Browsing by Author "Karabiber Cura, Özlem"

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Citation - WoS: 1
Detection of Alzheimer's Dementia by Using Deep Time-Frequency Feature Extraction
(AVES, 2023) Karabiber Cura, Özlem; Türe, H. Sabiha; Akan, Aydin
Alzheimer's disease (AD), a neurological condition connected with aging, causes cognitive deterioration and has a substantial influence on a patient's daily activities. One of the most widely used clinical methods for examining how AD affects the brain is the electroencephalogram (EEG). Handcraft calculating descriptive features for machine learning algorithms requires time and frequently increases computational complexity. Deep networks provide a practical solution to feature extraction compared to handcraft feature extraction. The proposed work employs a time-frequency (TF) representation and a deep feature extraction-based approach to detect EEG segments in control subjects (CS) and AD patients. To create EEG segments'TF representations, high-resolution synchrosqueezing transform (SST) and traditional short-time Fourier transform (STFT) approaches are utilized. For deep feature extraction, SST and STFT magnitudes are used. The collected features are classified using a variety of classifiers to determine the EEG segments of CS and AD patients. In comparison to the SST method, the STFT-based deep feature extraction strategy produced improved classification accuracy between 79.56% and 92.96%.
Citation - WoS: 11
Citation - Scopus: 13
Detection of Attention Deficit Hyperactivity Disorder Based on Eeg Feature Maps and Deep Learning
(Elsevier, 2024) Karabiber Cura, Özlem; Akan, Aydın; Kocaaslan Atlı, Sibel
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-FM- based 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.