This paper introduces an innovative approach to Attention-deficit/hyperactivity disorder (ADHD) diagnosis by employing deep learning (DL) techniques on electroencephalography (EEG) signals. This method addresses the limitations of current behavior-based diagnostic methods, which often lead to misdiagnosis and gender bias. By utilizing a publicly available EEG dataset and converting the signals into spectrograms, a Resnet-18 convolutional neural network (CNN) architecture was used to extract features for ADHD classification. The model achieved a high precision, recall, and an overall F1 score of 0.9. Feature extraction highlighted significant brain regions (frontopolar, parietal, and occipital lobes) associated with ADHD. These insights guided the creation of a three-part digital diagnostic system, facilitating cost-effective and accessible ADHD screening, especially in school environments. This system enables earlier and more accurate identification of students at risk for ADHD, providing timely support to enhance their developmental outcomes. This study showcases the potential of integrating EEG analysis with DL to enhance ADHD diagnostics, presenting a viable alternative to traditional methods.

翻译：本文提出了一种创新方法，通过将深度学习技术应用于脑电图信号，以改进注意力缺陷多动障碍的诊断。该方法针对当前基于行为的诊断方法存在的误诊和性别偏见等局限性。研究利用公开可用的脑电图数据集，将信号转换为谱图，并采用Resnet-18卷积神经网络架构提取特征进行ADHD分类。该模型取得了较高的精确率、召回率，整体F1分数达到0.9。特征提取过程凸显了与ADHD相关的关键脑区（额极区、顶叶和枕叶）。这些发现指导开发了一个三部分数字诊断系统，有助于实现经济高效且易于获取的ADHD筛查，特别适用于学校环境。该系统能够更早、更准确地识别存在ADHD风险的学生，为其提供及时支持以改善发展结果。本研究展示了将脑电图分析与深度学习相结合以增强ADHD诊断的潜力，为传统方法提供了可行的替代方案。