Browsing by Author "Ture, Hatice Sabiha"

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Citation - Scopus: 5
Classification of Alzheimers' Dementia by Using Various Signal Decomposition Methods
(IEEE, 2021) Cura, Ozlem Karabiber; Yilmaz, Gulce Cosku; Ture, Hatice Sabiha; Akan, Aydin
Neurological disorders may spring from any disorder in the brain or the central and autonomic nervous systems. Among the neurological disorders, while Alzheimer's disease and other dementias are the fourth-largest contributors of disabilityadjusted life years, they are the second largest contributor of deaths. In the proposed study, various signal decomposition methods such as EMD, EEMD, and DWT are presented to classify EEG segments of control subjects and Alzheimer' dementia patients. Time-domain features are calculated using selected 7 IMFs and 5 detail and approximation coefficients of DWT. Various classification techniques namely Decision Tree (DT), Support Vector Machine (SVM), k- Nearest Neighbor (kNN), and Random Forest (RF) are utilized to distinguish two groups. Simulation results demonstrate that the proposed approaches achieve outstanding validation accuracy rates.
Classification of Dementia Eeg Based on Sub-Bands Using Time-Frequency Approaches
(Institute of Electrical and Electronics Engineers Inc., 2022) Cura O.K.; Yilmaz G.C.; Ture H.S.; Akan A.; Yilmaz, Gulce Cosku; Ture, Hatice Sabiha; Cura, Ozlem Karabiber; Akan, Aydin
Alzheimer's dementia is a highly prevalent disorder among all neurological disorders. In this study, a new method based on time-Frequency (TF) representations such as Short Time Fourier Transform (STFT) and Synchrosqueezing Transform (SST) is proposed to classify EEG segments of AD patients and control subjects. Previous studies have shown that there are distinctive differences in the EEG signals of control subjects and AD patients in the low-frequency EEG subbands. Hence, in the proposed method TF representations of all EEG subbands are used for feature calculation separately. TF energy distributions obtained by SST and STFT approaches are used to calculate 13 TF features to gather distinctive information between EEG segments of control subjects and AD patients. Various classification techniques are utilized to distinguish feature sets of two the groups. Simulation results demonstrate that the proposed method achieve outstanding validation accuracy rates. © 2022 IEEE.
Citation - WoS: 14
Citation - Scopus: 21
Detection of Alzheimer's Dementia by Using Signal Decomposition and Machine Learning Methods
(World Scientific Publ Co Pte Ltd, 2022) Cura, Ozlem Karabiber; Akan, Aydin; Yilmaz, Gulce Cosku; Ture, Hatice Sabiha
Dementia is one of the most common neurological disorders causing defection of cognitive functions, and seriously affects the quality of life. In this study, various methods have been proposed for the detection and follow-up of Alzheimer's dementia (AD) with advanced signal processing methods by using electroencephalography (EEG) signals. Signal decomposition-based approaches such as empirical mode decomposition (EMD), ensemble EMD (EEMD), and discrete wavelet transform (DWT) are presented to classify EEG segments of control subjects (CSs) and AD patients. Intrinsic mode functions (IMFs) are obtained from the signals using the EMD and EEMD methods, and the IMFs showing the most significant differences between the two groups are selected by applying previously suggested selection procedures. Five-time-domain and 5-spectral-domain features are calculated using selected IMFs, and five detail and approximation coefficients of DWT. Signal decomposition processes are conducted for both 1 min and 5 s EEG segment durations. For the 1 min segment duration, all the proposed approaches yield prominent classification performances. While the highest classification accuracies are obtained using EMD (91.8%) and EEMD (94.1%) approaches from the temporal/right brain cluster, the highest classification accuracy for the DWT (95.2%) approach is obtained from the temporal/left brain cluster for 1 min segment duration.
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.