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首页> 外文期刊>IEEE transactions on neural systems and rehabilitation engineering >A Virtual-Reality System Integrated With Neuro-Behavior Sensing for Attention-Deficit/Hyperactivity Disorder Intelligent Assessment
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A Virtual-Reality System Integrated With Neuro-Behavior Sensing for Attention-Deficit/Hyperactivity Disorder Intelligent Assessment

机译：集成了神经行为传感的虚拟现实系统，用于关注缺陷/多动障碍智能评估

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摘要

Attention-deficit/Hyperactivity disorder(ADHD) is a common neurodevelopmental disorder among children. Traditional assessment methods generally rely on behavioral rating scales (BRS) performed by clinicians, and sometimes parents or teachers. However, BRS assessment is time consuming, and the subjective ratings may lead to bias for the evaluation. Therefore, the major purpose of this study was to develop a Virtual Reality (VR) classroom associated with an intelligent assessment model to assist clinicians for the diagnosis of ADHD. In this study, an immersive VR classroom embedded with sustained and selective attention tasks was developed in which visual, audio, and visual-audio hybrid distractions, were triggered while attention tasks were conducted. A clinical experiment with 37 ADHD and 31 healthy subjects was performed. Data from BRS was compared with VR task performance and analyzed by rank-sum tests and Pearson Correlation. Results showed that 23 features out of total 28 were related to distinguish the ADHD and non-ADHD children. Several features of task performance and neuro-behavioral measurements were also correlated with features of the BRSs. Additionally, the machine learning models incorporating task performance and neuro-behavior were used to classify ADHD and non-ADHD children. The mean accuracy for the repeated cross-validation reached to 83.2%, which demonstrated a great potential for our system to provide more help for clinicians on assessment of ADHD.
机译：注意力缺陷/多动障碍（ADHD）是儿童中常见的神经发育障碍。传统评估方法通常依赖于临床医生进行的行为评级秤（BRS），有时是父母或教师。但是，BRS评估是耗时的，主观评级可能导致评估的偏差。因此，本研究的主要目的是开发与智能评估模型相关的虚拟现实（VR）课堂，以帮助临床医生诊断ADHD。在这项研究中，开发了一种嵌入具有持续和选择性关注任务的沉浸式VR教室，其中在进行注意任务时触发视觉，音频和视觉音频混合分散注意力。进行了37例ADHD和31个健康受试者的临床实验。将BRS的数据与VR任务性能进行比较，并通过秩和测试和Pearson相关分析。结果表明，23种总共28个功能与区分ADHD和非ADHD儿童有关。任务性能和神经行为测量的若干特征也与BRSS的特征相关。此外，包含任务性能和神经行为的机器学习模型用于分类ADHD和非ADHD儿童。反复交叉验证的平均准确性达到83.2％，这对我们的系统提供了对临床医生提供更多帮助的巨大潜力，以评估ADHD。

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