[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123551-en":3,"doc-seo-123551-105":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},123551,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",7,"Healthcare","Auto-Detection of Hypsarrhythmia EEG in West Syndrome - Dedicated Feature Fusion and Machine Learning","West syndrome (WS) is a neurodevelopmental disorder marked by developmental delay, hypsarrhythmia EEG patterns, and motor spasms. Long-term visual review of hypsarrhythmia is time-consuming and can be unreliable. This study develops automated hypsarrhythmia diagnosis using machine learning with dedicated feature selection. EEG segments from 101 WS patients and 155 healthy controls are analyzed via amplitude, spectrum, entropy, and correlation features. Among four classifiers, AdaBoost achieves the best overall performance, and selected features improve discriminative power, supporting clinical deployment of automated WS diagnosis.","Auto-Detection of Hypsarrhythmia EEG in West Syndrome by Dedicated Feature Fusion and Machine Learning  \nCitation for published version (APA):  \nYan, Y. , Wu, Q. , He, W. , Guo, Q. , Hou, R. , Su, R. , Tan, T. , Wang, X. , Li, Y. , He, D. , & Xu, L. (2025) . AutoDetection of Hypsarrhythmia EEG in West Syndrome by Dedicated Feature Fusion and Machine Learning. IEEE Sensors Journal, 25(13), 24863-24872 . [https://doi.org/10.1109/JSEN.2025.3568867](https://doi.org/10.1109/JSEN.2025.3568867)  \nDocument license:  \nTAVERNE  \nDOI:  \n10.1109/JSEN.2025.3568867  \nDocument status and date:  \nPublished: 01/07/2025  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. 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Apr. 2026  \nIEEE SENSORS JOURNAL, VOL. 25, NO. 13, 1 JULY 2025 24863  \nAuto-Detection of Hypsarrhythmia EEG in West Syndrome by Dedicated Feature Fusion and Machine Learning  \nYumei Yan, Qinman Wu, Wenyuan He, Qiongru Guo, Ruolin Hou, Ruisheng Su, Tao Tan , Xiaoqiang Wang, Yuanning Li , Member, IEEE, Dake He, and Lin Xu , Senior Member, IEEE  \nAbstract—West syndrome (WS) is a neurodevelopmental disorder causing retardation in many patients. Hypsarrhythmia electroencephalography (EEG) and motor spasms are considered as clinical manifestations of WS. Visual inspection of hypsarrhythmia in long-term EEG recordings is time-consuming and unreliable. This study investigates automated hypsarrhythmia diagnosis using machine learning and dedicated feature selection. The 101 WS patients and 155 healthy controls (HCs) were involved. The 15-s representative hypsarrhythmia and nonhypsarrhythmia EEG segments were selected from each WS patient, and a normal  \nEEG segment with the same length was picked from each HC. Amplitude-, spectrum-, entropy-, and correlation-related features were extracted from each EEG segment. Four popular classifiers, i.e., logistic regression (LR), support vector machine (SVM), adaptive boosting (AdaBoost), and K-nearest neighbors (KNNs), were employed to perform three-label classification among hypsarrhythmia, nonhypsarrhythmia, and HC. Dedicated feature selection was implemented to identify an optimal feature subset for effective classification. Accuracy (ACC), sensitivity (SN), specificity (SP), and ","cbCaijwYRgKBW7UY","https://ap.wps.com/l/cbCaijwYRgKBW7UY","pdf",2728709,1,11,"English","en",105,"# Abstract\n# Introduction\n## Clinical features of West syndrome\n## Hypsarrhythmia EEG and the motivation for automation","[{\"question\":\"Why is automated hypsarrhythmia detection needed for West syndrome?\",\"answer\":\"Manual visual inspection of hypsarrhythmia in long-term EEG is time-consuming and unreliable, which motivates automated diagnosis methods.\"},{\"question\":\"What data and EEG segments were used in the study?\",\"answer\":\"The study used EEG segments selected from 101 WS patients and 155 healthy controls, including 15-s representative hypsarrhythmia and nonhypsarrhythmia segments for each patient and matching-length normal segments for each control.\"},{\"question\":\"Which modeling approach performed best and how was it evaluated?\",\"answer\":\"AdaBoost produced the best results across most metrics (accuracy, sensitivity, specificity, and AUC) using performance measures including ACC, SN, SP, and AUC.\"}]","Auto-Detection of Hypsarrhythmia EEG in West Syndrome - Dedicated Feature Fusion and Machine Learning | PDF",1785817268,28,{"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},"auto-detection-of-hypsarrhythmia-eeg-in-west-syndrome-dedicated-feature-fusion-and-machine-learning","",{"@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/auto-detection-of-hypsarrhythmia-eeg-in-west-syndrome-dedicated-feature-fusion-and-machine-learning/123551/",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},"Why is automated hypsarrhythmia detection needed for West syndrome?","Question",{"text":75,"@type":76},"Manual visual inspection of hypsarrhythmia in long-term EEG is time-consuming and unreliable, which motivates automated diagnosis methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and EEG segments were used in the study?",{"text":80,"@type":76},"The study used EEG segments selected from 101 WS patients and 155 healthy controls, including 15-s representative hypsarrhythmia and nonhypsarrhythmia segments for each patient and matching-length normal segments for each control.",{"name":82,"@type":73,"acceptedAnswer":83},"Which modeling approach performed best and how was it evaluated?",{"text":84,"@type":76},"AdaBoost produced the best results across most metrics (accuracy, sensitivity, specificity, and AUC) using performance measures including ACC, SN, SP, and AUC.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":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":121,"slug":122},8,"Research & Report",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"]