CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, People's Republic of China.
Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, Guangdong 518055, People's Republic of China.
J Neural Eng. 2023 Sep 21;20(5). doi: 10.1088/1741-2552/acf7f5.
. 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.. 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.. 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.. 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.
注意缺陷多动障碍（ADHD）是青少年中最常见的神经发育障碍，可严重损害人的注意力功能、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，这可能导致 ADHD 的诊断延迟，甚至误诊，因为诊断效率低且缺乏训练有素的诊断专家。对从 ADHD 患者记录的脑电图（EEG）信号进行深度学习可以提供一种客观、准确的方法，帮助医生进行临床诊断。本文提出了基于传统 Transformer 模型中的注意力机制的 EEG-Transformer 深度学习模型，可以对 EEG 信号的特征进行特征提取和信号分类处理。综合比较了所提出的变压器模型和三个现有的卷积神经网络模型。结果表明，所提出的 EEG-Transformer 模型在最快的收敛速度下，平均准确率为 95.85%，平均 AUC 值为 0.9926，优于其他三个模型。通过消融实验研究了模型中各模块的功能和关系。通过优化实验确定了性能最佳的模型。本文提出的 EEG-Transformer 模型可作为 ADHD 临床诊断的辅助工具，同时为 EEG 信号分类领域的迁移学习提供了基本模型。
