Amado-Caballero P, Casaseca-de-la-Higuera P, Alberola-Lopez S, Andres-de-Llano JM, Villalobos JAL, Garmendia-Leiza JR, Alberola-Lopez C. Objective ADHD Diagnosis Using Convolutional Neural Networks Over Daily-Life Activity Records.
IEEE J Biomed Health Inform 2020;
24:2690-2700. [PMID:
31905156 DOI:
10.1109/jbhi.2020.2964072]
[Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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 [Formula: see text] average sensitivity, [Formula: see text] specificity and AUC values over [Formula: see text]. 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 diagn-ostic method, which can be easily implemented with daily devices.
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