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
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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。
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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.

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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
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