Browsing by Author "Atli, Sibel Kocaaslan"

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Citation - WoS: 12
Citation - Scopus: 13
Attention Deficit Hyperactivity Disorder Recognition Based on Intrinsic Time-Scale Decomposition of Eeg Signals
(Elsevier Sci Ltd, 2023) Cura, Ozlem Karabiber; Atli, Sibel Kocaaslan; Akan, Aydin; Karabiber Cura, Ozlem; Kocaaslan Atli, Sibel
Attention deficit hyperactivity disorder (ADHD), a neuro-developmental condition, is characterized by various degrees of impulsivity, hyperactivity, and inattention. Treatment of this condition and minimizing its negative impact on learning, working, forming relationships, and quality of life depends heavily on the early identifi-cation. The Electroencephalography (EEG) is a useful neuroimaging technique for understanding ADHD. This study examines the brain activity of children with ADHD by analyzing the EEG signals using the intrinsic time-scale decomposition (ITD). Different combinations of the modes, known as Proper Rotation Components (PRCs), produced by ITD, are used to extract a variety of connectivity-based features (magnitude square coherence, cross power spectral density, correlation coefficient, covariance, cohentropy coefficient, correntropy coefficient). EEG signals of 15 ADHD children and 18 age-matched health children are recorded while resting with the eyes closed. Mentioned features are calculated using different channel pairs chosen from longitudinal and transversal planes. Through various machine learning approaches and a 10-fold cross-validation method, the proposed approach is evaluated to distinguish between ADHD patients and healthy controls. Classification accuracies are obtained for the longitudinal and transverse planes, between 92.90% to 99.90% and 91.70% to 100.00%, respectively. Our results support the remarkable performance of the proposed approach, and represent a substantial advance over similar studies in terms of recognizing and classifying ADHD.
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; Atli, Sibel Kocaaslan; Cura, Ozlem Karabiber
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.
Detection of Attention Deficit Hyperactivity Disorder Using Eeg Signals and Douglas-Peucker Algorithm
(IEEE, 2022) Cura, Ozlem Karabiber; Aydin, Gamze N.; Celen, Sibel; Atli, Sibel Kocaaslan; Akan, Aydin
Attention Deficit Hyperactivity Disorder (ADHD) is a neurological disease that typically appears in childhood. The disease has three main symptoms in children: inattention, hyperactivity, and impulsivity. Treatment of the disease is based on behavioral studies; however, there is no definitive diagnosis method. Hence, the electroencephalography (EEG) signals of ADHD subjects are often investigated to understand changes in the brain. In the proposed study, it is aimed to process and reduce the EEG data of ADHD and control subjects (CS) by using the Douglas-Peucker algorithm and to investigate the effects of the algorithm on EEG signal analysis. EEG data obtained from 18 control subjects (4 boys, 14 girls, mean age 13) and 15 ADHD patients (7 boys, 8 girls, mean age 12) are collected. By using reduced EEG data; time features such as energy, skewness, kurtosis, mean absolute deviation (MAD), root mean square (RMS), peak to peak (PTP) value, Hjorth parameters, and non-linear features such as largest Lyapunov Exponent (LLE), correlation dimension (CD), Hurst exponent (HE), Katz fractal dimension (KFD), Higuchi fractal dimension (HFD), are calculated to examine different signal characteristics. Extracted features are used to distinguish the EEG data of ADHD and CS by using various machine learning algorithms.
Citation - WoS: 59
Citation - Scopus: 72
Epileptic Seizure Classifications Using Empirical Mode Decomposition and Its Derivative
(Bmc, 2020) Cura, Ozlem Karabiber; Atli, Sibel Kocaaslan; Ture, Hatice Sabiha; Akan, Aydin; Karabiber Cura, Ozlem; Kocaaslan Atli, Sibel
Background Epilepsy is one of the most common neurological disorders associated with disruption of brain activity. In the classification and detection of epileptic seizures, electroencephalography (EEG) measurements, which record the electrical activities of the brain, are frequently used. Empirical mode decomposition (EMD) and its derivative, ensemble EMD (EEMD) are recently developed methods used to decompose non-stationary and nonlinear signals such as EEG into a finite number of oscillations called intrinsic mode functions (IMFs). Our main objective in this study is to present a hybrid IMF selection method combining four different approaches (energy, correlation, power spectral distance, and statistical significance measures), and investigate the effect of selected IMFs extracted by EMD and EEMD on the classification. We have applied the proposed IMF selection approach on the classification of EEG signals recorded from epilepsy patients who are under treatment at our collaborator hospital. Multichannel EEG signals collected from epilepsy patients are decomposed into IMFs, and then IMF selection was performed. Finally, time- and spectral-domain, and nonlinear features are extracted and feature sets are created for the classification. Results The maximum classification accuracies obtained using various combinations of IMFs were 94.56%, 95.63%, 96.8%, and 96.25% for SVM, KNN, naive Bayes, and logistic regression classifiers, respectively, by using EMD analysis; whereas, the EEMD approach has provided maximum classification accuracies of 96.06%, 97%, 97%, and 96.25% for SVM, KNN, naive Bayes, and logistic regression, respectively. Classification performance with the same features obtained using direct EEG signals instead of the decomposed IMFs was worse than the aforementioned 2 approaches for every combination. Conclusion Simulation results demonstrate that the proposed IMF selection approach affects the classification results. Also, EEMD provides a robust method for feature extraction from EEG signals in order to classify pre-seizure and seizure segments.