使用机器学习在基于电生理学的注意缺陷多动障碍(ADHD)分类中的区域贡献

IF 1.9 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Computation Pub Date : 2023-09-08 DOI:10.3390/computation11090180
Nishant Chauhan, Byung-Jae Choi
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引用次数: 0

摘要

注意缺陷多动障碍(ADHD)是儿童中一种常见的神经发育疾病,其特征是难以保持注意力、多动和冲动行为。尽管正在进行研究,我们仍然不能完全理解导致多动症的原因。脑电图(EEG)由于其高时间分辨率和非侵入性而成为研究adhd相关神经模式的有价值的工具。本研究旨在通过利用脑电图数据对ADHD儿童和健康对照组进行分类,从而提高诊断的准确性。我们使用了一个包含60名ADHD儿童和60名健康对照者脑电图记录的数据集。脑电图数据是在认知任务中捕获的,包括来自头皮上19个通道的信号。我们的主要目标是开发一种机器学习模型,能够使用EEG数据作为区分特征来区分ADHD受试者和对照组。我们使用了几种著名的分类器,包括支持向量机、随机森林、决策树、AdaBoost、朴素贝叶斯和线性判别分析,来识别不同的EEG模式。为了进一步提高分类精度,我们探讨了区域数据对分类结果的影响。我们根据产生脑电图数据的大脑区域(即额叶、颞叶、中央、顶叶和枕叶)排列脑电图数据,并检查它们对我们分类准确性的集体影响。值得注意的是,我们同时考虑了三个区域的组合,并发现某些组合可以提高准确性。我们的发现强调了基于脑电图的分类在区分ADHD儿童和健康对照方面的潜力。当应用于特定的区域组合时,朴素贝叶斯分类器产生了最高的准确率(84%)。此外,我们评估了基于半球特异性脑电图数据的分类性能,并发现了有希望的结果,特别是在使用右半球区域通道时。
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Regional Contribution in Electrophysiological-Based Classifications of Attention Deficit Hyperactive Disorder (ADHD) Using Machine Learning
Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition in children and is characterized by challenges in maintaining attention, hyperactivity, and impulsive behaviors. Despite ongoing research, we still do not fully understand what causes ADHD. Electroencephalography (EEG) has emerged as a valuable tool for investigating ADHD-related neural patterns due to its high temporal resolution and non-invasiveness. This study aims to contribute to diagnostic accuracy by leveraging EEG data to classify children with ADHD and healthy controls. We used a dataset containing EEG recordings from 60 children with ADHD and 60 healthy controls. The EEG data were captured during cognitive tasks and comprised signals from 19 channels across the scalp. Our primary objective was to develop a machine learning model capable of distinguishing ADHD subjects from controls using EEG data as discriminatory features. We employed several well-known classifiers, including a support vector machine, random forest, decision tree, AdaBoost, Naive Bayes, and linear discriminant analysis, to discern distinctive EEG patterns. To further enhance classification accuracy, we explored the impact of regional data on the classification outcomes. We arranged the EEG data according to the brain regions from which they were derived (namely frontal, temporal, central, parietal, and occipital) and examined their collective effects on the accuracy of our classifications. Notably, we considered combinations of three regions at a time and found that certain combinations led to enhanced accuracy. Our findings underscore the potential of EEG-based classification in distinguishing children with ADHD from healthy controls. The Naive Bayes classifier yielded the highest accuracy (84%) when applied to specific region combinations. Moreover, we evaluated the classification performance based on hemisphere-specific EEG data and found promising results, particularly when using the right hemisphere region channels.
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来源期刊
Computation
Computation Mathematics-Applied Mathematics
CiteScore
3.50
自引率
4.50%
发文量
201
审稿时长
8 weeks
期刊介绍: Computation a journal of computational science and engineering. Topics: computational biology, including, but not limited to: bioinformatics mathematical modeling, simulation and prediction of nucleic acid (DNA/RNA) and protein sequences, structure and functions mathematical modeling of pathways and genetic interactions neuroscience computation including neural modeling, brain theory and neural networks computational chemistry, including, but not limited to: new theories and methodology including their applications in molecular dynamics computation of electronic structure density functional theory designing and characterization of materials with computation method computation in engineering, including, but not limited to: new theories, methodology and the application of computational fluid dynamics (CFD) optimisation techniques and/or application of optimisation to multidisciplinary systems system identification and reduced order modelling of engineering systems parallel algorithms and high performance computing in engineering.
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