Attention deficit/hyperactivity disorder (ADHD) is a neuropsychological disorder that occurs in children and is characterized by inattention, impulsivity, and hyperactivity. Early and accurate diagnosis of ADHD is very important for effective intervention. The aim of this study is to develop a computer-aided approach to detecting ADHD using electroencephalogram (EEG) signals. Specifically, we explore a Gabor filter-based statistical features approach for the classification of EEG signals into ADHD and healthy control (HC). The EEG signal is processed by a bank of Gabor filters to obtain narrow-band signals. Subsequently, a set of statistical features is extracted. The computed features are then subjected to feature selection. Finally, the obtained feature vector is given to a classifier to detect ADHD and HC. Our approach achieves the highest classification accuracy of 96.4% on a publicly available dataset. Furthermore, our approach demonstrates better classification accuracy than the existing methods.

中文翻译：

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基于 Gabor 滤波器的 ADHD 检测统计特征

注意力缺陷/多动障碍（ADHD）是一种发生于儿童的神经心理障碍，其特征是注意力不集中、冲动和多动。 ADHD的早期、准确诊断对于有效干预非常重要。本研究的目的是开发一种计算机辅助方法，利用脑电图 (EEG) 信号检测 ADHD。具体来说，我们探索了一种基于 Gabor 滤波器的统计特征方法，用于将 EEG 信号分类为 ADHD 和健康控制 (HC)。 EEG 信号由一组 Gabor 滤波器处理以获得窄带信号。随后，提取一组统计特征。然后对计算出的特征进行特征选择。最后，将获得的特征向量提供给分类器来检测 ADHD 和 HC。我们的方法在公开数据集上实现了 96.4% 的最高分类准确率。此外，我们的方法比现有方法表现出更好的分类准确性。