Entropy difference-based EEG channel selection technique for automated detection of ADHD
Shishir Maheshwari1, Kandala N V P S Rajesh2, Vivek Kanhangad3
1Department of Electronics and Communication Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, Uttar Pradesh, India.
Plos One
|April 3, 2025
Summary
This study introduces an automated method for detecting Attention Deficit Hyperactivity Disorder (ADHD) in children using electroencephalogram (EEG) analysis. The novel entropy difference (EnD) approach achieved 99.29% accuracy, outperforming existing methods.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Machine Learning
Background:
- Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder in children.
- Accurate and early detection of ADHD is crucial for effective intervention and management.
- Existing ADHD detection methods often lack efficiency and require significant manual input.
Purpose of the Study:
- To develop an automated and highly accurate approach for ADHD detection using electroencephalogram (EEG) data.
- To introduce a novel entropy difference (EnD)-based channel selection method for identifying significant EEG channels.
- To evaluate the performance of the proposed method against existing techniques.
Main Methods:
- Utilized an entropy difference (EnD)-based approach for selecting the most significant EEG channels.
- Extracted features from selected channels using Discrete Wavelet Transform (DWT), Empirical Mode Decomposition (EMD), and Symmetrically-weighted Local Binary Pattern (SLBP).
- Employed k-nearest neighbor (k-NN), Ensemble classifier, and Support Vector Machine (SVM) for automated classification.
Main Results:
- The proposed EnD-based channel selection method achieved a highest accuracy of 99.29% on a public database.
- The EnD approach consistently outperformed traditional entropy-based channel selection methods.
- The developed automated ADHD detection system demonstrated superior performance compared to existing approaches.
Conclusions:
- The proposed automated ADHD detection system, utilizing EnD-based EEG channel selection, offers a highly accurate and efficient diagnostic tool.
- The EnD method effectively identifies crucial EEG channels, enhancing classification performance.
- This approach holds significant potential for improving the early diagnosis and management of ADHD in children.
