Fusion of Multi-Task Neurophysiological Data to Enhance the Detection of Attention- Deficit/Hyperactivity Disorder

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL
Kai-Feng Zhang;Shih-Ching Yeh;Eric Hsiao-Kuang Wu;Xiu Xu;Ho-Jung Tsai;Chun-Chuan Chen
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Abstract

Objective: Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder with a prevalence ranging from 6.1 to 9.4%. The main symptoms of ADHD are inattention, hyperactivity, impulsivity, and even destructive behaviors that may have a long-term negative influence on learning performance or social relationships. Early diagnosis and treatment provide the best chance of reducing and managing symptoms. Currently, ADHD diagnosis relies on behavioral observations and ratings by clinicians and parents. Medical diagnosis of ADHD was reported to be delayed because of a global shortage of well-trained clinicians, the heterogeneous nature of ADHD, and combined comorbidities. Therefore, alternative ways to increase the efficiency of early diagnosis are needed. Previous studies used behavioral and neurophysiological data to assess patients with ADHD, yielding an accuracy range from 56.6% to 92%. Several factors were shown to affect the detection rate, including methods and tasks used and the number of electroencephalogram (EEG) channels. Given that children with ADHD have difficulty sustaining attention, in this study, we tested whether data from multiple tasks with different difficulties and prolonged experiment times can probe the levels of brain resources engaged during task performance and increase ADHD detection. Specifically, we proposed a Deep Neural Network-based (DNN) fusion model of multiple tasks to enhance the detection of ADHD. Methods & Results: Forty-nine children with ADHD and thirty-two typically developing children were recruited. Analytic results show that the fusion of multi-task neurophysiological data can increase the separation rate to 89%, whereas a single data type can only achieve a best accuracy of 81%. Moreover, the use of multiple tasks helps distinguish between children with ADHD and typically developing children. Our results suggest that different neurophysiological models from multiple tasks can provide essential information to assist in ADHD screening. In conclusion, the proposed model offers a more efficient, and accurate alternative for early clinical diagnosis and management of ADHD. The application of artificial intelligence and multimodal neurophysiological data in clinical settings sets a precedent for digital health, paving the way for future advancements in the field.
融合多任务神经生理学数据,提高注意力缺陷/多动症的检测能力
目的:注意力缺陷/多动障碍(ADHD)是一种儿童期发病的神经发育障碍,发病率为 6.1% 至 9.4%。多动症的主要症状是注意力不集中、多动、冲动,甚至可能对学习成绩或社会关系产生长期负面影响的破坏性行为。早期诊断和治疗为减轻和控制症状提供了最佳机会。目前,多动症的诊断主要依靠临床医生和家长的行为观察和评分。据报道,由于全球缺乏训练有素的临床医生、ADHD 的异质性以及合并症等原因,ADHD 的医学诊断被延迟。因此,需要其他方法来提高早期诊断的效率。以往的研究使用行为学和神经生理学数据对多动症患者进行评估,准确率在 56.6% 到 92% 之间。研究表明,有几个因素会影响检测率,包括使用的方法和任务以及脑电图(EEG)通道的数量。鉴于多动症儿童很难持续保持注意力,在本研究中,我们测试了来自不同难度和延长实验时间的多个任务的数据是否能探查任务执行过程中大脑资源的参与程度,并提高多动症的检测率。具体来说,我们提出了一种基于深度神经网络(DNN)的多任务融合模型,以提高多动症的检测能力。方法与结果:我们招募了 49 名患有多动症的儿童和 32 名发育正常的儿童。分析结果表明,融合多任务神经生理学数据可将分离率提高到 89%,而单一数据类型只能达到 81% 的最佳准确率。此外,多任务的使用有助于区分多动症儿童和发育正常的儿童。我们的研究结果表明,来自多个任务的不同神经生理学模型可以为多动症筛查提供重要的辅助信息。总之,所提出的模型为多动症的早期临床诊断和管理提供了一种更有效、更准确的替代方法。人工智能和多模态神经生理学数据在临床中的应用开创了数字医疗的先河,为该领域未来的发展铺平了道路。
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来源期刊
CiteScore
7.40
自引率
2.90%
发文量
65
审稿时长
27 weeks
期刊介绍: The IEEE Journal of Translational Engineering in Health and Medicine is an open access product that bridges the engineering and clinical worlds, focusing on detailed descriptions of advanced technical solutions to a clinical need along with clinical results and healthcare relevance. The journal provides a platform for state-of-the-art technology directions in the interdisciplinary field of biomedical engineering, embracing engineering, life sciences and medicine. A unique aspect of the journal is its ability to foster a collaboration between physicians and engineers for presenting broad and compelling real world technological and engineering solutions that can be implemented in the interest of improving quality of patient care and treatment outcomes, thereby reducing costs and improving efficiency. The journal provides an active forum for clinical research and relevant state-of the-art technology for members of all the IEEE societies that have an interest in biomedical engineering as well as reaching out directly to physicians and the medical community through the American Medical Association (AMA) and other clinical societies. The scope of the journal includes, but is not limited, to topics on: Medical devices, healthcare delivery systems, global healthcare initiatives, and ICT based services; Technological relevance to healthcare cost reduction; Technology affecting healthcare management, decision-making, and policy; Advanced technical work that is applied to solving specific clinical needs.
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