Identification of Children with ADHD from EEG Signals Based on Entropy Measures and Support Vector Machine
Author:
Affiliation:
1. The University of Aizu,School of Computer Science and Engineering,Aizuwakamatsu,Japan,965-8580
2. Rajshahi University of Engineering and Technology,Dept. of Computer Science & Engineering,Rajshahi,Bangladesh,6204
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10322878/10323033/10323044.pdf?arnumber=10323044
Reference26 articles.
1. Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials
2. Machine-learning-based diagnosis of drug-naive adult patients with attention-deficit hyperactivity disorder using mismatch negativity
3. Functional brain dynamic analysis of ADHD and control children using nonlinear dynamical features of EEG signals
4. Machine learning approach for classification of ADHD adults
5. The blessing of Dimensionality: Feature Selection outperforms functional connectivity-based feature transformation to classify ADHD subjects from EEG patterns of phase synchronisation
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