Vol.28, No.3, 2021, pp.669-682, doi:10.32604/iasc.2021.017478
Machine Learning Based Framework for Classification of Children with ADHD and Healthy Controls
  • Anshu Parashar*, Nidhi Kalra, Jaskirat Singh, Raman Kumar Goyal
Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
* Corresponding Author: Anshu Parashar. Email:
Received 27 January 2021; Accepted 01 March 2021; Issue published 20 April 2021
Electrophysiological (EEG) signals provide good temporal resolution and can be effectively used to assess and diagnose children with Attention Deficit Hyperactivity Disorder (ADHD). This study aims to develop a machine learning model to classify children with ADHD and Healthy Controls. In this study, EEG signals captured under cognitive tasks were obtained from an open-access database of 60 children with ADHD and 60 Healthy Controls children of similar age. The regional contributions towards attaining higher accuracy are identified and further tested using three classifiers: AdaBoost, Random Forest and Support Vector Machine. The EEG data from 19 channels is taken as input features in individual and combinatorial sets to classifiers. Evaluating all the classifiers' overall performance, the highest accuracy of 84% is obtained with the AdaBoost classifier when all the Right Hemisphere channels are taken into consideration. The higher sensitivity of 96% indicates a better true positive detection rate of the model created with the Right Hemisphere features. This study highlights the intrinsic physiological contrast prevalent in brain activity of ADHD and healthy children, which can be effectively utilized for diagnostic purposes.
Attention deficit hyperactivity disorder; classification; electroencephalography; adaboost; random forest; support vector machine; machine learning
Cite This Article
A. Parashar, N. Kalra, J. Singh and R. K. Goyal, "Machine learning based framework for classification of children with adhd and healthy controls," Intelligent Automation & Soft Computing, vol. 28, no.3, pp. 669–682, 2021.
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