This article is part of the supplement: Consciousness and its Measures: Joint Workshop for COST Actions NeuroMath and Consciousness
Proceedings
Classification of ADHD patients on the basis of independent ERP components using a machine learning system
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* Corresponding author: Andreas Mueller andreas_mueller@swissonline.ch
- † Equal contributors
1 Brain and Trauma Foundation Grisons, Poststrasse 22, 7000 Chur, Switzerland
2 Institute of the Human Brain of Russian Academy of Sciences, ul. Acad. Pavlova 9, 197376 St. Petersburg, Russian Federation
Nonlinear Biomedical Physics 2010, 4(Suppl 1):S1 doi:10.1186/1753-4631-4-S1-S1
Published: 3 June 2010Abstract
Background
In the context of sensory and cognitive-processing deficits in ADHD patients, there is considerable evidence of altered event related potentials (ERP). Most of the studies, however, were done on ADHD children. Using the independent component analysis (ICA) method, ERPs can be decomposed into functionally different components. Using the classification method of support vector machine, this study investigated whether features of independent ERP components can be used for discrimination of ADHD adults from healthy subjects.
Methods
Two groups of age- and sex-matched adults (74 ADHD, 74 controls) performed a visual two stimulus GO/NOGO task. ERP responses were decomposed into independent components by means of ICA. A feature selection algorithm defined a set of independent component features which was entered into a support vector machine.
Results
The feature set consisted of five latency measures in specific time windows, which were collected from four different independent components. The independent components involved were a novelty component, a sensory related and two executive function related components. Using a 10-fold cross-validation approach, classification accuracy was 92%.
Conclusions
This study was a first attempt to classify ADHD adults by means of support vector machine which indicates that classification by means of non-linear methods is feasible in the context of clinical groups. Further, independent ERP components have been shown to provide features that can be used for characterizing clinical populations.