Objective ADHD Diagnosis Using Convolutional Neural Networks Over Daily-Life Activity Records

• Published in: IEEE journal of biomedical and health informatics Vol. 24; no. 9; pp. 2690 - 2700
• Main Authors: Amado-Caballero, Patricia, Casaseca-de-la-Higuera, Pablo, Alberola-Lopez, Susana, Andres-de-Llano, Jesus Maria, Villalobos, Jose Antonio Lopez, Garmendia-Leiza, Jose Ramon, Alberola-Lopez, Carlos
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: actigraphy; ADHD; Artificial neural networks; Attention deficit hyperactivity disorder; Children; Convolutional Neural Network (CNN); Deep learning; Diagnosis; Diagnostic systems; Electroencephalography; Etiology; Functional magnetic resonance imaging; Machine learning; Medical diagnosis; Neural networks; Pediatrics; Sleep; Spectrograms
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2020.2964072

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 <inline-formula><tex-math notation="LaTeX">{\text{97.62}\%}</tex-math></inline-formula> average sensitivity, <inline-formula><tex-math notation="LaTeX">{\text{99.52}\%}</tex-math></inline-formula> specificity and AUC values over <inline-formula><tex-math notation="LaTeX">{\text{99}\%}</tex-math></inline-formula>. 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.