[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121526-en":3,"doc-seo-121526-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},121526,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",7,"Healthcare","Interpretable machine learning for the diagnosis of hyperkinetic movement disorders","This work presents an interpretable machine learning method for differentiating hyperkinetic movement disorders using accelerometric sensor data. Essential tremor and cortical myoclonus are addressed as a concrete diagnostic example. Generalized Matrix Relevance Learning Vector Quantization (GMLVQ) is applied directly to power spectra from eight sensors capturing upper body movements, yielding excellent validation performance. The approach also explains phenotype-relevant patterns and highlights the frequency ranges that drive decisions. Explanatory power improves further by integrating information across multiple tasks per subject.","University of Groningen  \nInterpretable machine learning for the diagnosis of hyperkinetic movement disorders  \nvan den Brandhof, Elina; Elting, Jan Willem J. ; Tuitert, Inge; Dalenberg, Jelle; van der  \nStouwe, Madelein; de Koning-Tijssen, Marina. A. J. ; Biehl, Michael Published in:  \nEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning  \nDOI:  \n10.14428/esann/2025.ES2025-73  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2025  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nvan den Brandhof, E. , Elting, J. W. J. , Tuitert, I. , Dalenberg, J. , van der Stouwe, M. , de Koning-Tijssen, M.  \nA. J. , & Biehl, M. (2025) . Interpretable machine learning for the diagnosis of hyperkinetic movement disorders. In M. Verleysen (Ed.), European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN 2025 (pp. 621-626) . [Ciaco-i6doc.com](Ciaco-i6doc.com).  \n[https://doi.org/10.14428/esann/2025.ES2025-73](https://doi.org/10.14428/esann/2025.ES2025-73)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 30-12-2025  \nInterpretable machine learning for the diagnosis of hyperkinetic movement disorders  \nElina L. van den Brandhof1 ,2 , Jan W.J. Elting2 , Inge Tuitert2 , Jelle R. Dalenberg2 , A.M. Madelein van der Stouwe2 , Marina A.J. Tijssen2 and Michael Biehl1 ,3  \n1-Univ. of Groningen-Bernoulli Institute for Mathematics, Computer Science and Arti􀀌cial Intelligence, Groningen, NL  \n2-Univ. Medical Center Groningen (UMCG),(a) Dept. of Neurology,(b) Expertise Center Movement Disorders Groningen, Groningen, NL  \n3-Dept. of Metabolism and Systems Science, College of Medicine and Health, Univ. of Birmingham, UK  \nAbstract. We present a machine learning approach to the challenging di􀀋erentiation of hyperkinetic movement disorders, based on accelerometric sensor data. We address the diagnosis of essential tremor and cortical myoclonus as a speci􀀌c example. Generalized Matrix Relevance Learning Vector Quantization (GMLVQ) systems are applied directly to power spectra obtained from eight sensors recording upper body movements. We  \n􀀌nd excellent validation performance of the classi􀀌ers. Moreover, GMLVQ provides insight into the characteristic patterns of the phenotypes and the importance of particular frequency ranges in the spectra. We demonstrate that the explanatory power of the classi􀀌er is further enhanced when integrating information from several tasks per subject.  \n1 Introduction  \nHyperkinetic movement disorders cause involu","cbCaieqsIOF8BKiE","https://ap.wps.com/l/cbCaieqsIOF8BKiE","pdf",1800725,1,"English","en",105,"# Abstract\n# 1 Introduction\n## Hyperkinetic movement disorders and diagnostic challenges\n## Prototype-based relevance learning on accelerometry spectra","[{\"question\":\"What diagnostic problem does the document address?\",\"answer\":\"It targets the challenging differentiation of hyperkinetic movement disorders, demonstrated through essential tremor versus cortical myoclonus.\"},{\"question\":\"Which data and method are used for classification?\",\"answer\":\"The approach uses accelerometric sensor recordings and applies GMLVQ directly to power spectra from eight upper-body sensors.\"},{\"question\":\"How does the model provide interpretability?\",\"answer\":\"GMLVQ yields insights into characteristic phenotype patterns and identifies the importance of specific frequency ranges in the spectra.\"}]","Interpretable machine learning for the diagnosis of hyperkinetic movement disorders | 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