Evaluation Of Feature Extraction Techniques On Event-Related Potentials For Detection Of Attention-Deficit/Hyperactivity Disorder

P. Castro-Cabrera, J. Gomez-Garcia, F. Restrepo, O. Moscoso, G. Castellanos-Dominguez

Engineering in Medicine and Biology Society（2010）

Cited 8|Views0

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Abstract

Event-related potentials (ERPs) are one of the most informative and dynamic methods of monitoring cognitive processes, which are widely used in clinical research to deal a variety of psychiatric and neurological disorders as attention-deficit/hyperactivity disorder (ADHD). This work proposes an extraction and selection methodology for discriminating between normal and pathological patients with ADHD by using ERPs. Three different sets of features (morphological, wavelets, and nonlinear based) are analyzed, looking for the best classification accuracy. The results show that the wavelet features provided a good discriminative capability, but it improved by combining all the set of features and applying a feature selection algorithm, reaching a maximum accuracy rate of 91.3%.

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Key words

behavioural sciences,bioelectric potentials,electroencephalography,feature extraction,medical disorders,medical signal processing,neurophysiology,signal classification,ADHD detection,attention deficit hyperactivity disorder,classification accuracy,cognitive process monitoring,event related potentials,feature extraction techniques,feature selection algorithm,morphological features,neurological disorders,nonlinear features,psychiatric disorders,wavelet features,

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