使用EEG Transformer模型基于EEG信号对注意力缺陷/多动障碍进行分类*。

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Journal of neural engineering Pub Date : 2023-09-21 DOI:10.1088/1741-2552/acf7f5
Yuchao He, Xin Wang, Zijian Yang, Lingbin Xue, Yuming Chen, Junyu Ji, Feng Wan, Subhas Chandra Mukhopadhyay, Lina Men, Chi Fai Michael Tong, Guanglin Li, Shixiong Chen
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引用次数: 0

摘要

客观的注意力缺陷/多动障碍(ADHD)是青少年最常见的神经发育障碍,会严重损害一个人的注意力功能、认知过程和学习能力。目前,临床医生主要根据《精神障碍诊断与统计手册》-5的主观评估来诊断患者,由于诊断效率低和缺乏训练有素的诊断专家,这可能导致多动症的诊断延迟,甚至误诊。对ADHD患者脑电图(EEG)信号的深度学习可以为医生的临床诊断提供一种客观准确的方法。方法本文提出了脑电变压器深度学习模型,该模型基于传统变压器模型中的注意力机制,可以对脑电信号的特征进行特征提取和信号分类处理。将所提出的变换器模型与现有的三个卷积神经网络模型进行了全面比较。主要结果。结果表明,所提出的EEG Transformer模型以最快的收敛速度实现了95.85%的平均准确率和0.9926的平均AUC值,优于其他三个模型。通过烧蚀实验研究了该模型各模块的作用及相互关系。通过优化实验确定了性能最优的模型。意义本文提出的脑电变换器模型可以作为ADHD临床诊断的辅助工具,同时为脑电信号分类领域的可转移学习提供了一个基本模型。
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Classification of attention deficit/hyperactivity disorder based on EEG signals using a EEG-Transformer model.

Objective. Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in adolescents that can seriously impair a person's attention function, cognitive processes, and learning ability. Currently, clinicians primarily diagnose patients based on the subjective assessments of the Diagnostic and Statistical Manual of Mental Disorders-5, which can lead to delayed diagnosis of ADHD and even misdiagnosis due to low diagnostic efficiency and lack of well-trained diagnostic experts. Deep learning of electroencephalogram (EEG) signals recorded from ADHD patients could provide an objective and accurate method to assist physicians in clinical diagnosis.Approach. This paper proposes the EEG-Transformer deep learning model, which is based on the attention mechanism in the traditional Transformer model, and can perform feature extraction and signal classification processing for the characteristics of EEG signals. A comprehensive comparison was made between the proposed transformer model and three existing convolutional neural network models.Main results. The results showed that the proposed EEG-Transformer model achieved an average accuracy of 95.85% and an average AUC value of 0.9926 with the fastest convergence speed, outperforming the other three models. The function and relationship of each module of the model are studied by ablation experiments. The model with optimal performance was identified by the optimization experiment.Significance. The EEG-Transformer model proposed in this paper can be used as an auxiliary tool for clinical diagnosis of ADHD, and at the same time provides a basic model for transferable learning in the field of EEG signal classification.

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来源期刊
Journal of neural engineering
Journal of neural engineering 工程技术-工程:生物医学
CiteScore
7.80
自引率
12.50%
发文量
319
审稿时长
4.2 months
期刊介绍: The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels. The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.
期刊最新文献
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