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

Shishir Maheshwari, Kandala N V P S Rajesh, Vivek Kanhangad, U Rajendra Acharya and T Sunil Kumar

PLOS ONE, 2025, vol. 20, issue 4, 1-21

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 has outperformed the existing approaches in automated ADHD detection.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0319487

DOI: 10.1371/journal.pone.0319487

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