Rostami Mohammad, Farashi Sajjad, Khosrowabadi Reza, Pouretemad Hamidreza
Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran.
Basic Clin Neurosci. 2020 May-Jun;11(3):359-367. doi: 10.32598/bcn.9.10.115. Epub 2020 May 1.
Attention-Deficit/Hyperactivity Disorder (ADHD) is a well-known neurodevelopmental disorder. Diagnosis and treatment of ADHD can often lead to a developmental trajectory toward positive results. The present study aimed at implementing the decision tree method to recognize children with and without ADHD, as well as ADHD subtypes.
In the present study, the subjects included 61 children with ADHD (subdivided into ADHD-I (n=25), ADHD-H (n=14), and ADHD-C (n=22) groups) and 43 typically developing controls matched by IQ and age. The Child Behavior Checklist (CBCL), Integrated Visual And Auditory (IVA) test, and quantitative EEG during eyes-closed resting-state were utilized to evaluate the level of behavioral, neuropsychology, and electrophysiology markers using a decision tree algorithm, respectively.
Based on the results, excellent classification accuracy (100%) was obtained to discriminate children with ADHD from the control group. Also, the ADHD subtypes, including combined, inattention, and hyperactive/impulsive subtypes were recognized from others with an accuracy of 80.41%, 84.17%, and 71.46%, respectively.
Our results showed that children with ADHD can be recognized from the healthy controls based on the neuropsychological data (sensory-motor parameters of IVA). Also, subtypes of ADHD can be distinguished from each other using behavioral, neuropsychiatric and electrophysiological parameters. The findings suggested that the decision tree method may present an efficient and accurate diagnostic tool for the clinicians.
注意力缺陷多动障碍（ADHD）是一种广为人知的神经发育障碍。ADHD的诊断和治疗通常会导向积极的发展轨迹。本研究旨在运用决策树方法识别患有和未患有ADHD的儿童以及ADHD的亚型。
在本研究中，受试者包括61名患有ADHD的儿童（细分为注意缺陷型ADHD-I组（n = 25）、多动冲动型ADHD-H组（n = 14）和混合型ADHD-C组（n = 22））以及43名在智商和年龄上匹配的发育正常的对照组儿童。分别使用儿童行为量表（CBCL）、整合视听连续执行测试（IVA）以及闭眼静息状态下的定量脑电图，通过决策树算法评估行为、神经心理学和电生理指标的水平。
基于研究结果，在区分患有ADHD的儿童和对照组儿童时获得了极高的分类准确率（100%）。此外，还识别出了ADHD的亚型，包括混合型、注意缺陷型和多动冲动型，其准确率分别为80.41%、84.17%和71.46%。
我们的结果表明，基于神经心理学数据（IVA的感觉运动参数）可以将患有ADHD的儿童与健康对照组区分开来。此外，还可以使用行为、神经精神和电生理参数区分ADHD的不同亚型。研究结果表明，决策树方法可能为临床医生提供一种高效且准确的诊断工具。
