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

Abstract

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

Clinical Utility of EEG in Attention-Deficit/Hyperactivity Disorder: A Research Update

“…A combination of 5 peak amplitude and latency measures associated with inhibition, monitoring and other executive operations were extracted to maximize group discrimination. High rates of classification accuracy for both the original sample (91 %) and a subsequent validation sample (94 %) were obtained [41]. This, and other studies, using a semi-supervised feature selection to define new features of the EEG signal [42], as well as studies using graph theory and community pattern analysis of EEG-derived functional connectivity [43] may provide new avenues to identify and test EEG measures, which can both tolerate sample heterogeneity and provide maximal discrimination between individuals with and without ADHD.…”

“…One example is a study by Mueller et al [41] who used machine-learning methods on independent component analysis (ICA)-resolved ERP features from samples of healthy controls and ADHD adults. A combination of 5 peak amplitude and latency measures associated with inhibition, monitoring and other executive operations were extracted to maximize group discrimination.…”

“…In the first example of the application of ICA to EEG data from a clinical ADHD study (previously reviewed), Mueller et al [41] applied ICA decomposition to matrices of 19-channel ERP averages from 297 participants in a visual Go/NoGo task and used peak features of the resulting component ERPs to select ERP window inputs to a support-vector machine subject group classifier. Applying ICA to matrices of ERPs has the advantage of efficiently separating spatiotemporally distinct information in the data, but the results cannot be as spatially distinct as ICA decomposition of the nonaveraged continuous EEG data, which can also benefit from extra degrees of freedom afforded by higher density recordings.…”

A Survey of Support Vector Machines with Uncertainties

“…In recent years, SVM have been applied to many fields and have many algorithmic and modeling variations. In the biomedical field, SVM have been used to identify physical diseases [7][8][9][10] as well as psychological diseases [11]. Electroencephalography (EEG) signals can also be analyzed using SVM [12][13][14].…”

New Insights in Prediction and Dynamic Modeling from Non-Gaussian Mixture Processing Methods

“…This application can be used to remove unimportant signals and reduce the workload of the expert during exploration. Moreover, it can also be used for automatic diagnosis of illnesses such as ADHD (Attention Deficit Hyperactivity Disorder) or sleep disorders [180,230]. There are also works on the application of ICA to the separation of fMRI signals [168].…”