Browsing by Author "Atli, S.K."

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    Citation - Scopus: 5
    Classification of Adhd by Using Multiple Feature Maps of Eeg Signals and Deep Feature Extraction
    (European Signal Processing Conference, EUSIPCO, 2023) Cura, O.K.; Atli, S.K.; Şen, Sena Yağmur; Akan, Aydın
    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. In order to comprehend changes in the brain, electroencephalography (EEG) signals of ADHD patients are frequently examined. In the proposed study, we introduced EEG feature maps (EEG-FM)-based image construction to be used as input to CNN architectures. To demonstrate the effectiveness of the proposed method, EEG data of 15 ADHD patients and 18 control subjects are analyzed and ADHD detection performance is demonstrated. EEG-FM-based images are obtained using both time domain features such as Hjorth parameters (activity, mobility, complexity), skewness, kurtosis, and peak-to-peak, and nonlinear features such as largest Lyapunov Exponent, correlation dimension, Hurst exponent, Katz fractal dimension, Higuchi fractal dimension, and approximation entropy. ResNet18 is trained using EEG-FM-based images and deep features are extracted for each image subset. Using the SVM classifier, the ADHD detection performance of the proposed approach is evaluated. Experimental results revealed that using EEG-FM-based images as input to ResNet architecture offers important benefits in identifying ADHD. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.
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    Detection of Attention Deficit Hyperactivity Disorder Using Decision-Level Fusion of Brain Connectivity Information Based on Deep Learning
    (Institute of Electrical and Electronics Engineers Inc., 2023) Cura, O.K.; Ciklacandir, F.G.; Akan, Aydın; Atli, S.K.
    Attention Deficit Hyperactivity Disorder (ADHD) is a neurological disorder that often first appears in children. The condition is treated via behavioral studies, but there is no definitive technique to identify it. Electroencephalography (EEG) signals of ADHD patients are widely investigated to understand alterations in the brain. We offered EEG connectivity featured image creation to be used as input to CNN architectures in the proposed study. EEG data from 15 ADHD patients and 18 control participants are evaluated, and ADHD detection performance is shown, to demonstrate the efficacy of the suggested method. EEG connectivity featured images are obtained using six different connectivity features (magnitude square coherence, cross-power spectral density, correlation coefficient, covariance, cohentropy coefficient, and correntrophy coefficient). Deep features are extracted for each image subset using EEG connectivity-featured images to train ResNet-50. The effectiveness of the suggested strategy for detecting ADHD is assessed using the DT, LR, and SVM classifiers and decision-level fusion. According to experimental results, employing EEG connectivity featured images as input to ResNet-50 architecture, and decision-level fusion offers important advantages in identifying ADHD. © 2023 IEEE.