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Classification of attention deficit/hyperactivity disorder based on EEG signals using a EEG-Transformer model ∗

脑电图 计算机科学 变压器 人工智能 卷积神经网络 注意缺陷多动障碍 机器学习 模式识别(心理学) 心理学 工程类 神经科学 精神科 电压 电气工程
作者
Yuchao He,Xin Wang,Zijian Győző Yang,Lingbin Xue,Yuming Chen,Junyu Ji,Feng Wan,Subhas Chandra Mukhopadhyay,Lina Men,Michael C. F. Tong,Guanglin Li,Shixiong Chen
出处
期刊:Journal of Neural Engineering [IOP Publishing]
卷期号:20 (5): 056013-056013 被引量:9
标识
DOI:10.1088/1741-2552/acf7f5
摘要

Abstract 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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