Novel entropy difference-based EEG channel selection technique for automated detection of ADHD
<|reference_start|>Novel entropy difference-based EEG channel selection technique for automated detection of ADHD: 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<|reference_end|>
@article{maheshwari2024novel,
    title={Novel entropy difference-based EEG channel selection technique for
  automated detection of ADHD},
    author={Shishir Maheshwari, Kandala N V P S Rajesh, Vivek Kanhangad, U
  Rajendra Acharya, T Sunil Kumar},
    journal={arXiv preprint arXiv:2404.09493},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.09493},
    primaryClass={eess.SP cs.HC cs.NE}
}