Cura, O.K.Ciklacandir, F.G.Akan, AydınAtli, S.K.2024-02-242024-02-2420239798350328967https://doi.org/10.1109/TIPTEKNO59875.2023.10359178https://hdl.handle.net/20.500.14365/51802023 Medical Technologies Congress, TIPTEKNO 2023 -- 10 November 2023 through 12 November 2023 -- 195703Attention 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.eninfo:eu-repo/semantics/closedAccessADHDDeep Feature extractionEEG connectivity featured imageMachine learningDeep learningDiseasesElectrophysiologyFeature extractionImage fusionImage processingSpectral densityAttention deficit hyperactivity disorderBrain connectivityConditionConnectivity informationDecision level fusionDeep feature extractionElectroencephalography connectivity featured imageFeatures extractionMachine-learningNeurological disordersElectroencephalographyConference Object10.1109/TIPTEKNO59875.2023.103591782-s2.0-85182727233