Applications of Artificial Intelligence in Automatic Detection of Epileptic Seizures Using EEG Signals: A Review

<!DOCTYPE html> <html lang="en" xml:lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> Applications of Artificial Intelligence in Automatic Detection of Epileptic Seizures Using EEG Signals: A Review | Artificial Intelligence and Applications </title> <link rel="icon" href="https://ojs.bonviewpress.com/public/journals/5/favicon_en_US.png"> <meta name="generator" content="Open Journal Systems 3.4.0.7"> <meta name="gs_meta_revision" content="1.1"/> <meta name="citation_journal_title" content="Artificial Intelligence and Applications"/> <meta name="citation_journal_abbrev" content="AIA"/> <meta name="citation_issn" content="2811-0854"/> <meta name="citation_author" content="Sani Saminu"/> <meta name="citation_author_institution" content="State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, China"/> <meta name="citation_author" content="Guizhi Xu"/> <meta name="citation_author_institution" content="State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, China"/> <meta name="citation_author" content="Shuai Zhang"/> <meta name="citation_author_institution" content="State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, China"/> <meta name="citation_author" content="Isselmou Ab El Kader"/> <meta name="citation_author_institution" content="State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, China"/> <meta name="citation_author" content="Hajara Abdulkarim Aliyu"/> <meta name="citation_author_institution" content="Jigawa State Polytechnic Dutse, Nigeria"/> <meta name="citation_author" content="Adamu Halilu Jabire"/> <meta name="citation_author_institution" content="Taraba State University Jalingo, Nigeria"/> <meta name="citation_author" content="Yusuf Kola Ahmed"/> <meta name="citation_author_institution" content="Biomedical Engineering Department, University of Ilorin, Nigeria"/> <meta name="citation_author" content="Mohammed Jajere Adamu"/> <meta name="citation_author_institution" content="School of Microelectronics, Tianjin University, China"/> <meta name="citation_title" content="Applications of Artificial Intelligence in Automatic Detection of Epileptic Seizures Using EEG Signals: A Review"/> <meta name="citation_language" content="en"/> <meta name="citation_date" content="2023"/> <meta name="citation_volume" content="1"/> <meta name="citation_issue" content="1"/> <meta name="citation_firstpage" content="11"/> <meta name="citation_lastpage" content="25"/> <meta name="citation_doi" content="10.47852/bonviewAIA2202297"/> <meta name="citation_abstract_html_url" content="https://ojs.bonviewpress.com/index.php/AIA/article/view/297"/> <meta name="citation_abstract" xml:lang="en" content="Correctly interpreting an Electroencephalography (EEG) signal with high accuracy is a tedious and time-consuming task that may take several years of manual training due to its complexity, noisy, non-stationarity, and nonlinear nature. To deal with the vast amount of data and recent challenges of meeting the requirements to develop low cost, high speed, low complexity smart internet of medical things (IoMT) computer-aided devices (CAD), artificial intelligence (AI) techniques which consist of machine learning and deep learning plays a vital role in achieving the stated goals. Over the years, machine learning techniques have been developed to detect and classify epileptic seizures. But until recently, deep learning techniques have been applied in various applications such as image processing and computer visions. However, several research studies have turned their attention to exploring the efficacy of deep learning to overcome some challenges associated with conventional automatic seizure detection techniques. This paper endeavors to review and investigate the fundamentals, applications, and progress of AI-based techniques applied in CAD system for epileptic seizure detection and characterisation. It would help in actualising and realising smart wireless wearable medical devices so that patients can monitor seizures before their occurrence and help doctors diagnose and treat them. The work reveals that the recent application of deep learning algorithms improves the realisation and implementation of mobile health in a clinical environment. 聽 Received: 5 July 2022 | Revised: 10 August 2022 | Accepted: 27 August 2022 聽 Conflicts of Interest The authors declare that they have no conflicts of interest to this work."/> <meta name="citation_keywords" xml:lang="en" content="EEG"/> <meta name="citation_keywords" xml:lang="en" content="CAD system"/> <meta name="citation_keywords" xml:lang="en" content="machine learning"/> <meta name="citation_keywords" xml:lang="en" content="deep learning"/> <meta name="citation_keywords" xml:lang="en" content="artificial intelligence"/> <meta name="citation_keywords" xml:lang="en" content="epileptic seizures"/> <meta name="citation_pdf_url" content="https://ojs.bonviewpress.com/index.php/AIA/article/download/297/184"/> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Sani Saminu"/> <meta name="DC.Creator.PersonalName" content="Guizhi Xu"/> <meta name="DC.Creator.PersonalName" content="Shuai Zhang"/> <meta name="DC.Creator.PersonalName" content="Isselmou Ab El Kader"/> <meta name="DC.Creator.PersonalName" content="Hajara Abdulkarim Aliyu"/> <meta name="DC.Creator.PersonalName" content="Adamu Halilu Jabire"/> <meta name="DC.Creator.PersonalName" content="Yusuf Kola Ahmed"/> <meta name="DC.Creator.PersonalName" content="Mohammed Jajere Adamu"/> <meta name="DC.Date.created" scheme="ISO8601" content="2022-08-31"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2022-07-05"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2023-01-30"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-11-25"/> <meta name="DC.Description" xml:lang="en" content="Correctly interpreting an Electroencephalography (EEG) signal with high accuracy is a tedious and time-consuming task that may take several years of manual training due to its complexity, noisy, non-stationarity, and nonlinear nature. To deal with the vast amount of data and recent challenges of meeting the requirements to develop low cost, high speed, low complexity smart internet of medical things (IoMT) computer-aided devices (CAD), artificial intelligence (AI) techniques which consist of machine learning and deep learning plays a vital role in achieving the stated goals. Over the years, machine learning techniques have been developed to detect and classify epileptic seizures. But until recently, deep learning techniques have been applied in various applications such as image processing and computer visions. However, several research studies have turned their attention to exploring the efficacy of deep learning to overcome some challenges associated with conventional automatic seizure detection techniques. This paper endeavors to review and investigate the fundamentals, applications, and progress of AI-based techniques applied in CAD system for epileptic seizure detection and characterisation. It would help in actualising and realising smart wireless wearable medical devices so that patients can monitor seizures before their occurrence and help doctors diagnose and treat them. The work reveals that the recent application of deep learning algorithms improves the realisation and implementation of mobile health in a clinical environment. 聽 Received: 5 July 2022 | Revised: 10 August 2022 | Accepted: 27 August 2022 聽 Conflicts of Interest The authors declare that they have no conflicts of interest to this work."/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="297"/> <meta name="DC.Identifier.pageNumber" content="11-25"/> <meta name="DC.Identifier.DOI" content="10.47852/bonviewAIA2202297"/> <meta name="DC.Identifier.URI" content="https://ojs.bonviewpress.com/index.php/AIA/article/view/297"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2022 Authors"/> <meta name="DC.Rights" content="https://creativecommons.org/licenses/by/4.0/"/> <meta name="DC.Source" content="Artificial Intelligence and Applications"/> <meta name="DC.Source.ISSN" content="2811-0854"/> 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class="page page_article"> <nav class="cmp_breadcrumbs" role="navigation" aria-label="You are here:"> <ol> <li> <a href="https://ojs.bonviewpress.com/index.php/AIA/index"> Home </a> <span class="separator">/</span> </li> <li> <a href="https://ojs.bonviewpress.com/index.php/AIA/issue/archive"> Archives </a> <span class="separator">/</span> </li> <li> <a href="https://ojs.bonviewpress.com/index.php/AIA/issue/view/34"> Vol. 1 No. 1 (2023) </a> <span class="separator">/</span> </li> <li class="current" aria-current="page"> <span aria-current="page"> Review </span> </li> </ol> </nav> <article class="obj_article_details"> <h1 class="page_title"> Applications of Artificial Intelligence in Automatic Detection of Epileptic Seizures Using EEG Signals: A Review </h1> <div class="row"> <div class="main_entry"> <section class="item authors"> <h2 class="pkp_screen_reader">Authors</h2> <ul class="authors"> <li> <span class="name"> Sani Saminu </span> <span class="affiliation"> State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, China </span> </li> <li> <span class="name"> Guizhi Xu </span> <span class="affiliation"> State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, China </span> </li> <li> <span class="name"> Shuai Zhang </span> <span class="affiliation"> State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, China </span> </li> <li> <span class="name"> Isselmou Ab El Kader </span> <span class="affiliation"> State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, China </span> </li> <li> <span class="name"> Hajara Abdulkarim Aliyu </span> <span class="affiliation"> Jigawa State Polytechnic Dutse, Nigeria </span> </li> <li> <span class="name"> Adamu Halilu Jabire </span> <span class="affiliation"> Taraba State University Jalingo, Nigeria </span> </li> <li> <span class="name"> Yusuf Kola Ahmed </span> <span class="affiliation"> Biomedical Engineering Department, University of Ilorin, Nigeria </span> <span class="orcid"> <svg class="orcid_icon" viewBox="0 0 256 256" aria-hidden="true"> <style type="text/css"> .st0{fill:#A6CE39;} .st1{fill:#FFFFFF;} </style> <path class="st0" d="M256,128c0,70.7-57.3,128-128,128C57.3,256,0,198.7,0,128C0,57.3,57.3,0,128,0C198.7,0,256,57.3,256,128z"/> <g> <path class="st1" d="M86.3,186.2H70.9V79.1h15.4v48.4V186.2z"/> <path class="st1" d="M108.9,79.1h41.6c39.6,0,57,28.3,57,53.6c0,27.5-21.5,53.6-56.8,53.6h-41.8V79.1z M124.3,172.4h24.5 c34.9,0,42.9-26.5,42.9-39.7c0-21.5-13.7-39.7-43.7-39.7h-23.7V172.4z"/> <path class="st1" d="M88.7,56.8c0,5.5-4.5,10.1-10.1,10.1c-5.6,0-10.1-4.6-10.1-10.1c0-5.6,4.5-10.1,10.1-10.1 C84.2,46.7,88.7,51.3,88.7,56.8z"/> </g> </svg> <a href="https://orcid.org/0000-0002-1023-0307" target="_blank"> https://orcid.org/0000-0002-1023-0307 </a> </span> </li> <li> <span class="name"> Mohammed Jajere Adamu </span> <span class="affiliation"> School of Microelectronics, Tianjin University, China </span> </li> </ul> </section> <section class="item doi"> <h2 class="label"> DOI: </h2> <span class="value"> <a href="https://doi.org/10.47852/bonviewAIA2202297"> https://doi.org/10.47852/bonviewAIA2202297 </a> </span> </section> <section class="item keywords"> <h2 class="label"> Keywords: </h2> <span class="value"> EEG, CAD system, machine learning, deep learning, artificial intelligence, epileptic seizures </span> </section> <section class="item abstract"> <h2 class="label">Abstract</h2> <p>Correctly interpreting an Electroencephalography (EEG) signal with high accuracy is a tedious and time-consuming task that may take several years of manual training due to its complexity, noisy, non-stationarity, and nonlinear nature. To deal with the vast amount of data and recent challenges of meeting the requirements to develop low cost, high speed, low complexity smart internet of medical things (IoMT) computer-aided devices (CAD), artificial intelligence (AI) techniques which consist of machine learning and deep learning plays a vital role in achieving the stated goals. Over the years, machine learning techniques have been developed to detect and classify epileptic seizures. But until recently, deep learning techniques have been applied in various applications such as image processing and computer visions. However, several research studies have turned their attention to exploring the efficacy of deep learning to overcome some challenges associated with conventional automatic seizure detection techniques. This paper endeavors to review and investigate the fundamentals, applications, and progress of AI-based techniques applied in CAD system for epileptic seizure detection and characterisation. It would help in actualising and realising smart wireless wearable medical devices so that patients can monitor seizures before their occurrence and help doctors diagnose and treat them. The work reveals that the recent application of deep learning algorithms improves the realisation and implementation of mobile health in a clinical environment.</p> <p>聽</p> <p><strong>Received:</strong> 5 July 2022<strong> | Revised:</strong> 10 August 2022 <strong>| Accepted:</strong> 27 August 2022</p> <p>聽</p> <p><strong>Conflicts of Interest</strong></p> <p>The authors declare that they have no conflicts of interest to this work.</p> </section> <br /><div class="separator"></div><div class="item abstract" id="trendmd-suggestions"></div><script defer src='//js.trendmd.com/trendmd.min.js' data-trendmdconfig='{"website_id":"89268", "element":"#trendmd-suggestions"}'></script><div class="item downloads_chart"> <h3 class="label"> Metrics </h3> <div id="paperbuzz"><div id="loading">Metrics Loading ...</div></div> <script type="text/javascript"> window.onload = function () { var options = { paperbuzzStatsJson: JSON.parse('{\"altmetrics_sources\":[{\"events\":null,\"events_count\":1909,\"events_count_by_day\":[{\"count\":\"16\",\"date\":\"2022-08-31\"},{\"count\":\"1\",\"date\":\"2022-09-01\"},{\"count\":\"7\",\"date\":\"2022-09-02\"},{\"count\":\"4\",\"date\":\"2022-09-03\"},{\"count\":\"3\",\"date\":\"2022-09-04\"},{\"count\":\"7\",\"date\":\"2022-09-05\"},{\"count\":\"7\",\"date\":\"2022-09-06\"},{\"count\":\"6\",\"date\":\"2022-09-07\"},{\"count\":\"10\",\"date\":\"2022-09-08\"},{\"count\":\"2\",\"date\":\"2022-09-09\"},{\"count\":\"3\",\"date\":\"2022-09-10\"},{\"count\":\"4\",\"date\":\"2022-09-11\"},{\"count\":\"2\",\"date\":\"2022-09-12\"},{\"count\":\"4\",\"date\":\"2022-09-13\"},{\"count\":\"2\",\"date\":\"2022-09-14\"},{\"count\":\"3\",\"date\":\"2022-09-15\"},{\"count\":\"3\",\"date\":\"2022-09-16\"},{\"count\":\"2\",\"date\":\"2022-09-17\"},{\"count\":\"1\",\"date\":\"2022-09-18\"},{\"count\":\"1\",\"date\":\"2022-09-19\"},{\"count\":\"3\",\"date\":\"2022-09-20\"},{\"count\":\"1\",\"date\":\"2022-09-21\"},{\"count\":\"2\",\"date\":\"2022-09-22\"},{\"count\":\"5\",\"date\":\"2022-09-23\"},{\"count\":\"1\",\"date\":\"2022-09-25\"},{\"count\":\"4\",\"date\":\"2022-09-27\"},{\"count\":\"7\",\"date\":\"2022-09-28\"},{\"count\":\"2\",\"date\":\"2022-09-29\"},{\"count\":\"2\",\"date\":\"2022-09-30\"}],\"events_count_by_month\":[{\"count\":\"16\",\"date\":\"2022-08\"},{\"count\":\"99\",\"date\":\"2022-09\"},{\"count\":\"38\",\"date\":\"2022-10\"},{\"count\":\"48\",\"date\":\"2022-11\"},{\"count\":\"28\",\"date\":\"2022-12\"},{\"count\":\"35\",\"date\":\"2023-01\"},{\"count\":\"89\",\"date\":\"2023-02\"},{\"count\":\"112\",\"date\":\"2023-03\"},{\"count\":\"95\",\"date\":\"2023-04\"},{\"count\":\"55\",\"date\":\"2023-05\"},{\"count\":\"56\",\"date\":\"2023-06\"},{\"count\":\"57\",\"date\":\"2023-07\"},{\"count\":\"51\",\"date\":\"2023-08\"},{\"count\":\"36\",\"date\":\"2023-09\"},{\"count\":\"26\",\"date\":\"2023-10\"},{\"count\":\"72\",\"date\":\"2023-11\"},{\"count\":\"57\",\"date\":\"2023-12\"},{\"count\":\"90\",\"date\":\"2024-01\"},{\"count\":\"44\",\"date\":\"2024-02\"},{\"count\":\"131\",\"date\":\"2024-03\"},{\"count\":\"76\",\"date\":\"2024-04\"},{\"count\":\"76\",\"date\":\"2024-05\"},{\"count\":\"69\",\"date\":\"2024-06\"},{\"count\":\"83\",\"date\":\"2024-07\"},{\"count\":\"73\",\"date\":\"2024-08\"},{\"count\":\"141\",\"date\":\"2024-09\"},{\"count\":\"90\",\"date\":\"2024-10\"},{\"count\":\"66\",\"date\":\"2024-11\"}],\"events_count_by_year\":[{\"count\":\"229\",\"date\":\"2022\"},{\"count\":\"741\",\"date\":\"2023\"},{\"count\":\"939\",\"date\":\"2024\"}],\"source\":{\"display_name\":\"File downloads\"},\"source_id\":\"fileDownloads\"}],\"crossref_event_data_url\":\"https:\\/\\/api.eventdata.crossref.org\\/v1\\/events?rows=1000&filter=from-collected-date:1990-01-01,until-collected-date:2099-01-01,obj-id:10.47852\\/bonviewaia2202297\",\"doi\":\"10.47852\\/bonviewaia2202297\",\"metadata\":{\"DOI\":\"10.47852\\/bonviewaia2202297\",\"ISSN\":[\"2811-0854\"],\"URL\":\"http:\\/\\/dx.doi.org\\/10.47852\\/bonviewaia2202297\",\"abstract\":\"<jats:p>Correctly interpreting an Electroencephalography (EEG) signal with high accuracy is a tedious and time-consuming task that may take several years of manual training due to its complexity, noisy, non-stationarity, and nonlinear nature. 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The full text ingestion is still in progress and will be completed at January, 2025.</p> </div> </article> <article class="block_announcements_article"> <h3 class="block_announcements_article_headline"> <a href="https://ojs.bonviewpress.com/index.php/AIA/announcement/view/87"> AIA Published Volume 2, Issue 4 on October 28, 2024 </a> </h3> <time class="block_announcements_article_date" datetime="2024-10-28"> <strong>October 28, 2024</strong> </time> <div class="block_announcements_article_content"> <p>We are excited to announce that <em><strong>Artificial Intelligence and Applications (AIA)</strong></em> published Volume 2 Issue 4 on October 28, 2024!</p> </div> </article> <a id="show-all" href="https://ojs.bonviewpress.com/index.php/AIA/announcement">Show all announcements ...</a> </div> </div> <div class="pkp_block block_keyword_cloud"> <h2 class="title">Keywords</h2> <div class="content" id='wordcloud'></div> <script> function randomColor() { var cores = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']; return cores[Math.floor(Math.random()*cores.length)]; } document.addEventListener("DOMContentLoaded", function() { var keywords = [{"text":"text detection","size":1},{"text":"text recognition","size":1},{"text":"text spotting","size":1},{"text":"text classification","size":1},{"text":"scene text","size":1},{"text":"car number plate detection","size":1},{"text":"optical character recognition","size":1},{"text":"supervised machine learning","size":1},{"text":"distributed machine learning","size":1},{"text":"anomaly detection","size":1},{"text":"structural health monitoring","size":1},{"text":"vehicle-bridge collisions","size":1},{"text":"railway bridges","size":1},{"text":"classification model","size":1},{"text":"deep learning","size":1},{"text":"polyp detection","size":1},{"text":"cnn","size":1},{"text":"image classification","size":1},{"text":"colorectal disease","size":1},{"text":"alzheimer's disease","size":1},{"text":"intervention techniques","size":1},{"text":"conventional methods","size":1},{"text":"artificial intelligence","size":1},{"text":"cognitive stimulation","size":1},{"text":"reality orientation","size":1},{"text":"reminiscence therapy","size":1},{"text":"enemy identification","size":1},{"text":" text similarity","size":1},{"text":"sentence transformer models","size":1},{"text":"natural language processing","size":1},{"text":"machine learning","size":1},{"text":"hr demand","size":1},{"text":"business","size":1},{"text":"hr management","size":1},{"text":"m-knn algorithm","size":1},{"text":"origin tool","size":1},{"text":"soliton solutions","size":1},{"text":"modified benjamin-bona-mahony equation","size":1},{"text":"ostrovsky-benjamin-bona-mahony equation","size":1},{"text":"mikhailov-novikov-wang equation","size":1},{"text":"physics informed neural networks","size":1},{"text":"cancer prediction","size":1},{"text":"prostate cancer","size":1},{"text":"unsupervised learning","size":1},{"text":"intelligent system","size":1},{"text":"cybernetics","size":1},{"text":"decision support","size":1},{"text":"baby cry","size":1},{"text":"multiple instance learning","size":1},{"text":"audio classification","size":1}]; var totalWeight = 0; var blockWidth = 300; var blockHeight = 200; var transitionDuration = 200; var length_keywords = keywords.length; var layout = d3.layout.cloud(); layout.size([blockWidth, blockHeight]) .words(keywords) .fontSize(function(d) { return fontSize(+d.size); }) .on('end', draw); var svg = d3.select("#wordcloud").append("svg") .attr("viewBox", "0 0 " + blockWidth + " " + blockHeight) .attr("width", '100%'); function update() { var words = layout.words(); fontSize = d3.scaleLinear().range([16, 34]); if (words.length) { fontSize.domain([+words[words.length - 1].size || 1, +words[0].size]); } } keywords.forEach(function(item,index){totalWeight += item.size;}); update(); function draw(words, bounds) { var width = layout.size()[0], height = layout.size()[1]; scaling = bounds ? 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Applications of Artificial Intelligence in Automatic Detection of Epileptic Seizures Using EEG Signals: A Review | Artificial Intelligence and Applications