Attallah Omneya
Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 21937, Egypt.
Wearables, Biosensing and Biosignal Processing Laboratory, Arab Academy for Science, Technology and Maritime Transport, Alexandria 21937, Egypt.
Biomimetics (Basel). 2024 Mar 20;9(3):188. doi: 10.3390/biomimetics9030188.
The severe effects of attention deficit hyperactivity disorder (ADHD) among adolescents can be prevented by timely identification and prompt therapeutic intervention. Traditional diagnostic techniques are complicated and time-consuming because they are subjective-based assessments. Machine learning (ML) techniques can automate this process and prevent the limitations of manual evaluation. However, most of the ML-based models extract few features from a single domain. Furthermore, most ML-based studies have not examined the most effective electrode placement on the skull, which affects the identification process, while others have not employed feature selection approaches to reduce the feature space dimension and consequently the complexity of the training models. This study presents an ML-based tool for automatically identifying ADHD entitled "ADHD-AID". The present study uses several multi-resolution analysis techniques including variational mode decomposition, discrete wavelet transform, and empirical wavelet decomposition. ADHD-AID extracts thirty features from the time and time-frequency domains to identify ADHD, including nonlinear features, band-power features, entropy-based features, and statistical features. The present study also looks at the best EEG electrode placement for detecting ADHD. Additionally, it looks into the location combinations that have the most significant impact on identification accuracy. Additionally, it uses a variety of feature selection methods to choose those features that have the greatest influence on the diagnosis of ADHD, reducing the classification's complexity and training time. The results show that ADHD-AID has provided scores for accuracy, sensitivity, specificity, F1-score, and Mathew correlation coefficients of 0.991, 0.989, 0.992, 0.989, and 0.982, respectively, in identifying ADHD with 10-fold cross-validation. Also, the area under the curve has reached 0.9958. ADHD-AID's results are significantly higher than those of all earlier studies for the detection of ADHD in adolescents. These notable and trustworthy findings support the use of such an automated tool as a means of assistance for doctors in the prompt identification of ADHD in youngsters.
注意力缺陷多动障碍（ADHD）在青少年中的严重影响可通过及时识别和迅速的治疗干预来预防。传统的诊断技术复杂且耗时，因为它们是基于主观的评估。机器学习（ML）技术可以使这一过程自动化，并避免人工评估的局限性。然而，大多数基于ML的模型从单个领域提取的特征很少。此外，大多数基于ML的研究没有考察颅骨上最有效的电极放置位置，这会影响识别过程，而其他研究没有采用特征选择方法来减少特征空间维度，从而降低训练模型的复杂性。本研究提出了一种基于ML的自动识别ADHD的工具，名为“ADHD-AID”。本研究使用了几种多分辨率分析技术，包括变分模态分解、离散小波变换和经验小波分解。ADHD-AID从时域和时频域提取30个特征来识别ADHD，包括非线性特征、频段功率特征、基于熵的特征和统计特征。本研究还研究了用于检测ADHD的最佳脑电图电极放置位置。此外，还研究了对识别准确率影响最大的位置组合。此外，它使用多种特征选择方法来选择那些对ADHD诊断影响最大的特征，降低分类的复杂性和训练时间。结果表明，在10折交叉验证中识别ADHD时，ADHD-AID的准确率、灵敏度、特异性、F1分数和马修相关系数分别为0.991、0.989、0.992、0.989和0.982。此外，曲线下面积达到0.9958。ADHD-AID的结果显著高于之前所有关于青少年ADHD检测的研究。这些显著且可靠的发现支持使用这种自动化工具作为帮助医生快速识别青少年ADHD的一种手段。
