Browsing by Author "Kocaaslan Atli, Sibel"

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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: 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.