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

2011; BioMed Central; Volume: 5; Issue: 1 Linguagem: Inglês

10.1186/1753-4631-5-5

1753-4631

Andreas Mueller, Gian Candrian, Venke Arntsberg Grane, Juri D. Kropotov, В. А. Пономарев, Gian-Marco Baschera,

Neural and Behavioral Psychology Studies

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