Classification of ADHD patients on the basis of independent ERP components using a machine learning system

Andreas Mueller, Gian Candrian, Juri D Kropotov, Valery A Ponomarev, Gian-Marco Baschera, Andreas Mueller, Gian Candrian, Juri D Kropotov, Valery A Ponomarev, Gian-Marco Baschera

Abstract

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.

Figures

Figure 1
Figure 1
Schematic representation of the two stimulus GO/NOGO task Time dynamics of stimuli in four trial categories. Abbreviations: A, P, H are stimuli consisting of pictures of animals, plants and humans. GO trials require the subject to press a button. NOGO trials require suppression of a prepared action. IGNORE trials are stimuli pairs beginning with a picture of a plant, which require no preparation for action. NOVEL trials are stimuli pairs requiring no action, with the presentation of a novel sound together with the second human picture stimulus. Time intervals are depicted at the bottom.
Figure 2
Figure 2
Projection of input feature space into a higher dimensional space The classification process of two samples (circles and squares) is complex in 2 dimensions (left) and simple in three or more dimensions (right). The elements close to the hyperplane are called support vectors.
Figure 3
Figure 3
Sensory related independent components Top: BA 18 component at O1. Middle: BA 39 left component at T5. Bottom: BA 39 right component at T6. Time courses (left) are presented separately for control (black) and ADHD (red) group. x axis is time in ms, y axis is amplitude in μV. Results of t-statistics are presented below the curves with vertical bars corresponding to p<0.05. SLORETA imaging is presented on the right.
Figure 4
Figure 4
Executive independent components Top: BA 5 component at Pz. Middle: BA 6 component at Cz. Bottom: BA 25 component at Cz. Time courses (left) are presented separately for control (black) and ADHD (red) group. x axis is time in ms, y axis is amplitude in μV. Results of t-statistics are presented below the curves with vertical bars corresponding to p<0.05. SLORETA imaging is presented on the right.
Figure 5
Figure 5
Novelty related independent component BA 6 novelty component at Cz. Time course (left) is presented separately for control (black) and ADHD (red) group. x axis is time in ms, y axis is amplitude in μV. Results of t-statistics are presented below the curve with vertical bars corresponding to p<0.05. SLORETA imaging is presented on the right.

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