Automatic identification of ADHD children during visual attention task using variable length EEG

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  • Nausheen Ansari Aligarh Muslim University
  • Omar Farooq Aligarh Muslim University
  • Yusuf Uzzaman Khan Aligarh Muslim University



Neuropsychiatric Disorders, EEG, DWPT, ADHD, Continuous Performance Task


neuropsychiatric disorders affect millions of people of all ages worldwide. Attention deficit/Hyperactivity Disorder (ADHD), a typical neurodevelopmental disorder, deteriorates the performance of children in family and school settings thereby, hindering typical brain development. ADHD children, in most cases, are predominantly inattentive. In this research, automatic identification of neuronal patterns in ADHD children undergoing visual continuous performance tasks (CPT) is used to successfully differentiate between ADHD and typically developing children. The proposed methodology utilizes wavelet packet decomposition to extract relative energy from different EEG sub-bands, namely-delta (0.5- 4 Hz) theta (4-8 Hz), alpha (8-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz) and gamma (>30 Hz). The obtained feature from all bands is then passed to the Support Vector Machine (SVM) for the classification of children as normal and those with ADHD. The performance of the algorithm is assessed by following performance parameters, accuracy, sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve. While testing the classification performance keeping the relative energy of individual bands in feature space, it is observed that between groups difference in normal and ADHD is much higher in high-frequency bands i.e., beta and gamma compared to low-frequency bands. However, all other bands are interacting features that performed well along with the relevant features (beta and gamma energy bands, here). The area under the curve obtained with a subject-independent approach and combined EEG sub-band's relative energy in feature space is found to be 0.99.

Author Biographies

Nausheen Ansari, Aligarh Muslim University

Centre for Biomedical Engineering,  India

Omar Farooq, Aligarh Muslim University

Department of Electronics Engineering, India

Yusuf Uzzaman Khan, Aligarh Muslim University

Department of Electrical Engineering,  India




How to Cite

Ansari, N., Farooq, O., & Khan, Y. U. (2023). Automatic identification of ADHD children during visual attention task using variable length EEG. International Conference on Modern and Advanced Research, 286–291.

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