[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120922-en":3,"doc-seo-120922-105":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":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":29},120922,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",7,"Healthcare","Fatigue State Detection Through Multiple Machine Learning Classifiers Using EEG Signal","Fatigued drivers can trigger serious long-distance highway accidents, making reliable fatigue-state identification a critical safety need. This study develops a dependable EEG-based fatigue detection system by applying preprocessing to seventy-six subjects’ electroencephalogram readings. Three machine learning approaches—Decision Tree, K-Nearest Neighbors, and Random Forest—are tested using analytical methods to classify fatigue states. Using all classifiers yields satisfactory performance versus state-of-the-art methods, with Decision Tree achieving 88.61% for four-class and 88.21% for two-class setups, supporting practical real-time deployment.","[http://arqiipubl.com/ams](http://arqiipubl.com/ams)  \nAPPLICATIONS OF  \nMODELLING AND SIMULATION  \neISSN 2600-8084 VOL 7, 2023, 178-189  \nFatigue State Detection Through Multiple Machine Learning Classifiers Using EEG Signal  \nMd Mahmudul Hasan1, Mirza Mahfuj Hossain2 and Norizam Sulaiman1*  \n1Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600  \nPekan, Pahang, Malaysia  \n2Department of Computer Science and Engineering, Jashore University of Science and Technology, Jashore-7408, Jashore,  \nBangladesh  \n*[Corresponding author: norizam@umpsa.edu.my](Corresponding author: norizam@umpsa.edu.my)  \nSubmitted 9 September 2023, Revised 19 November 2023, Accepted 2 December 2023, Available online 3 December 2023. Copyright © 2023 The Authors.  \nAbstract: Fatigued drivers can often cause long-distance accidents worldwide. Fatigue states are the primary cause of highway accidents. This study is conducted to provide a comprehensive and reliable fatigue state detection system to avoid accidentsand make a good decision. Three machine learning algorithms were applied to seventy-six subjects' electroencephalogram (EEG) readings to test their performance. A preprocessing stage extracts relevant information before applying machine learning algorithms to the signal. Three analytical methods were employed in this study, specifically the Decision Tree, the K-Nearest Neighbors and the Random Forest. The study revealed that employing all the classifiers resulted in a satisfactory accuracy rate compared to existing state-of-the-art methods for detecting fatigue states. The classification accuracy using Decision Tree for four classes and two classes were achieved at 88.61% and 88.21% respectively, which can make this EEG-based technology a practical and dependable solution for real-time applications.  \nKeywords: Decision Tree; EEG signal; Fatigue detection; K-Nearest Neighbor; Machine learning; Random Forest.   \n1. INTRODUCTION  \nApproximately 15% to 20% of persons within the general population experience a condition known as excessive daytime sleepiness (EDS) [1-4], resulting in reduced work and driving efficacy. The primary etiological factors contributing to the development of EDS encompass socially induced sleep deprivation in persons without underlying medical conditions, medical diseases such as sleep apnea or narcolepsy, and the use of sedative medications [5-7] . The accurate evaluation of sleepiness holds significant importance in the areas of diagnosis, therapy, and the determination of driving capability. Despite the advancements in technology, the accurate evaluation of tiredness continues to pose a significant difficulty in the field of sleepwake medicine [8]. Driver fatigue has been recognized as a prominent factor contributing to road accidents in numerous nations [9] . Globally, road accidents cause 1.17 million deaths annually, 70% of which take place in poor nations. Pedestrians account for 65% of these deaths, with children making up 35% of the victims [10] . Road crashes cause injuries to between 23 and 34 million people annually, according to estimates [10] . The reason behind the fatigue states and associated risks of fatigue state is illustrated in Figure 1.  \nFigure 1. Fatigue states: causes and risks  \nThis article is distributed under a Creative Commons Attribution 4.0 License that permits any use, reproduction and distribution of the work without further permission provided that the original work is properly cited.  \n178  \nNumerous research has been undertaken in the course of time with the aim of detecting drowsiness and alerting the driver, hence mitigating the frequency of accidents [11] . Another study on drowsiness and fatigue, the equipment that detects drowsiness and fatigue in drivers is variously known as a driver vigilance monitor, a drowsiness detection system, or a fatigue monitoring system [12] . A variety of valuable indicators can be employed to m","cbCaimvRUoEC8pdK","https://ap.wps.com/l/cbCaimvRUoEC8pdK","pdf",682040,1,12,"English","en",105,"# Introduction\n## Driver fatigue and its risks\n## Measuring sleepiness and vigilance\n# Methods\n## Data and preprocessing\n## Classifiers\n# Results\n## Classification accuracy\n# Conclusion","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study addresses fatigue-state detection for drivers using EEG to help prevent highway accidents caused by driver tiredness.\"},{\"question\":\"Which EEG-based machine learning classifiers are used?\",\"answer\":\"Decision Tree, K-Nearest Neighbors, and Random Forest are applied to EEG readings after a preprocessing stage.\"},{\"question\":\"How accurate is the proposed approach?\",\"answer\":\"Using a Decision Tree model, accuracy reaches 88.61% for four classes and 88.21% for two classes, and combining all classifiers gives satisfactory results compared with existing approaches.\"}]","Fatigue State Detection Through Multiple Machine Learning Classifiers Using EEG Signal | PDF",1785732714,30,{"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},"fatigue-state-detection-through-multiple-machine-learning-classifiers-using-eeg-signal","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/fatigue-state-detection-through-multiple-machine-learning-classifiers-using-eeg-signal/120922/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address?","Question",{"text":75,"@type":76},"The study addresses fatigue-state detection for drivers using EEG to help prevent highway accidents caused by driver tiredness.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which EEG-based machine learning classifiers are used?",{"text":80,"@type":76},"Decision Tree, K-Nearest Neighbors, and Random Forest are applied to EEG readings after a preprocessing stage.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate is the proposed approach?",{"text":84,"@type":76},"Using a Decision Tree model, accuracy reaches 88.61% for four classes and 88.21% for two classes, and combining all classifiers gives satisfactory results compared with existing 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},[93,97,101,105,110,115,118,122,127,130,134],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":29,"slug":121},8,"Research & Report","research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]