[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125585-en":3,"doc-seo-125585-105":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},125585,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures","Early diagnosis and grading of social anxiety disorder (SAD) provide critical clinical support for tailoring treatment and monitoring symptom progression. This study proposes a machine-learning approach that classifies SAD severity by extracting patterns of brain information flow represented as graphical networks. Directed information flow is quantified with partial directed coherence (PDC) and graph-theory measures across delta, theta, alpha, and beta bands from resting-state EEG.","TYPE Original Research PUBLISHED 09 May 2023  \nDOI 10. 3389/fpsyt.2023.1155812  \nOPEN ACCESS  \nEDITED BY  \nAamir Malik,  \nBrno University of Technology, Czechia  \nREVIEWED BY  \nSadia Shakil,  \nBrno University of Technology, Czechia Faruque Reza,  \nUniversiti Sains Malaysia Health Campus, Malaysia  \n*CORRESPONDENCE  \nNidal Kamel  \n [nidal.k@vinuni.edu.vn](nidal.k@vinuni.edu.vn)[ ](nidal.k@vinuni.edu.vn)[Amal A. Al-Shargabi](Amal A. Al-Shargabi)  \n [alshargabi@qu.edu.sa](alshargabi@qu.edu.sa)  \nRECEIVED 31 January 2023  \nACCEPTED 17 April 2023  \nPUBLISHED 09 May 2023  \nCITATION  \nAl-Ezzi A, Kamel N, Al-Shargabi AA,  \nAl-Shargie F, Al-Shargabi A, Yahya N and Al-Hiyali MI (2023) Machine learning for the detection of social anxiety disorder using e􀀀ective connectivity and graph theory measures. Front. Psychiatry 14:1155812 .  \ndoi: 10.3389/fpsyt.2023.1155812  \nCOPYRIGHT  \n© 2023 Al-Ezzi, Kamel, Al-Shargabi, Al-Shargie, Al-Shargabi, Yahya and Al-Hiyali. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning for the detection of social anxiety disorder using e􀀀ective connectivity and graph theory measures  \nAbdulhakim Al-Ezzi1 , Nidal Kamel  2*, Amal A. Al-Shargabi3* , Fares Al-Shargie4 , Alaa Al-Shargabi  5 , Norashikin Yahya1 and Mohammed Isam Al-Hiyali1  \n1 Centre for Intelligent Signal & Imaging Research (CISIR), Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia, 2 College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam, 3 Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia, 4 Faculty of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates, 5 Department of Information Technology, Universiti Teknlogi Malaysia, Skudai, Malaysia  \nIntroduction: The early diagnosis and classification of social anxiety disorder (SAD) are crucial clinical support tasks for medical practitioners in designing patient treatment programs to better supervise the progression and development of SAD. This paper proposes an e􀀀ective method to classify the severity of SAD into di􀀀erent grading (severe, moderate, mild, and control) by using the patterns of brain information flow with their corresponding graphical networks.  \nMethods: We quantified the directed information flow using partial directed coherence (PDC) and the topological networks by graph theory measures at four frequency bands (delta, theta, alpha, and beta) . The PDC assesses the causal interactions between neuronal units of the brain network. Besides, the graph theory of the complex network identifies the topological structure of the network. Resting-state electroencephalogram (EEG) data were recorded for 66 patients with di􀀀erent severities of SAD (22 severe, 22 moderate, and 22 mild) and 22 demographically matched healthy controls (HC) .  \nResults: PDC results have found significant di􀀀erences between SAD groups and HCs in theta and alpha frequency bands (p \u003C 0.05) . Severe and moderate SAD groups have shown greater enhanced information flow than mild and HC groups in all frequency bands. Furthermore, the PDC and graph theory features have been used to discriminate three classes of SAD from HCs using several machine learning classifiers. In comparison to the features obtained by PDC, graph theory network features combined with PDC have achieved maximum classification performance with accuracy (92 . 78%), sensitivity (95 . 25%), and specificity (94 . 12%) using Support Vector Machine (SVM) .  \nDiscussion: Based on the results, it can be concluded that","cbCaiblvsx9L4Cjy","https://ap.wps.com/l/cbCaiblvsx9L4Cjy","pdf",6459710,1,20,"English","en",105,"# Introduction\n# Methods\n# Results\n# Discussion","[{\"question\":\"How does the study detect and grade social anxiety disorder (SAD)?\",\"answer\":\"It uses machine-learning classification based on directed brain information flow patterns captured by partial directed coherence (PDC) and graph-theory measures derived from resting-state EEG.\"},{\"question\":\"What data and features are used in the analysis?\",\"answer\":\"Resting-state EEG data are collected from patients with severe, moderate, mild SAD and demographically matched healthy controls, and features are computed across delta, theta, alpha, and beta frequency bands using PDC and graph-theory network metrics.\"},{\"question\":\"Which frequency bands and methods show significant discrimination?\",\"answer\":\"PDC reveals significant differences between SAD groups and healthy controls in theta and alpha bands (p \\u003c 0.05), and combining graph-theory network features with PDC achieves the best classification performance using an SVM classifier.\"}]","Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures | 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does the study detect and grade social anxiety disorder (SAD)?","Question",{"text":75,"@type":76},"It uses machine-learning classification based on directed brain information flow patterns captured by partial directed coherence (PDC) and graph-theory measures derived from resting-state EEG.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and features are used in the analysis?",{"text":80,"@type":76},"Resting-state EEG data are collected from patients with severe, moderate, mild SAD and demographically matched healthy controls, and features are computed across delta, theta, alpha, and beta frequency bands using PDC and graph-theory network metrics.",{"name":82,"@type":73,"acceptedAnswer":83},"Which frequency bands and methods show significant discrimination?",{"text":84,"@type":76},"PDC reveals significant differences between SAD groups and healthy controls in theta and alpha bands (p \u003C 0.05), and combining graph-theory network features with PDC achieves the best 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