Research

Discriminating between ADHD adults and controls using independent ERP components and a support vector machine: a validation study

Andreas Mueller^1*, Gian Candrian^1†, Venke A Grane^2†, Juri D Kropotov^3†, Valery A Ponomarev^3† and Gian-Marco Baschera^4†

• * Corresponding author: Andreas Mueller andreas_mueller@swissonline.ch

• † Equal contributors

Author Affiliations

¹ Brain and Trauma Foundation Grisons, Poststrasse 22, 7000 Chur, Switzerland

² Neuropsychological Service, Helgeland Hospital, Mosjøen, Norway

³ Institute of the Human Brain of Russian Academy of Sciences, St. Petersburg, Russian Federation

⁴ Department of Computer Science, ETH Zurich, Switzerland

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Nonlinear Biomedical Physics 2011, 5:5 doi:10.1186/1753-4631-5-5

Published: 19 July 2011

Abstract

Background

There are numerous event-related potential (ERP) studies in relation to attention-deficit hyperactivity disorder (ADHD), and a substantial number of ERP correlates of the disorder have been identified. However, most of the studies are limited to group differences in children. Independent component analysis (ICA) separates a set of mixed event-related potentials into a corresponding set of statistically independent source signals, which are likely to represent different functional processes. Using a support vector machine (SVM), a classification method originating from machine learning, this study aimed at investigating the use of such independent ERP components in differentiating adult ADHD patients from non-clinical controls by selecting a most informative feature set. A second aim was to validate the predictive power of the SVM classifier by means of an independent ADHD sample recruited at a different laboratory.

Methods

Two groups of age-matched adults (75 ADHD, 75 controls) performed a visual two stimulus go/no-go task. ERP responses were decomposed into independent components, and a selected set of independent ERP component features was used for SVM classification.

Results

Using a 10-fold cross-validation approach, classification accuracy was 91%. Predictive power of the SVM classifier was verified on the basis of the independent ADHD sample (17 ADHD patients), resulting in a classification accuracy of 94%. The latency and amplitude measures which in combination differentiated best between ADHD patients and non-clinical subjects primarily originated from independent components associated with inhibitory and other executive operations.

Conclusions

This study shows that ERPs can substantially contribute to the diagnosis of ADHD when combined with up-to-date methods.