[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127859-en":3,"doc-seo-127859-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127859,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning Models for Seizure Prediction and Detection from EEG","Epilepsy involves repeated unprovoked seizures that can lead to serious complications, making timely care essential. This thesis develops machine learning methods to both detect seizures and predict them from EEG recordings by identifying the preictal stage prior to seizure onset. A manually defined prediction target avoids reliance on manually annotated preictal phases, testing 30s and 60s windows. Signals are represented by 186 time-, frequency-, and time-frequency features and evaluated using SVM, random forest, logistic regression, and MLP, with random forest achieving the strongest results for detection and classification.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nMachine Learning Models for Seizure Prediction and Detection from EEG  \nPermalink  \n[https://escholarship.org/uc/item/0jp471c0](https://escholarship.org/uc/item/0jp471c0)  \nAuthor  \nAli, Ahmed Siddique  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nLos Angeles  \nMachine Learning Models for Seizure Prediction and Detection from EEG  \nA thesis submitted in partial satisfaction of the requirements for the degree Master of Science in Bioengineering  \nby  \nAhmed Siddique Ali  \n© Copyright by Ahmed Siddique Ali  \n2024  \nABSTRACT OF THE THESIS  \nMachine Learning Models for Seizure Prediction and Detection from EEG  \nby  \nAhmed Siddique Ali  \nMaster of Science in Bioengineering University of California, Los Angeles, 2024 Professor Wentai Liu, Chair  \nEpilepsy is a disease characterized by having multiple unprovoked seizures; it can cause serious health complications for the individuals affected. The ability to predict seizures from EEG recordings can improve the standard of care for epilepsy patients by allowing care to be provided in a timely manner. In addition, detecting seizure occurrence in electroencephalogram (EEG) recordings can greatly speed up the time-and labor-intensive process ofEEG annotation, which will also improve the level of care for patients. This study sought to both detect seizures and predict them by detecting the preictal stage before onset using machine learning (ML) models. To avoid the need for a manually-annotated preictal phase, a time period known as the“prediction target” was manually chosen and the signal was considered “preictal” if it fell within this period before seizure onset; prediction targets of 30 and 60 seconds were tested. A total of 186 features were extracted from the signal in the time-domain, the frequency-domain, and the time-frequency domain in order to characterize the most information about the signals’ shape  \nand frequency content. The extracted features were used with four different ML models: Support Vector Machine (SVM), random forest, logistic regression, and Multilayer Perceptron (MLP) . Of the 4 models, random forest performed the best, with an average accuracy of 65% on classification between ictal, preictal and background, and 85% on detection alone. The model showed strong performance on detecting seizures and an ability to detect the preictal phase. With further improvements, it could become highly effective for both seizure detection and prediction.  \nThe thesis of Ahmed Siddique Ali is approved.  \nWilliam F. Speier Corey Wells Arnold Aaron S. Meyer Wentai Liu, Committee Chair  \nUniversity of California, Los Angeles 2024  \nTo my family and friends Thankyouforyour unwavering support  \nv  \nTable of Contents  \n1. Introduction................................................................................................................................ 1  \n1.1 Epilepsy................................................................................................................................ 1  \n1.2. EEG For Epilepsy............................................................................................................... 2  \n1.3. Current Methods of Seizure Prediction...............................................................................4  \n2. Methodology............................................................................................................................... 9  \n2.1. Dataset............................................................................................................................... 10  \n2.2. Preprocessing.................................................................................................................... 11  \n2.3. Division into Epochs....................................................................","cbCaisdT1SdtWOda","https://ap.wps.com/l/cbCaisdT1SdtWOda","pdf",1229725,2,1,65,"English","en",105,"# Introduction\n## Epilepsy\n## EEG For Epilepsy\n## Current Methods of Seizure Prediction\n# Methodology\n## Dataset\n## Preprocessing\n## Division into Epochs\n## Data Balancing\n## Patient Selection\n## Feature Extraction\n## Statistical Features\n## Hjorth Parameters\n## Average band power\n## Empirical Mode Decomposition\n## Continuous Wavelet Transform\n## Principal Component Analysis\n## Cross-validation\n## Models\n## Support Vector Machine\n## Random Forest\n## Logistic Regression\n## Multilayer Perceptron","[{\"question\":\"How does the thesis define the preictal stage for seizure prediction?\",\"answer\":\"A prediction target time period is manually chosen. EEG segments are labeled “preictal” if they fall within this window before seizure onset, avoiding the need for manually annotated preictal phases.\"},{\"question\":\"What models are evaluated for seizure detection and prediction?\",\"answer\":\"Four machine learning models are tested: Support Vector Machine (SVM), random forest, logistic regression, and Multilayer Perceptron (MLP).\"},{\"question\":\"Which model performs best, and what accuracies are reported?\",\"answer\":\"Random forest performs best, reaching about 65% average accuracy for classifying ictal, preictal, and background, and about 85% accuracy for detection alone.\"}]","Machine Learning Models for Seizure Prediction and Detection from EEG | PDF",1785942393,164,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-models-for-seizure-prediction-and-detection-from-eeg","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/machine-learning-models-for-seizure-prediction-and-detection-from-eeg/127859/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How does the thesis define the preictal stage for seizure prediction?","Question",{"text":76,"@type":77},"A prediction target time period is manually chosen. EEG segments are labeled “preictal” if they fall within this window before seizure onset, avoiding the need for manually annotated preictal phases.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What models are evaluated for seizure detection and prediction?",{"text":81,"@type":77},"Four machine learning models are tested: Support Vector Machine (SVM), random forest, logistic regression, and Multilayer Perceptron (MLP).",{"name":83,"@type":74,"acceptedAnswer":84},"Which model performs best, and what accuracies are reported?",{"text":85,"@type":77},"Random forest performs best, reaching about 65% average accuracy for classifying ictal, preictal, and background, and about 85% accuracy for detection alone.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]