School of Science and Technology, Singapore University of Social Sciences, Singapore.
Developmental Psychiatry, Institute of Mental Health, Singapore.
Comput Methods Programs Biomed. 2021 Mar;200:105941. doi: 10.1016/j.cmpb.2021.105941. Epub 2021 Jan 14.
Attention deficit hyperactivity disorder (ADHD) is often presented with conduct disorder (CD). There is currently no objective laboratory test or diagnostic method to discern between ADHD and CD, and diagnosis is further made difficult as ADHD is a common neuro-developmental disorder often presenting with other co-morbid difficulties; and in particular with conduct disorder which has a high degree of associated behavioural challenges. A novel automated system (AS) is proposed as a convenient supplementary tool to support clinicians in their diagnostic decisions. To the best of our knowledge, we are the first group to develop an automated classification system to classify ADHD, CD and ADHD+CD classes using brain signals.
The empirical mode decomposition (EMD) and discrete wavelet transform (DWT) methods were employed to decompose the electroencephalogram (EEG) signals. Autoregressive modelling coefficients and relative wavelet energy were then computed on the signals. Various nonlinear features were extracted from the decomposed coefficients. Adaptive synthetic sampling (ADASYN) was then employed to balance the dataset. The significant features were selected using sequential forward selection method. The highly discriminatory features were subsequently fed to an array of classifiers.
The highest accuracy of 97.88% was achieved with the K-Nearest Neighbour (KNN) classifier. The proposed system was developed using ten-fold validation strategy on EEG data from 123 children. To the best of our knowledge this is the first study to develop an AS for the classification of ADHD, CD and ADHD+CD classes using EEG signals.
Our AS can potentially be used as a web-based application with cloud system to aid the clinical diagnosis of ADHD and/or CD, thus supporting faster and accurate treatment for the children. It is important to note that testing with larger data is required before the AS can be employed for clinical applications.
注意力缺陷多动障碍（ADHD）常伴有品行障碍（CD）。目前尚无客观的实验室测试或诊断方法来区分 ADHD 和 CD，并且由于 ADHD 是一种常见的神经发育障碍，常伴有其他合并症，尤其是品行障碍，具有高度相关的行为挑战，因此诊断更加困难。我们提出了一种新型自动化系统（AS），作为一种方便的辅助工具，以支持临床医生做出诊断决策。据我们所知，我们是第一组开发使用脑信号对 ADHD、CD 和 ADHD+CD 进行分类的自动化分类系统的团队。
采用经验模态分解（EMD）和离散小波变换（DWT）方法对脑电图（EEG）信号进行分解。然后计算信号的自回归模型系数和相对小波能量。从分解系数中提取各种非线性特征。然后采用自适应综合采样（ADASYN）对数据集进行平衡。使用顺序前向选择方法选择显著特征。随后将高度判别特征输入到一系列分类器中。
使用 K-最近邻（KNN）分类器实现了 97.88%的最高准确率。该系统是在对 123 名儿童的 EEG 数据进行十折验证策略的基础上开发的。据我们所知，这是第一项使用 EEG 信号开发用于 ADHD、CD 和 ADHD+CD 分类的 AS 的研究。
我们的 AS 可以作为基于网络的应用程序与云系统一起使用，以辅助 ADHD 和/或 CD 的临床诊断，从而支持更快、更准确地治疗儿童。在 AS 可用于临床应用之前，需要进行更大规模数据的测试。
