Detection of Attention Deficit Hyperactivity Disorder based on EEG feature maps and deep learning

• Autorzy: Karabiber Cura Ozlem , Akan Aydin , Kocaaslan Atli Sibel

Identyfikatory

DOI

10.1016/j.bbe.2024.07.003

• Języki publikacji: EN

Abstrakty

EN

Attention Deficit Hyperactivity Disorder (ADHD) is a neurological condition, typically manifesting in childhood. Behavioral studies are used to treat the illness, but there is no conclusive way to diagnose it. To comprehend changes in the brain, electroencephalography (EEG) signals of ADHD patients are frequently examined. In the proposed study, we introduce EEG feature map (EEG-FM)-based image construction to input deep learning architectures for classifying ADHD. To demonstrate the effectiveness of the proposed method, EEG data of 15 ADHD patients and 18 control subjects are analyzed and detection performance is presented. EEG-FMbased images are obtained using both traditional time domain features used in EEG analysis, such as Hjorth parameters (activity, mobility, complexity), skewness, kurtosis, and peak-to-peak, and nonlinear features such as the largest Lyapunov Exponent, correlation dimension, Hurst exponent, Katz fractal dimension, Higuchi fractal dimension, and approximation entropy. EEG-FM-based images are used to train DarkNet19 architecture and deep features are extracted for each image dataset. Fewer deep features are chosen for each image dataset using the Minimum Redundancy Maximum Relevance (mRMR) feature selection method, and the concatenated deep feature set is created by merging the selected features. Finally, various machine learning methods are used to classify the concatenated deep features. Our EEG-FM and DarkNet19-based approach yields classification accuracies for ADHD between 96.6% and 99.9%. Experimental results indicate that the use of EEG-FM-based images as input to DarkNet19 architecture gives significant advantages in the detection of ADHD.

Słowa kluczowe

EN

ADHD detection; EEG feature map; deep feature extraction; feature concatenation; machine learning

PL

wykrywanie ADHD; EEG; głęboka ekstrakcja cech; uczenie maszynowe

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2024
• Tom: Vol. 44, no. 3
• Strony: 450--460
• Opis fizyczny: Bibliogr. 43 poz., rys., tab., wykr.

Twórcy

autor

Karabiber Cura Ozlem

• Department of Biomedical Engineering, Izmir Katip Celebi University, Cigli, 36520, Izmir, Turkey

autor

Akan Aydin

• Department of Electrical and Electronics Engineering, Izmir University of Economics, Balcova, 35330, Izmir, Turkey

autor

Kocaaslan Atli Sibel

• Department of Biophysics, Faculty of Medicine, Izmir Katip Celebi University, Cigli, 36520, Izmir, Turkey

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).