A Robust Machine Learning Based Framework for the Automated Detection of ADHD Using Pupillometric Biomarkers and Time Series Analysis.
A Robust Machine Learning Based Framework for the Automated Detection of ADHD Using Pupillometric Biomarkers and Time Series Analysis.
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DOI：
10.1038/s41598-021-95673-5
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发表时间：
2021-08-12
影响因子：
4.6
通讯作者：
Khanna S
中科院分区：
文献类型：
--
作者：
Das W;Khanna S
注意力缺陷/多动障碍（ADHD）的准确和有效检测对于确保受影响个体的适当治疗至关重要。然而，目前的临床检查效率低下，容易误诊，因为它们依赖于对感知行为的定性观察。我们提出了一个强大的基于机器学习的框架，分析瞳孔大小动态作为ADHD自动检测的客观生物标志物。我们的框架将全面的瞳孔特征工程和可视化管道与最先进的二元分类算法和单变量特征选择集成在一起。支持向量机分类器在50例患者的解密数据集上使用10倍嵌套交叉验证（CV）实现了平均85.6%的受试者工作特征下面积（AUROC），77.3%的灵敏度和75.3%的特异性。783个工程特征中的218个，包括傅立叶变换度量、绝对能量、连续分位数变化、近似熵、聚合线性趋势以及瞳孔大小扩张速度，被发现是统计学上显著的区分因素（p < 0.05），并提供了对瞳孔大小动态与ADHD存在之间的关联的新的行为见解。尽管样本量有限，但强大的AUROC值突出了二元分类器在检测ADHD方面的鲁棒性-因此，通过额外的数据，灵敏度和特异性指标可以大大增强。这项研究是第一个将基于机器学习的方法应用于仅使用瞳孔测量法检测ADHD的研究，并强调了其作为潜在的区分性生物标志物的优势，为开发新的诊断应用铺平了道路，以帮助使用视力测量范式和机器学习检测ADHD。
Accurate and efficient detection of attention-deficit/hyperactivity disorder (ADHD) is critical to ensure proper treatment for affected individuals. Current clinical examinations, however, are inefficient and prone to misdiagnosis, as they rely on qualitative observations of perceived behavior. We propose a robust machine learning based framework that analyzes pupil-size dynamics as an objective biomarker for the automated detection of ADHD. Our framework integrates a comprehensive pupillometric feature engineering and visualization pipeline with state-of-the-art binary classification algorithms and univariate feature selection. The support vector machine classifier achieved an average 85.6% area under the receiver operating characteristic (AUROC), 77.3% sensitivity, and 75.3% specificity using ten-fold nested cross-validation (CV) on a declassified dataset of 50 patients. 218 of the 783 engineered features, including fourier transform metrics, absolute energy, consecutive quantile changes, approximate entropy, aggregated linear trends, as well as pupil-size dilation velocity, were found to be statistically significant differentiators (p < 0.05), and provide novel behavioral insights into associations between pupil-size dynamics and the presence of ADHD. Despite a limited sample size, the strong AUROC values highlight the robustness of the binary classifiers in detecting ADHD—as such, with additional data, sensitivity and specificity metrics can be substantially augmented. This study is the first to apply machine learning based methods for the detection of ADHD using solely pupillometrics, and highlights its strength as a potential discriminative biomarker, paving the path for the development of novel diagnostic applications to aid in the detection of ADHD using oculometric paradigms and machine learning.
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影响因子：
6
作者：
Christ, Maximilian;Braun, Nils;Kempa-Liehr, Andreas W.
通讯作者：
Kempa-Liehr, Andreas W.
影响因子：
4.6
作者：
Vabalas, Andrius;Gowen, Emma;Casson, Alexander J.
通讯作者：
Casson, Alexander J.
影响因子：
3.8
作者：
Babiker A;Faye I;Prehn K;Malik A
通讯作者：
Malik A
DOI：
10.1080/15374416.2017.1417860
发表时间：
2018-03
期刊：
Journal of clinical child and adolescent psychology : the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53
影响因子：
--
作者：
Danielson ML;Bitsko RH;Ghandour RM;Holbrook JR;Kogan MD;Blumberg SJ
通讯作者：
Blumberg SJ
随机青蛙：一种有效的可逆跳跃马尔可夫链蒙特卡罗方法，用于变量选择及其在基因选择和疾病分类中的应用
影响因子：
6.2
作者：
Li, Hong-Dong;Xu, Qing-Song;Liang, Yi-Zeng
通讯作者：
Liang, Yi-Zeng