用脑电图和深度学习提升多动症诊断准确率
An ADHD Diagnostic Interface Based on EEG Spectrograms and Deep Learning Techniques
- 将脑电信号转为谱图，用ResNet-18识别多动症特征
- 模型F1分数达0.9，精准定位前额极区等关键脑区
- 开发三部分数字系统，适合学校早期筛查
本文提出一种基于脑电图（EEG）谱图与深度学习的注意力缺陷多动障碍（ADHD）诊断新方法，旨在克服传统行为评估易误诊和性别偏见的问题。研究使用公开的EEG数据集，将信号转换为谱图，采用ResNet-18卷积神经网络提取特征进行分类，模型在测试中达到0.9的总体F1分数，表现出高精度与召回率。特征分析揭示了与多动症相关的显著脑区，包括前额极叶、顶叶和枕叶。基于这些发现，构建了一个包含三个模块的数字化诊断系统，可实现低成本、高可及性的校园筛查，有助于尽早识别有风险学生并提供及时支持，改善其发展结果。该研究展示了脑电分析与深度学习结合在提升多动症诊断中的潜力，为传统方法提供了可行替代方案。
原文摘要 · Abstract (English)
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.
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