Novel entropy difference-based EEG channel selection technique for automated detection of ADHD

TL;DR

This paper introduces an entropy difference-based EEG channel selection method for automated ADHD detection, achieving high accuracy and outperforming existing entropy-based approaches.

Contribution

The paper proposes a novel entropy difference-based channel selection technique that improves EEG-based ADHD detection accuracy over traditional entropy methods.

Findings

Achieved up to 99.29% classification accuracy.

EnD-based channel selection outperforms entropy-based methods.

Effective across multiple feature extraction and classification techniques.

Abstract

Attention deficit hyperactivity disorder (ADHD) is one of the common neurodevelopmental disorders in children. This paper presents an automated approach for ADHD detection using the proposed entropy difference (EnD)- based encephalogram (EEG) channel selection approach. In the proposed approach, we selected the most significant EEG channels for the accurate identification of ADHD using an EnD-based channel selection approach. Secondly, a set of features is extracted from the selected channels and fed to a classifier. To verify the effectiveness of the channels selected, we explored three sets of features and classifiers. More specifically, we explored discrete wavelet transform (DWT), empirical mode decomposition (EMD) and symmetrically-weighted local binary pattern (SLBP)-based features. To perform automated classification, we have used k-nearest neighbor (k-NN), Ensemble classifier, and support vectors machine (SVM) classifiers. Our proposed approach yielded the highest accuracy of 99.29% using the public database. In addition, the proposed EnD-based channel selection has consistently provided better classification accuracies than the entropy-based channel selection approach. Also, the developed method