[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124011-en":3,"doc-seo-124011-105":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":20,"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},124011,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A Machine Learning Approach for Sex and Age Classification of Paediatric EEGs","Electroencephalography (EEG) supports the investigation of childhood seizures and brain disorders, yet expert visual review can estimate age but cannot determine a child’s sex reliably. This study examines EEGs from 351 healthy children aged 6–10 and develops machine-learning methods for simultaneous sex and age classification. The approach demonstrates potential for age determination with test-set performance reaching 66.67% accuracy under a 1-year error tolerance, while biological-sex estimation remains weak. Results suggest utility in separating developmentally normal from delayed children and motivate further work despite the limited dataset size.","| Title | A Machine Learning Approach for Sex and Age Classification of Paediatric EEGs |\n| --- | --- |\n| Authors(s) | Wei, Lan, McHugh, John C., Mooney, Catherine |\n| Publication date | 2023-07-27 |\n| Publication information | Wei, Lan, John C. McHugh, and Catherine Mooney.“A Machine Learning Approach for Sex and Age Classification of Paediatric EEGs.” IEEE, 2023. |\n| Conference details | The 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Sydney, Australia, 24-27 July 2023 |\n| Publisher | IEEE |\n| Item record/more\u003Cbr>information | [http://hdl.handle.net/10197/26093](http://hdl.handle.net/10197/26093) |\n| Publisher's statement | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |\n| Publisher's version (DOI) | 10.1109/EMBC40787.2023.10341120 |\n\nDownloaded 2024-10-19 12:32:27  \nThe UCD community has made this article openly available. Please share how this access  \nbenefits you. Your story matters! (@ucd_oa)  \n© Some rights reserved. For more information  \nA Machine Learning Approach for Sex and Age Classification of  \nPaediatric EEGs  \nLan Wei 1 , John C McHugh2 and Catherine Mooney 1  \nAbstract—Electroencephalography (EEG) is an important investigation of childhood seizures and other brain disorders. Expert visual analysis of EEGs can estimate subjects’ age based on the presence of particular maturational features. The sex of a child, however, cannot be determined by visual inspection. In this study, we explored sex and age differences in the EEGs of 351 healthy male and female children aged between 6 and 10 years. We developed machine learning-based methods to classify the sex and age of healthy children from their EEGs. This preliminary study based on small EEG numbers demonstrates the potential for machine learning in helping with age determination in healthy children. This may be useful in distinguishing developmentally normal from developmentally delayed children. The model performed poorly for estimation of biological sex. However, we achieved 66.67% accuracy in age prediction allowing a 1 year error, on the test set.  \nI. INTRODUCTION  \nThe human brain undergoes significant maturational change during childhood, which is paralleled by changes within the electroencephalogram (EEG) . Changes are particularly marked within the first year, after which more gradual evolution occurs into early childhood and onwards to adolescence. Appreciation of age-specific normative EEG patterns is clinically important as abnormalities of brain development can be associated with slowing of background activities as well as other patterns of abnormal activity.  \nMedical reporting of EEG is primarily based on visual inspection by qualified experts in Clinical Neurophysiology with a combined appreciation of age-related neurological disease and knowledge of age-related EEG patterns. Research on machine learning-based EEG feature estimation for age and sex determination is limited.  \nAn automated EEG-based sex and age classification method would be a powerful tool for brain development researchers and the research to date, although limited, has shown some good results [1] . Kaushik et al. [2] developed a deep BLSTM-LSTM network model to predict sex and age from 60 EEG recordings with an age range from 6- 55 years old, which achieved an accuracy of 93.7% forage classification and 97.5% for sex classification. Similarly, Nguyen et al. [3], [4] and Kaur et al. [5] developed methods  \n*This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the NeuroInsight Marie Skłodowska-Curie grant agreement No. 101034252. We acknowl","cbCaimMoHx6qpNwp","https://ap.wps.com/l/cbCaimMoHx6qpNwp","pdf",472123,1,5,"English","en",105,"# Introduction\n## Motivation and background\n## Related work and limitations\n# Materials and Methodology\n## CHI EEG Dataset\n## Data split and prediction strategy","[{\"question\":\"Why is EEG-based age estimation possible but sex classification difficult by visual inspection?\",\"answer\":\"Age-related maturational features can be recognized by experts when reviewing EEGs, but sex is not determinable reliably through visual inspection of EEG patterns alone.\"},{\"question\":\"How many subjects were used and what age range was studied?\",\"answer\":\"The preliminary study used EEG recordings from 351 healthy children aged between 6 and 10 years.\"},{\"question\":\"What performance was achieved for age prediction and how accurate was sex estimation?\",\"answer\":\"The model achieved 66.67% accuracy for age prediction allowing a 1-year error on the test set, but it performed poorly for estimating biological sex.\"}]","A Machine Learning Approach for Sex and Age Classification of Paediatric EEGs | 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is EEG-based age estimation possible but sex classification difficult by visual inspection?","Question",{"text":75,"@type":76},"Age-related maturational features can be recognized by experts when reviewing EEGs, but sex is not determinable reliably through visual inspection of EEG patterns alone.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How many subjects were used and what age range was studied?",{"text":80,"@type":76},"The preliminary study used EEG recordings from 351 healthy children aged between 6 and 10 years.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance was achieved for age prediction and how accurate was sex estimation?",{"text":84,"@type":76},"The model achieved 66.67% accuracy for age prediction allowing a 1-year error on the test set, but it performed poorly for estimating biological 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