Paper Info
Title
Objective ADHD Diagnosis Using Convolutional Neural Networks Over Daily-Life Activity Records
Abstract
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.
Year
DOI
Venue
2020
10.1109/JBHI.2020.2964072
IEEE Journal of Biomedical and Health Informatics
Keywords
DocType
Volume
Actigraphy,Activities of Daily Living,Adolescent,Attention Deficit Disorder with Hyperactivity,Child,Humans,Neural Networks, Computer
Journal
24
Issue
ISSN
Citations 
9
2168-2194
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Patricia Amado-Caballero100.34
Pablo Casaseca27715.83
Susana Alberola-Lopez300.34
Jesus Maria Andres400.68
José Antonio López-Villalobos500.68
Jose Ramon Garmendia-Leiza600.34
Carlos Alberola-López748252.95