基于脑电识别注意缺陷/多动障碍的混合深度学习模型。

IF 1.6 4区 医学 Q3 CLINICAL NEUROLOGY Clinical EEG and Neuroscience Pub Date : 2024-01-01 Epub Date: 2023-09-08 DOI:10.1177/15500594231193511
Nupur Chugh, Swati Aggarwal, Arnav Balyan
{"title":"基于脑电识别注意缺陷/多动障碍的混合深度学习模型。","authors":"Nupur Chugh, Swati Aggarwal, Arnav Balyan","doi":"10.1177/15500594231193511","DOIUrl":null,"url":null,"abstract":"<p><p>Common misbehavior among children that prevents them from paying attention to tasks and interacting with their surroundings appropriately is attention-deficit/hyperactivity disorder (ADHD). Studies of children's behavior presently face a significant problem in the early and timely diagnosis of this disease. To diagnose this disease, doctors often use the patient's description and questionnaires, psychological tests, and the patient's behavior in which reliability is questionable. Convolutional neural network (CNN) is one deep learning technique that has been used for the diagnosis of ADHD. CNN, however, does not account for how signals change over time, which leads to low classification performances and ambiguous findings. In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. Hence, the proposed hybrid CNN-LSTM model could therefore be utilized to help with the clinical diagnosis of ADHD patients.</p>","PeriodicalId":10682,"journal":{"name":"Clinical EEG and Neuroscience","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG.\",\"authors\":\"Nupur Chugh, Swati Aggarwal, Arnav Balyan\",\"doi\":\"10.1177/15500594231193511\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Common misbehavior among children that prevents them from paying attention to tasks and interacting with their surroundings appropriately is attention-deficit/hyperactivity disorder (ADHD). Studies of children's behavior presently face a significant problem in the early and timely diagnosis of this disease. To diagnose this disease, doctors often use the patient's description and questionnaires, psychological tests, and the patient's behavior in which reliability is questionable. Convolutional neural network (CNN) is one deep learning technique that has been used for the diagnosis of ADHD. CNN, however, does not account for how signals change over time, which leads to low classification performances and ambiguous findings. In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. Hence, the proposed hybrid CNN-LSTM model could therefore be utilized to help with the clinical diagnosis of ADHD patients.</p>\",\"PeriodicalId\":10682,\"journal\":{\"name\":\"Clinical EEG and Neuroscience\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical EEG and Neuroscience\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/15500594231193511\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/9/8 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical EEG and Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/15500594231193511","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/9/8 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0

摘要

儿童中常见的不良行为是注意力缺陷/多动障碍(ADHD),它使他们无法集中注意力完成任务并与周围环境进行适当的互动。儿童行为的研究目前面临着早期和及时诊断该疾病的重大问题。为了诊断这种疾病,医生经常使用病人的描述和问卷,心理测试,以及病人的行为,其中可靠性值得怀疑。卷积神经网络(CNN)是一种深度学习技术,已被用于ADHD的诊断。然而,CNN没有考虑信号如何随时间变化,这导致分类性能低,结果模棱两可。在本研究中,作者设计了一种长短期记忆(LSTM)和CNN相结合的混合深度学习模型,同时提取和学习脑电图(EEG)数据的空间特征和长期依赖关系。使用2个公开的EEG数据集评估了所提出的混合深度学习模型的有效性。该模型在ADHD数据集和FOCUS数据集上的分类准确率分别为98.86%和98.28%。实验结果表明,本文提出的CNN-LSTM混合模型优于目前最先进的脑电图诊断方法。因此,本文提出的CNN-LSTM混合模型可用于ADHD患者的临床诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
The Hybrid Deep Learning Model for Identification of Attention-Deficit/Hyperactivity Disorder Using EEG.

Common misbehavior among children that prevents them from paying attention to tasks and interacting with their surroundings appropriately is attention-deficit/hyperactivity disorder (ADHD). Studies of children's behavior presently face a significant problem in the early and timely diagnosis of this disease. To diagnose this disease, doctors often use the patient's description and questionnaires, psychological tests, and the patient's behavior in which reliability is questionable. Convolutional neural network (CNN) is one deep learning technique that has been used for the diagnosis of ADHD. CNN, however, does not account for how signals change over time, which leads to low classification performances and ambiguous findings. In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. Hence, the proposed hybrid CNN-LSTM model could therefore be utilized to help with the clinical diagnosis of ADHD patients.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Clinical EEG and Neuroscience
Clinical EEG and Neuroscience 医学-临床神经学
CiteScore
5.20
自引率
5.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Clinical EEG and Neuroscience conveys clinically relevant research and development in electroencephalography and neuroscience. Original articles on any aspect of clinical neurophysiology or related work in allied fields are invited for publication.
期刊最新文献
Ikelos-RWA. Validation of an Automatic Tool to Quantify REM Sleep Without Atonia. Age-dependent Electroencephalogram Characteristics During Different Levels of Anesthetic Depth. The Clinical Utility of Finding Unexpected Subclinical Spikes Detected by High-Density EEG During Neurodiagnostic Investigations Comparative Analysis of LORETA Z Score Neurofeedback and Cognitive Rehabilitation on Quality of Life and Response Inhibition in Individuals with Opioid Addiction Deep Learning-Based Artificial Intelligence Can Differentiate Treatment-Resistant and Responsive Depression Cases with High Accuracy
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1