Research Study Abstract

Automatic Diagnosis of ADHD Based on Nonlinear Analysis of Actimetry Registries

  • Added on September 3, 2013

Introduction:
Attention-Deficit Hyperactivity Disorder (ADHD) is the most common mental health problem in childhood and adolescence. Its diagnosis is commonly performed in a subjective manner since current objective measurements are either expensive or time-consuming. However, subjective methods tend to overestimate the severity of the pathology. In this paper, we propose a novel methodology for automatic diagnosis of ADHD based on signal processing methods.

Methods:
The method is constructed in two stages: 1) An automatic activity/rest detection filter which allows for a separate analysis of both types of periods and 2) A feature extraction module based on nonlinear regularity quantification of either the global signal or the detected epochs.

Results:
Results on real data show that the proposed methodology can discriminate between patients and controls with sensibility and specificity values approaching 80%.

Author(s)

  • Diego Martin 1
  • Pablo Casaseca 1
  • Susana Alberola 2
  • Jose Antonio Lopez 2
  • Francisco Carlos Ruiz 2
  • Jesus Marıa Andres 2
  • Jose Ramon Garmendia 2
  • Julio Ardura 2

Institution(s)

  • 1

    Laboratory of Image Processing (LPI), E.T.S.I. Telecomunicacion, University of Valladolid, Spain
  • 2

    Chronobiology Group, Faculty of Medicine University of Valladolid, Spain

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