Wavelet-based approach for diagnosing attention deficit hyperactivity disorder (ADHD).
Wavelet-based approach for diagnosing attention deficit hyperactivity disorder (ADHD).
复制标题
基于小波的方法来诊断注意力缺陷多动障碍（ADHD）。
DOI：
10.1038/s41598-022-26077-2
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发表时间：
2022-12-19
影响因子：
4.6
通讯作者：
Vidakovic, Brani
中科院分区：
文献类型：
--
作者：
Vimalajeewa, Dixon;McDonald, Ethan;Bruce, Scott Alan;Vidakovic, Brani
注意缺陷多动障碍（ADHD）是一种常见的影响儿童的认知障碍。ADHD可以干扰教育，社会和情感发展，因此早期发现对于获得适当的护理至关重要。标准ADHD诊断方案严重依赖于对感知行为的主观评估。客观的诊断措施将是一个受欢迎的发展，并可能有助于准确和有效地诊断ADHD。瞳孔动力学分析已被提出作为一个有前途的替代方法，有效地检测受影响的个人。本研究提出了一种基于瞳孔动力学自相似性的方法，并评估其作为潜在诊断生物标志物的强度。在小波域中开发本地化的歧视性特征，并通过滚动窗口方法选择，以建立分类器。在基于任务的10-12岁儿童瞳孔直径时间序列数据集上的应用表明，所提出的方法在检测ADHD方面达到了78%以上的准确率。与在原始数据域中构造特征的方法相比，所提出的基于小波的分类器以较少的特征实现了更准确的ADHD分类。研究结果表明，所提出的诊断程序涉及瞳孔直径数据的可解释的基于小波的自相似特征，可能有助于提高ADHD诊断的有效性。
Attention deficit hyperactivity disorder (ADHD) is a common cognitive disorder affecting children. ADHD can interfere with educational, social, and emotional development, so early detection is essential for obtaining proper care. Standard ADHD diagnostic protocols rely heavily on subjective assessments of perceived behavior. An objective diagnostic measure would be a welcome development and potentially aid in accurately and efficiently diagnosing ADHD. Analysis of pupillary dynamics has been proposed as a promising alternative method of detecting affected individuals effectively. This study proposes a method based on the self-similarity of pupillary dynamics and assesses its strength as a potential diagnostic biomarker. Localized discriminatory features are developed in the wavelet domain and selected via a rolling window method to build classifiers. The application on a task-based pupil diameter time series dataset of children aged 10–12 years shows that the proposed method achieves greater than 78% accuracy in detecting ADHD. Comparing with a recent approach that constructs features in the original data domain, the proposed wavelet-based classifier achieves more accurate ADHD classification with fewer features. The findings suggest that the proposed diagnostic procedure involving interpretable wavelet-based self-similarity features of pupil diameter data can potentially aid in improving the efficacy of ADHD diagnosis.
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影响因子：
4.6
作者：
Das W;Khanna S
通讯作者：
Khanna S
DOI：
10.1214/09-aoas312
发表时间：
2009-01-01
期刊：
The annals of applied statistics
影响因子：
--
作者：
Kosorok MR
通讯作者：
Kosorok MR
影响因子：
4.6
作者：
Nobukawa S;Shirama A;Takahashi T;Takeda T;Ohta H;Kikuchi M;Iwanami A;Kato N;Toda S
通讯作者：
Toda S
影响因子：
1.5
作者：
Dueck, Johannes;Edelmann, Dominic;Richards, Donald
通讯作者：
Richards, Donald
影响因子：
0.8
作者：
Szekely, Gabor J.;Rizzo, Maria L.
通讯作者：
Rizzo, Maria L.