Browsing by Author "Cura, O.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.
Citation - WoS: 5
Citation - Scopus: 7
Classification of Alzheimer’s Dementia Eeg Signals Using Deep Learning
(SAGE Publications Ltd, 2025) Sen, S.Y.; Cura, O.K.; Yilmaz, G.C.; Akan, A.
Alzheimer’s dementia (AD) is a predominant neurological disorder arising from corruptions in brain functions and is characterized by a chronic or progressive nature. While the precise etiology of dementia remains incompletely elucidated, its manifestation is frequently associated with discernible structural and chemical alterations in the brain. Living with dementia significantly impacts individuals’ daily lives due to the resultant loss of cognitive functions. This study presents a novel method to monitor and detect AD using advanced signal processing applied to electroencephalography (EEG) signals. The intrinsic time-scale decomposition (ITD) algorithm is employed to extract proper rotation components (PRCs) from EEG signals, utilizing a 5-second EEG segment duration. The proposed method is compared with the detection of 5-second raw EEG segments using a custom one-dimensional convolutional neural network (1D CNN). Additionally, four different quartiles (Quartile 1 (Q1), Q2, Q3, and Q4) of EEG signals are considered to identify the most significant contributor to AD. Experimental results demonstrate that the ITD-based approach yields better detection performance compared to using raw EEG signals. The most promising result is achieved by the EEG-PRCs method in Q1, with an accuracy of 94.00%, sensitivity of 93.50%, and specificity of 93.90%. In contrast, the highest-performing result of the raw EEG segments method is in Q2, with an accuracy of 88.40%, sensitivity of 89.10%, and specificity of 87.60% in terms of detecting AD. © The Author(s) 2024.
Citation - Scopus: 3
Classification of Dementia Eeg Signals by Using Time-Frequency Images for Deep Learning
(Institute of Electrical and Electronics Engineers Inc., 2023) Şen, Sena Yağmur; Cura, O.K.; Akan, Aydın
Dementia is a prevalent neurological disorder that impairs cognitive functions and significantly diminishes the quality of life. In this research, a deep learning method is introduced for detecting and monitoring Alzheimer's Dementia (AD) by analyzing Electroencephalography (EEG) signals. To accomplish this, a signal decomposition technique known as Intrinsic Time Scale Decomposition (ITD) is employed to classify EEG segments obtained from both AD patients and control subjects. The analysis specifically concentrates on 5-second EEG segments, utilizing ITD to extract Proper Rotation Components (PRCs) from these segments. The PRCs are subsequently transformed into Time-Frequency (TF) images using the Short-Time Fourier Transform (STFT) spectrogram. These TF images serve as training data for a 2-Dimensional Convolutional Neural Network (2D CNN). The proposed approach is compared with the classification of the spectrogram of 5-second EEG segments using the same CNN architecture. The experimental results conclusively demonstrate the superior classification performance of the ITD-based approach when compared to the utilization of raw EEG signals. © 2023 IEEE.
Citation - Scopus: 2
Combinations of Eeg Topographic Feature Maps for the Classification of Adhd
(European Signal Processing Conference, EUSIPCO, 2023) Pehlivan, Sude; Akdemir, Onur; Cura, O.K.; Akbuğday, Burak; Akan, Aydın
Attention-Deficit/Hyperactivity Disorder (ADHD) is a common mental disorder affecting both children and adults. It is characterized by issues with concentration, hyperactivity, and impulsivity, which can interfere with everyday duties and interpersonal relationships. Although behavioral studies are utilized to treat the disease, there is no proven method for detecting it. The Electroencephalogram (EEG) is a non-invasive method that monitors electrical activity in the brain and is commonly used to identify neurological and mental illnesses such as ADHD. In this study, the topographic EEG feature maps (EEG-FMs) were obtained from 6 traditional time-domain characteristics known as Hjorth activity, Hjorth mobility, Hjorth complexity, kurtosis, and skewness. The feature maps were concatenated and used as input to Convolutional Neural Network (CNN) model for ADHD classification. To show the efficacy of the recommended approach, EEG data from 15 ADHD individuals and 18 control subjects (CS) were analyzed. The results showed that concatenated EEG-FMs were successful to classify ADHD with up to 99.72% accuracy. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.
Citation - Scopus: 2
Detection of Alzheimer's Dementia Using Intrinsic Time Scale Decomposition of Eeg Signals and Deep Learning
(Institute of Electrical and Electronics Engineers Inc., 2023) Şen, Sena Yağmur; Cura, O.K.; Akan, Aydın
Dementia is a prevalent neurological disorder that results in cognitive function decline, significantly impacting the quality of life. In this study, a signal decomposition based method is proposed for the detection and follow-up Alzheimer's Dementia (AD) by using Electroencephalography (EEG) signals. The proposed approach uses the Intrinsic Time Scale Decomposition (ITD) to classify EEG segments of AD patients and control subjects. Signal decomposition process is conducted with 5 seconds EEG segment duration. Proper Rotation Components (PRCs) extracted from the EEG segments are used to train a 1-Dimensional Convolutional Neural Network (1D CNN). The proposed method is compared with classification of 5s duration EEG segments using the same CNN architecture. The experimental results demonstrate that utilizing ITD based approach yields better classification performance when compared to using the plain EEG signals. © 2023 IEEE.
Citation - Scopus: 3
Detection of Attention Deficit Hyperactivity Disorder by Using Eeg Feature Maps and Deep Learning
(European Signal Processing Conference, EUSIPCO, 2023) Akbuğday, Burak; Bozbas, O. A.; Cura, O.K.; Pehlivan, Sude; Akan, Aydın
Attention deficit hyperactivity disorder (ADHD) is a mental disorder that affects the behavior of the persons, and usually onsets in childhood. ADHD generally causes impulsivity, hyperactivity, and inattention which impairs day-to-day life even in the adulthood if left undiagnosed and untreated. Although various guidelines for diagnosis of ADHD exist, a universally accepted objective diagnostic procedure is not established. Since current diagnosis of ADHD heavily relies on the expertise of healthcare providers, an EEG Topographic Feature Map (EEG-FM) based method is proposed in this study which aims to objectively diagnose ADHD. 6 different features extracted from EEG recordings acquired from 33 participants, 15 ADHD patients and 18 control subjects, converted into EEG-FM images and fed into a convolutional neural network (CNN) based classifier. Results indicate that the proposed method can accurately classify ADHD patients with up to 99% accuracy, precision, and recall. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.
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.