卷积神经网络
活动记录
计算机科学
人工智能
注意缺陷多动障碍
机器学习
深度学习
人工神经网络
医学
精神科
失眠症
作者
Patricia Amado-Caballero,Pablo Casaseca‐de‐la‐Higuera,Susana Alberola-López,Jesús María Andrés-de-Llano,José Antonio López Villalobos,José Ramón Garmendia Leiza,Carlos Alberola-López
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2020-09-01
卷期号:24 (9): 2690-2700
被引量:15
标识
DOI:10.1109/jbhi.2020.2964072
摘要
Attention Deficit/Hyperactivity Disorder (ADHD) is the most common neurobehavioral disorder in children and adolescents. However, its etiology is still unknown, and this hinders the existence of reliable, fast and inexpensive standard diagnostic methods. Objective: This paper proposes an end-to-end methodology for automatic diagnosis of the combined type of ADHD. Methods: Diagnosis is based on the analysis of 24 hour-long activity records using Convolutional Neural Networks to classify spectrograms of activity windows. Results: We achieve up to 97.62% average sensitivity, 99.52% specificity and AUC values over 99%. Overall, our figures overcome those obtained by actigraphy-based methods reported in the literature as well as others based on more expensive (and not so convenient) acquisition methods. Conclusion: These results reinforce the idea that combining deep learning techniques together with actimetry can lead to a robust and efficient system for objective ADHD diagnosis. Significance: Reliance on simple activity measurements leads to an inexpensive and non-invasive objective diagnostic method, which can be easily implemented with daily devices.
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