Bansal Jayoti, Gangwar Gaurav, Aljaidi Mohammad, Alkoradees Ali, Singh Gagandeep
Department of Computer Science Engineering, Baba Farid College of Engineering & Technology, Bathinda 151001, Punjab, India.
Department of Computer Science, Zarqa University, Zarqa 13110, Jordan.
Brain Sci. 2025 Jan 20;15(1):95. doi: 10.3390/brainsci15010095.
Attention-Deficit/Hyperactivity Disorder (ADHD) represents a widely prevalent and heterogeneous neurodevelopmental condition in pediatric populations, often exhibiting a substantial propensity to persist into adulthood. ADHD is a multifaceted disorder that resists straightforward diagnostic tests. Clinicians must invest substantial time and effort to secure an accurate diagnosis and implement effective treatment. ADHD diagnosis is primarily based on psychiatric tests, as there is currently no clinically utilized objective diagnostic tool. Nonetheless, several studies in have documented endeavors to create objective instruments designed to assist in the diagnostic process of ADHD, aiming to enhance diagnostic accuracy and reduce subjectivity.
This research endeavor sought to establish an objective diagnostic modality for ADHD through the utilization of electroencephalography (EEG) signal analysis. With the use of innovative deep learning techniques, this research seeks to improve the diagnosis of ADHD using EEG data. To capture complex patterns in EEG data, this study proposes a double-augmented attention mechanism ResNet-based model. Using an autoencoder for feature extraction, the Reptile Search Algorithm for feature selection, and a modified ResNet architecture for model training comprise the technique.
AUC, F1-score, accuracy, precision, recall, and other standard classifiers like Random Forest and AdaBoost were utilized to compare the model's performance. By a wide margin, the proposed ResNet model outperforms the traditional models with a 99.42% accuracy, 99.03% precision, 99.82% recall, and 99.42% F1-score.
ROC AUC score of 0.99 for the model underscores its remarkable capability to differentiate between children with and without ADHD, thereby minimizing misclassification errors and improving diagnostic precision.
注意力缺陷多动障碍（ADHD）是儿科人群中一种广泛流行且异质性的神经发育疾病，通常有很大倾向持续到成年期。ADHD是一种多方面的疾病，难以通过简单的诊断测试确诊。临床医生必须投入大量时间和精力来获得准确诊断并实施有效治疗。ADHD诊断主要基于精神科测试，因为目前尚无临床可用的客观诊断工具。尽管如此，已有多项研究记录了为创建有助于ADHD诊断过程的客观工具所做的努力，旨在提高诊断准确性并减少主观性。
本研究试图通过利用脑电图（EEG）信号分析来建立一种ADHD的客观诊断方法。通过使用创新的深度学习技术，本研究旨在利用EEG数据改进ADHD的诊断。为了捕捉EEG数据中的复杂模式，本研究提出了一种基于双增强注意力机制ResNet的模型。该技术包括使用自动编码器进行特征提取、使用爬虫搜索算法进行特征选择以及使用改进的ResNet架构进行模型训练。
使用AUC、F1分数、准确率、精确率、召回率以及随机森林和AdaBoost等其他标准分类器来比较模型性能。所提出的ResNet模型以99.42%的准确率、99.03%的精确率、99.82%的召回率和99.42%的F1分数，大幅优于传统模型。
该模型的ROC AUC分数为0.99，突出了其在区分患有和未患有ADHD的儿童方面的卓越能力，从而最大限度地减少错误分类误差并提高诊断精度。
