure Detection on Wearable Devices","Epilepsy monitoring requires continuous real-time observation of patient health status and fast alerting when seizures occur. Wearable systems enable ambulatory monitoring and caregiver notification, and they must operate under strict constraints on memory, computing power, and battery lifetime. For the ICASSP 2023 Seizure Detection Challenge, a lightweight machine-learning framework is proposed for real-time epilepsy monitoring on wearable devices, evaluated on the SeizeIT2 dataset from the SensorDot (SD) platform, achieving 73.6% sensitivity and 96.7% specificity for seizure detection.","Lightweight Machine Learning for Seizure Detection on Wearable Devices  \nHuang, Baichuan; Abtahi Fahliani, Azra; Aminifar, Amir  \nPublished in:  \nICASSP, the International Conference on Acoustics, Speech, and Signal Processing 2023  \n2023  \nDocument Version:  \nPublisher's PDF, also known as Version of record  \nLink to publication  \nCitation for published version (APA):  \nHuang, B. , Abtahi Fahliani, A. , & Aminifar, A. (2023) . Lightweight Machine Learning for Seizure Detection on Wearable Devices. In ICASSP, the International Conference on Acoustics, Speech, and Signal Processing 2023 IEEE-Institute of Electrical and Electronics Engineers Inc..  \nTotal number of authors: 3  \nGeneral rights  \nUnless other specific re-use rights are stated the following general rights apply:  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal  \nRead more about Creative commons licenses: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nLIGHTWEIGHT MACHINE LEARNING FOR SEIZURE DETECTION  \nON WEARABLE DEVICES  \nBaichuan Huang, AzraAbtahi, AmirAminifar  \nDepartment of Electrical and Information Technology, Lund University, Sweden {baichuan.huang, azra.abtahi fahliani, [amir.aminifar](amir.aminifar}@eit.lth.se)[}](amir.aminifar}@eit.lth.se)[@eit.lth.se](amir.aminifar}@eit.lth.se)  \nABSTRACT  \nFor patients with epilepsy, automatic epilepsy monitoring, i.e., the process of direct observation of the patient’s health status in real time, is crucial. Wearable systems provide the possibility of real-time epilepsy monitoring and alerting caregivers upon the occurrence of a seizure. In the context of the ICASSP 2023 Seizure Detection Challenge, we propose a lightweight machine-learning framework for real-time epilepsy monitoring on wearable devices. We evaluate our proposed framework on the SeizeIT2 dataset from the wearable SensorDot (SD) of Byteflies. The experimental results show that our proposed framework achieves a sensitivity of 73.6% and a specificity of 96.7% in seizure detection.  \nIndex Terms— Seizure Detection, Lightweight Machine learning, Wearable IoT.  \n1. INTRODUCTION  \nEpilepsy is one of the most common neurological disorders that affects around 65 million people worldwide. Despite the advances in anti-epileptic drugs, one-third of epileptic patients still suffer from recurrent seizures with a high relapse rate. Furthermore, people with epilepsy (PWE) have a 2–3 times higher mortality rate compared to the corresponding healthy population, mainly because of seizure-triggered accidents and Sudden Unexpected Death in Epilepsy (SUDEP) . Video-electroencephalogram (EEG) recording is the gold standard of epilepsy monitoring. However, the standard full scalp-EEG recording setup has several limitations for patient monitoring outside the hospital environment. In contrast, automated EEG-based seizure detection on wearable devices provides the possibility of real-time patient monitoring in ambulatory settings.  \nSmart wearable techniques can detect the onset of seizures in real time and alert family members and caregivers for rescue. However, wearable devices have stringent resource constraints, including limited memory storage, computing  \nThis research has been partially supported by the Wa","cbCaitdRam2tV1WR","https://ap.wps.com/l/cbCaitdRam2tV1WR","pdf",1815207,1,3,"English","en",105,"# Abstract\n# Introduction\n# Lightweight Machine Learning\n## Lightweight Seizure Detection","[{\"question\":\"Why is automatic epilepsy monitoring important for wearable systems?\",\"answer\":\"It enables real-time observation of a patient’s health status and supports alerting caregivers immediately when seizures occur.\"},{\"question\":\"What machine-learning approach is used for lightweight seizure detection?\",\"answer\":\"The proposed framework is based on the Random Forest algorithm and power features extracted from EEG frequency bands.\"},{\"question\":\"How does the framework perform on the SeizeIT2 dataset?\",\"answer\":\"It achieves 73.6% sensitivity and 96.7% specificity for seizure detection on the validation data.\"}]","Lightweight Machine Learning for Seizure Detection on Wearable Devices | PDF",1785731925,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":28},"lightweight-machine-learning-for-seizure-detection-on-wearable-devices","",{"@graph":35,"@context":83},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":21},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/lightweight-machine-learning-for-seizure-detection-on-wearable-devices/120767/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":23,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"Why is automatic epilepsy monitoring important for wearable systems?","Question",{"text":73,"@type":74},"It enables real-time observation of a patient’s health status and supports alerting caregivers immediately when seizures occur.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"What machine-learning approach is used for lightweight seizure detection?",{"text":78,"@type":74},"The proposed framework is based on the Random Forest algorithm and power features extracted from EEG frequency bands.",{"name":80,"@type":71,"acceptedAnswer":81},"How does the framework perform on the SeizeIT2 dataset?",{"text":82,"@type":74},"It achieves 73.6% sensitivity and 96.7% specificity for seizure detection on the validation data.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":90},[91,95,99,103,108,113,118,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":92,"show_sort_weight":93,"slug":94},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":96,"show_sort_weight":97,"slug":98},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":100,"show_sort_weight":101,"slug":102},"Exam",70,"exam",{"id":104,"doc_module":4,"doc_module_name":45,"category_name":105,"show_sort_weight":106,"slug":107},5,"Comic",60,"comic",{"id":109,"doc_module":4,"doc_module_name":45,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":114,"doc_module":4,"doc_module_name":45,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":119,"slug":120},30,"research-report",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":104,"slug":136},19,"General","general"]