Objective ADHD diagnosis using convolutional neural networks over daily-life activity records

Patricia Amado-Caballero, Pablo Casaseca-de-la-Higuera*, Susana Alberola-Lopez, Jesus Maria Andres-de-Llano, Jose Antonio Lopez-Villalobos, Jose Ramon Garmendia-Leiza, Carlos Alberola-Lopez

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
205 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)2690-2700
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
Issue number9
Early online date6 Jan 2020
Publication statusPublished - 30 Sep 2020


  • ADHD
  • actigraphy
  • deep learning
  • concolutional neural network (CNN)


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