RT info:eu-repo/semantics/article
T1 Objective ADHD Diagnosis Using Convolutional Neural Networks Over Daily-Life Activity Records
A1 Amado-Caballero, Patricia
A1 Casaseca de la Higuera, Juan Pablo
A1 Alberola López, Susana
A1 Andres-de-Llano, Jesus Maria
A1 Villalobos, Jose Antonio Lopez
A1 Garmendia-Leiza, Jose Ramon
A1 Alberola López, Carlos
AB 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.
SN 2168-2194
YR 2020
FD 2020
LK https://uvadoc.uva.es/handle/10324/64414
UL https://uvadoc.uva.es/handle/10324/64414
LA eng
NO Amado-Caballero, P., Casaseca-de-la-Higuera, P., Alberola-Lopez, S., Andres-de-Llano, J. M., Villalobos, J. A. L., Garmendia-Leiza, J. R., & Alberola-Lopez, C. (2020). Objective ADHD diagnosis using convolutional neural networks over daily-life activity records. IEEE journal of biomedical and health informatics, 24(9), 2690-2700.
DS UVaDOC
RD 24-nov-2024