ADHD Detection Using Machine learning Algorithms and EEG brain signals
DOI:
https://doi.org/10.32792/jeps.v13i1.237Keywords:
ADHD,, EEG,, machine learning,, K-means.Abstract
Attention deficit hyperactivity disorder (ADHD) is a behavioral problem that can last into adulthood
and affect children. Because it can show complicated brain activity, electroencephalography (EEG) plays
a key role in determining the neurophysiology of ADHD. several statistical features are extracted from
five frequency bands by using a discrete wavelet transform. The proposed system is evaluated by using
K-means-based feature selection and 5 machine learning methods (Least-square support vector machine,
k-nearest neighbor, Decision tree, and naive-Bayes classifier, support vector machine), so this system
developed using a ten-fold cross-validation strategy and showed the testing accuracy for each classifier as
( 96.49%, 92.66%, 88.08%, 68.39%,53.71% ), respectively.
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