[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126624-en":3,"doc-seo-126624-105":30,"detail-sidebar-cat-0-en-105":84},{"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":20,"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":29},126624,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Machine Learning Based IoT Adaptive Architecture for Epilepsy Seizure Detection - Anatomy and Analysis","Seizure tracking is vital for monitoring epilepsy treatments, yet caregiver seizure diaries and clinical observation can miss events. A wearable, noninvasive IoT approach enables better long-term ambulatory monitoring and improved tolerability. The study presents an affordable adaptive architecture using a simple real-time k-Nearest-Neighbors (kNN) model, customizable to individual users with under four seconds of training. Validation uses 500 subjects with data sampled at 178 Hz, achieving a mean accuracy of 94.5%.","Machine Learning Based IoT Adaptive Architecture for Epilepsy Seizure Detection: Anatomy and  \nAnalysis  \nZag ElSayed, Murat Ozer, Nelly Elsayed  \nSchool of Information Technology University of Cincinnati Ohio, United States  \n{elsayezs, ozermm, [elsayeny](elsayeny}@ ucmail.uc.edu)[}](elsayeny}@ ucmail.uc.edu)[@ ucmail.uc.edu](elsayeny}@ ucmail.uc.edu)  \nAhmed Abdelgawad  \nSchool of Engineering and Technology Central Michigan University Michigan, United States [abdel1a@cmich.edu](abdel1a@cmich.edu)  \narXiv :2305 . 19347v2 [ cs .LG] 5 Sep 2023  \nAbstract—A seizure tracking system is crucial for monitoring and evaluating epilepsy treatments. Caretaker seizure diaries are used in epilepsy care today, but clinical seizure monitoring may miss seizures. Monitoring devices that can be worn may be better tolerated and more suitable for long-term ambulatory use. Many techniques and methods are proposed for seizure detection; However, simplicity and affordability are key concepts for daily use while preserving the accuracy of the detection. In this study, we propose a versal, affordable noninvasive based on a simple real-time k-Nearest-Neighbors (kNN) machine learning that can be customized and adapted to individual users in less than four seconds of training time; the system was verified and validated using 500 subjects, with seizure detection data sampled at 178 Hz, the operated with a mean accuracy of (94.5%).  \nIndex Terms—Machine learning, IoT, epilepsy seizure, detection, ML  \nI. INTRODUCTION  \nWhen brain cells breakdown and sends sabotaged electrical signals, it is called a seizure, in some cases different areas of human body are affected by such symptoms. Seizures are very common syndrome and affects more than 40% of the population [1], [2], unfortunately, for a variety of reasons, some people can experience them more frequently (diagnosed with Epilepsy) . In many cases, seizures can be treated, especially if the underlying cause is known. Classically, epilepsy is considered to be a syndrome of repeated seizure, however there is more of depth in the difference, as seizure falls in two categories. First, Provoked Seizure, they take place asa result of additional situations or conditions (high fevers, alcohol or drug withdrawal, low blood sugar), responsible for 25% to 30% of all seizures. Second, Unprovoked Seizures, they happen when a person’s brain is more likely to develop spontaneous seizures and are not indicators of any present medical condition or environment. Additionally, epilepsy is diagnosed when a patient has at least two unprovoked seizures. Moreover, one unprovoked seizure increases the likelihood of at least one more in the subsequent ten years. Epilepsy cannot be determined by a doctor based only on provoked seizures [2] . Seizure can cause problems with sleeping, thinking, and memory, or socializing with others. Seizure is responsible for (55%)  \nFig. 1: SUDEP Conditions and Triggers.  \nof sudden unexpected death in epilepsy (SUDEP) annually, as well as trauma, drowning, or other known causes [3] . Sudden unexpected death in epilepsy (SUDEP) is a rare but serious problem, the stages responsible for SUDEP are illustrated in Fig. ??. It is most common in people between the ages of 20 and 45, and is more common in males and those with childhood-onset epilepsy. It is generally thought that a seizure can cause changes in the brain and body that can lead to respiratory and cardiac problems, which may ultimately result in death. There is a higher odds ratio for SUDEP in these groups, and it is also more common in people who have epilepsy surgery [4] . Studies from the United States and Europe have shown that there is a higher rate of SUDEPin populations with socioeconomic barriers to care, such as lack of employment, lack of access to medications and other treatments, and increased distance from appropriate healthcare providers. However, based on recent population-based studies, there is no increased risk of SUDEP in pa","cbCaivWPetRtBjLZ","https://ap.wps.com/l/cbCaivWPetRtBjLZ","pdf",1051425,1,6,"English","en",105,"# Abstract\n# I. Introduction\n## SUDEP conditions, triggers, and risk factors\n## kNN approach and adaptive wearable architecture\n# Related work / references","[{\"question\":\"How was the proposed approach validated and what accuracy was achieved?\",\"answer\":\"Validation used 500 subjects with detection data sampled at 178 Hz. The system achieved a mean accuracy of 94.5%.\"}]","Machine Learning Based IoT Adaptive Architecture for Epilepsy Seizure Detection - Anatomy and Analysis | PDF",1785933857,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":79,"head_meta":81,"extra_data":83,"updated_unix":28},"machine-learning-based-iot-adaptive-architecture-for-epilepsy-seizure-detection-anatomy-and-analysis","",{"@graph":36,"@context":78},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-iot-adaptive-architecture-for-epilepsy-seizure-detection-anatomy-and-analysis/126624/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"How was the proposed approach validated and what accuracy was achieved?","Question",{"text":76,"@type":77},"Validation used 500 subjects with detection data sampled at 178 Hz. 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