Maheshwari Shishir, Rajesh Kandala N V P S, Kanhangad Vivek, Acharya U Rajendra, Kumar T Sunil
Department of Electronics and Communication Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, Uttar Pradesh, India.
School of Electronics Engineering, VIT-AP University, Vijayawada, Andhra Pradesh, India.
PLoS One. 2025 Apr 3;20(4):e0319487. doi: 10.1371/journal.pone.0319487. eCollection 2025.
Attention deficit hyperactivity disorder (ADHD) is one of the common neurodevelopmental disorders in children. This paper presents an automated approach for ADHD detection using the proposed entropy difference (EnD)-based encephalogram (EEG) channel selection approach. In the proposed approach, we selected the most significant EEG channels for the accurate identification of ADHD using an EnD-based channel selection approach. Secondly, a set of features is extracted from the selected channels and fed to a classifier. To verify the effectiveness of the channels selected, we explored three sets of features and classifiers. More specifically, we explored discrete wavelet transform (DWT), empirical mode decomposition (EMD) and symmetrically-weighted local binary pattern (SLBP)-based features. To perform automated classification, we have used k-nearest neighbor (k-NN), Ensemble classifier, and support vectors machine (SVM) classifiers. Our proposed approach yielded the highest accuracy of 99.29% using the public database. In addition, the proposed EnD-based channel selection has consistently provided better classification accuracies than the entropy-based channel selection approach. Also, the developed method has outperformed the existing approaches in automated ADHD detection.
注意缺陷多动障碍（ADHD）是儿童常见的神经发育障碍之一。本文提出了一种基于熵差（EnD）的脑电图（EEG）通道选择方法的ADHD自动检测方法。在所提出的方法中，我们使用基于EnD的通道选择方法选择最显著的EEG通道，以准确识别ADHD。其次，从所选通道中提取一组特征并输入到分类器中。为了验证所选通道的有效性，我们探索了三组特征和分类器。更具体地说，我们探索了基于离散小波变换（DWT）、经验模态分解（EMD）和对称加权局部二值模式（SLBP）的特征。为了进行自动分类，我们使用了k近邻（k-NN）、集成分类器和支持向量机（SVM）分类器。我们提出的方法在使用公共数据库时获得了99.29%的最高准确率。此外，所提出的基于EnD的通道选择始终比基于熵的通道选择方法提供更好的分类准确率。而且，所开发的方法在ADHD自动检测方面优于现有方法。
