[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120888-en":3,"doc-seo-120888-105":30,"detail-sidebar-cat-0-en-105":91},{"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},120888,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Comparison of Automated Machine Learning (AutoML) Tools for Epileptic Seizure Detection Using Electroencephalograms (EEG) - research report","Epilepsy is a neurological condition marked by recurrent seizures driven by abnormal brain electrical activity. Electroencephalogram (EEG) analysis provides a non-invasive route to quantify brain signals and supports epilepsy diagnosis, while machine learning can improve accuracy and efficiency. Automated machine learning (AutoML) reduces the expertise needed to configure models and automate key steps. This study compares AutoGluon, Auto-Sklearn, and Amazon SageMaker on multiple EEG datasets using metrics such as accuracy, F1, recall, and precision, showing dataset size and tool choice influence performance.","UC Riverside  \nUC Riverside Previously Published Works  \nTitle  \nComparison of Automated Machine Learning (AutoML) Tools for Epileptic Seizure Detection Using Electroencephalograms (EEG)  \nPermalink  \n[https://escholarship.org/uc/item/4v5979wx](https://escholarship.org/uc/item/4v5979wx)  \nJournal  \nComputers, 12(10)  \nISSN  \n2073-431X  \nAuthors  \nLenkala, Swetha  \nMarry, Revathi Gopovaram, Susmitha Reddyet al.  \nPublication Date  \n2023  \nDOI  \n10.3390/computers12100197  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons AttributionNonCommercial-NoDerivatives License, available at [https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \ncomputers   \nArticle  \nComparison of Automated Machine Learning (AutoML) Tools for Epileptic Seizure Detection Using Electroencephalograms (EEG)  \nSwetha Lenkala 1, Revathi Marry 1, Susmitha Reddy Gopovaram 1, Tahir Cetin Akinci 2,3, * and Oguzhan Topsakal 1  \nCitation: Lenkala, S.; Marry, R.; Gopovaram, S.R.; Akinci, T.C.; Topsakal, O. Comparison of Automated Machine Learning (AutoML) Tools for Epileptic Seizure Detection Using Electroencephalograms (EEG) . Computers 2023, 12, 197 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)computers12100197  \nAcademic Editors: Mads Sloth Vinding, Ivan Maximov and Christoph Stefan Aigner  \nReceived: 29 August 2023  \nRevised: 22 September 2023  \nAccepted: 27 September 2023  \nPublished: 29 September 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Science, Florida Polytechnic University, Lakeland, FL 33805, USA;  \nslenkala4630@fl[oridapoly.edu](oridapoly.edu) (S.L.); rmarry4881@fl[oridapoly.edu](oridapoly.edu) (R.M.);  \nsgopavaram2200@fl[oridapoly.edu](oridapoly.edu) (S.R.G.); otopsakal@fl[oridapoly.edu](oridapoly.edu) (O.T.)  \n2 Winston Chung Global Energy Center, University of California at Riverside, Riverside, CA 92509, USA  \n3 Department of Electrical Engineering., Istanbul Technical University (ITU), 34469 Istanbul, Turkey  \n* Correspondence: [tahircetin.akinci@ucr.edu](tahircetin.akinci@ucr.edu)  \nAbstract: Epilepsy is a neurological disease characterized by recurrent seizures caused by abnormal electrical activity in the brain. One of the methods used to diagnose epilepsy is through electroencephalogram (EEG) analysis. EEG is a non-invasive medical test for quantifying electrical activity in the brain. Applying machine learning (ML) to EEG data for epilepsy diagnosis has the potential to be more accurate and efficient. However, expert knowledge is required to set up the ML model with correct hyperparameters. Automated machine learning (AutoML) tools aim to make ML more accessible to non-experts and automate many ML processes to create a high-performing ML model. This article explores the use of automated machine learning (AutoML) tools for diagnosing epilepsy using electroencephalogram (EEG) data. The study compares the performance of three different AutoML tools, AutoGluon, Auto-Sklearn, and Amazon Sagemaker, on three different datasets from the UC Irvine ML Repository, Bonn EEG time series dataset, and Zenodo. Performance measures used for evaluation include accuracy, F1 score, recall, and precision. The results show that all three AutoML tools were able to generate high-performing ML models for the diagnosis of epilepsy. The generated ML models perform better when the training dataset is larger in size. Amazon Sagemaker and Auto-Sklearn performed better with smaller datase","cbCaithUjwWIo2vy","https://ap.wps.com/l/cbCaithUjwWIo2vy","pdf",799965,1,14,"English","en",105,"# Introduction\n## Epilepsy and diagnostic approaches\n## EEG and its role in diagnosis\n## Automated machine learning for EEG\n# Methods and AutoML tools\n## Compared tools: AutoGluon, Auto-Sklearn, Amazon SageMaker\n## Datasets used\n# Evaluation and results\n## Performance metrics\n## Impact of dataset size on performance\n# Conclusion","[{\"question\":\"Why is EEG analysis important for epilepsy detection?\",\"answer\":\"EEG is a non-invasive technique that quantifies electrical activity in the brain. It provides informative data that supports epilepsy diagnosis, and it is well-suited for applying machine learning models.\"},{\"question\":\"Which AutoML tools are compared in the study?\",\"answer\":\"The study compares AutoGluon, Auto-Sklearn, and Amazon SageMaker for epilepsy detection using EEG data.\"},{\"question\":\"How does training dataset size affect AutoML performance?\",\"answer\":\"Results indicate that models generally perform better with larger training datasets. Amazon SageMaker and Auto-Sklearn show stronger performance on smaller datasets compared with the other approaches.\"}]","Comparison of Automated Machine Learning (AutoML) Tools for Epileptic Seizure Detection Using Electroencephalograms (EEG) - research report | PDF",1785732506,35,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"comparison-of-automated-machine-learning-automl-tools-for-epileptic-seizure-detection-using-electroencephalograms-eeg-research-report","",{"@graph":36,"@context":85},[37,54,68],{"@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/comparison-of-automated-machine-learning-automl-tools-for-epileptic-seizure-detection-using-electroencephalograms-eeg-research-report/120888/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is EEG analysis important for epilepsy detection?","Question",{"text":75,"@type":76},"EEG is a non-invasive technique that quantifies electrical activity in the brain. It provides informative data that supports epilepsy diagnosis, and it is well-suited for applying machine learning models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which AutoML tools are compared in the study?",{"text":80,"@type":76},"The study compares AutoGluon, Auto-Sklearn, and Amazon SageMaker for epilepsy detection using EEG data.",{"name":82,"@type":73,"acceptedAnswer":83},"How does training dataset size affect AutoML performance?",{"text":84,"@type":76},"Results indicate that models generally perform better with larger training datasets. Amazon SageMaker and Auto-Sklearn show stronger performance on smaller datasets compared with the other approaches.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]