[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124100-en":3,"doc-seo-124100-105":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},124100,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Diagnosis of Parkinson's Disease Using EEG Signals and Machine Learning Techniques - A Comprehensive Study","Parkinson's disease is a widespread neurodegenerative condition where early diagnosis enables timely intervention and better patient outcomes. This study proposes an innovative diagnostic pipeline based on human EEG signal analysis, leveraging a Support Vector Machine (SVM) classification model. The work combines a comprehensive review of EEG signal analysis and machine learning methods with advanced feature engineering, extensive hyperparameter tuning, and kernel selection to improve accuracy. Performance is validated on EEG recordings from Parkinson's patients and healthy controls, while interpretability and healthcare ethics such as data privacy and bias are addressed.","Diagnosis of Parkinson's Disease Using EEG Signals and Machine Learning Techniques: A  \nComprehensive Study  \n1st Maryam Allahbakhshi  \nDepartment of Biomedical Engineering Qazvin Branch, Islamic Azad University, Qazvin, Iran [Mimabakhshi97@gmail.com](Mimabakhshi97@gmail.com)  \n2nd Aylar Sadri  \nDepartment of Biomedical Engineering Qazvin Branch, Islamic Azad University, Qazvin, Iran [Aylarsadri15@gmail.com](Aylarsadri15@gmail.com)  \n3rd Seyed Omid Shahdi  \nDepartment of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran [Shahdi@qiau.ac.ir](Shahdi@qiau.ac.ir)  \nAbstract— Parkinson's disease is a widespread neurodegenerative condition necessitating early diagnosis for effective intervention. This paper introduces an innovative method for diagnosing Parkinson's disease through the analysis of human EEG signals, employing a Support Vector Machine (SVM) classification model. this research presents novel contributions to enhance diagnostic accuracy and reliability. Our approach incorporates a comprehensive review of EEG signal analysis techniques and machine learning methods. Drawing from recent studies, we have engineered an advanced SVM-based model optimized for Parkinson's disease diagnosis. Utilizing cutting-edge feature engineering, extensive hyperparameter tuning, and kernel selection, our method achieves not only heightened diagnostic accuracy but also emphasizes model interpretability, catering to both clinicians and researchers. Moreover, ethical concerns in healthcare machine learning, such as data privacy and biases, are conscientiously addressed. We assess our method's performance through experiments on a diverse dataset comprising EEG recordings from Parkinson's disease patients and healthy controls, demonstrating significantly improved diagnostic accuracy compared to conventional techniques. In conclusion, this paper introduces an innovative SVM-based approach for diagnosing Parkinson's disease from human EEG signals. Building upon the IEEE framework and previous research, its novelty lies in the capacity to enhance diagnostic accuracy while upholding interpretability and ethical considerations for practical healthcare applications. These advances promise to revolutionize early Parkinson's disease detection and management, ultimately contributing to enhanced patient outcomes and quality of life.  \nKeywords—Parkinson's Disease(PD), Electroencephalogram (EEG) Signals, Machine Learning(ML), Support Vector Machine (SVM), Classification.  \nI. INTRODUCTION  \nParkinson's disease (PD) is a debilitating neurodegenerative disorder that affects millions of individuals worldwide. It is characterized by a wide range of motor and non-motor symptoms, with motor symptoms such as tremors, bradykinesia, and rigidity being THE MOST RECOGNIZABLE.Early and accurate diagnosis of PD is essential for timely medical intervention and to improve the patient's quality of life. This paper addresses the challenging task of diagnosing Parkinson's disease based on human EEG (Electroencephalography) signals by utilizing advanced machine learning methods. The significance of early diagnosis, combined with the potential of EEG signals, has  \nmotivated a growing body of research to develop accurate and efficient diagnostic systems[8] .  \nThe foundation of this work is built upon a comprehensive review of existing research encompassing 25 referenced studies, which have explored a multitude of methodologies, ranging from machine learning techniques to novel signal processing strategies.  \nThe contributions of these studies have formed the basis for our research, and we have aimed to advance the state of the art by incorporating novel elements into the diagnostic process[5] .  \nMachine learning, particularly within the domain ofEEG signal analysis, has shown remarkable promise in the diagnosis of Parkinson's disease. The potential to uncover patterns, features, and biomarkers in EEG signals associated with PD has attracted researc","cbCailgDFwiJ5Peh","https://ap.wps.com/l/cbCailgDFwiJ5Peh","pdf",372710,1,9,"English","en",105,"# Introduction\n## EEG-based machine learning for Parkinson's diagnosis\n## Related methods and ethical considerations","[{\"question\":\"What is the main goal of the proposed study?\",\"answer\":\"To diagnose Parkinson's disease using human EEG signals with a machine learning approach based on an SVM classification model, improving accuracy while maintaining interpretability and ethical considerations.\"},{\"question\":\"How does the method improve SVM performance for EEG-based diagnosis?\",\"answer\":\"It uses feature engineering, extensive hyperparameter tuning, and kernel selection to enhance diagnostic accuracy and reliability.\"},{\"question\":\"How is the model evaluated and what topics beyond accuracy are considered?\",\"answer\":\"Experiments are conducted on EEG data from Parkinson's patients and healthy controls, and the study also addresses model interpretability and healthcare ethics such as data privacy and bias.\"}]","Diagnosis of Parkinson's Disease Using EEG Signals and Machine Learning Techniques - A Comprehensive Study | PDF",1785820325,23,{"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},"diagnosis-of-parkinsons-disease-using-eeg-signals-and-machine-learning-techniques-a-comprehensive-study","",{"@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/diagnosis-of-parkinsons-disease-using-eeg-signals-and-machine-learning-techniques-a-comprehensive-study/124100/",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","2026-08-04",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 is the main goal of the proposed study?","Question",{"text":75,"@type":76},"To diagnose Parkinson's disease using human EEG signals with a machine learning approach based on an SVM classification model, improving accuracy while maintaining interpretability and ethical considerations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the method improve SVM performance for EEG-based diagnosis?",{"text":80,"@type":76},"It uses feature engineering, extensive hyperparameter tuning, and kernel selection to enhance diagnostic accuracy and reliability.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the model evaluated and what topics beyond accuracy are considered?",{"text":84,"@type":76},"Experiments are conducted on EEG data from Parkinson's patients and healthy controls, and the study also addresses model interpretability and healthcare ethics such as data privacy and bias.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"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"]