Wos Kapalı Erişimli Yayınlar

Permanent URI for this collectionhttps://hdl.handle.net/11727/10753

Browse

Filters

2019 - 201912020 - 20227

Settings

Author: search.filters.author.Dolu, Nazan

Search Results

Now showing 1 - 8 of 8
  • No Thumbnail Available
    Item
    Evaluation of Electrodermal Activity and Anxiety Behaviors in Diabetic Rats Given Vildagliptin and Metformin
    (2022) Shawesh, Muftah; Alshareef, Mohammed; Boyuk, Gulbahar; Yigit, Ayse Arzu; Dolu, Nazan
  • No Thumbnail Available
    Item
    Effects of Cognitive Load and State of Vigilance on Sympathetic Skin Response
    (2022) Karimi, Nazli; Dolu, Nazan; Kiziltan, Erhan; Sirinoglu, Tugce; Gundogan, Nimet Unay
  • No Thumbnail Available
    Item
  • No Thumbnail Available
    Item
    Investigation of the Effect of Boron on Sweating in Rats by Sympathetic Skin Response
    (2022) Boyuk, Gulbahar; Dolu, Nazan; Aksoy, Busra
  • No Thumbnail Available
    Item
  • No Thumbnail Available
    Item
    Combining functional near-infrared spectroscopy and EEG measurements for the diagnosis of attention-deficit hyperactivity disorder
    (2020) Guven, Aysegul; Altinkaynak, Miray; Dolu, Nazan; Izzetoglu, Meltem; Pektas, Ferhat; Ozmen, Sevgi; Demirci, Esra; Batbat, Turgay; 0000-0002-3104-7587; AAG-4494-2019
    Recently multimodal neuroimaging which combines signals from different brain modalities has started to be considered as a potential to improve the accuracy of diagnosis. The current study aimed to explore a new method for discriminating attention-deficit hyperactivity disorder (ADHD) patients and control group by means of simultaneous measurement of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Twenty-three pre-medicated combined type ADHD children and 21 healthy children were included in the study. Nonlinear brain dynamics of subjects were obtained from EEG signal using Higuchi fractal dimensions and Lempel-Ziv complexity, latency and amplitude values of P3 wave obtained from auditory evoked potentials and frontal cortex hemodynamic responses calculated from fNIRS. Lower complexity values, prolonged P3 latency and reduced P3 amplitude values were found in ADHD children. fNIRS indicated that the control subjects exhibited higher right prefrontal activation than ADHD children. Features are analyzed, looking for the best classification accuracy and finally machine learning techniques, namely Support Vector Machines, Naive Bayes and Multilayer Perception Neural Network, are introduced for EEG signals alone and for combination of fNIRS and EEG signals. Naive Bayes provided the best classification with an accuracy rate of 79.54% and 93.18%, using EEG and EEG-fNIRS systems, respectively. Our findings demonstrate that utilization of information by combining features obtained from fNIRS and EEG improves the classification accuracy. As a conclusion, our method has indicated that EEG-fNIRS multimodal neuroimaging is a promising method for ADHD objective diagnosis.
  • No Thumbnail Available
    Item
    Diagnosis of Attention Deficit Hyperactivity Disorder with combined time and frequency features
    (2020) Altinkaynak, Miray; Dolu, Nazan; Guven, Aysegul; Pektas, Ferhat; Ozmen, Sevgi; Demirci, Esra; Izzetoglu, Meltem; 0000-0002-3104-7587; AAG-4494-2019
    The aim of this study was to build a machine learning model to discriminate Attention Deficit Hyperactivity Disorder (ADHD) patients and healthy controls using information from both time and frequency analysis of Event Related Potentials (ERP) obtained from Electroencephalography (EEG) signals while participants performed an auditory oddball task. The study included 23 unmedicated ADHD patients and 23 healthy controls. The EEG signal was analyzed in time domain by nonlinear brain dynamics and morphological features, and in time-frequency domain with wavelet coefficients. Selected features were applied to various machine learning techniques including; Multilayer Perceptron, Naive Bayes, Support Vector Machines, k-nearest neighbor, Adaptive Boosting, Logistic Regression and Random Forest to classify ADHD patients and healthy controls. Longer P300 latencies and smaller P300 amplitudes were observed in ADHD patients relative to controls. In fractal dimension calculation relative to the control group, the ADHD group demonstrated reduced complexity. In addition, certain wavelet coefficients provided significantly different values in both groups. Combining these extracted features, our results indicated that Multilayer Perceptron method provided the best classification with an accuracy rate of 91.3% and a high level of reliability of concurrence (Kappa = 0.82). The results showed that combining time and frequency domain features can be a useful and discriminative for diagnostic purposes in ADHD. The study presents a supporting diagnostic tool that uses EEG signal processing and machine learning algorithms. The findings would be helpful in the objective diagnosis of ADHD. (C) 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
  • No Thumbnail Available
    Item
    Evaluation of divided attention using different stimulation models in event-related potentials
    (2019) Batbat, Turgay; Gueven, Aysegul; Dolu, Nazan; 0000-0002-3104-7587; 31352660; AAG-4494-2019
    Divided attention is defined as focusing on different tasks at once, and this is described as one of the biggest problems of today's society. Default examinations for understanding attention are questionnaires or physiological signals, like evoked potentials and electroencephalography. Physiological records were obtained using visual, auditory, and auditory-visual stimuli combinations with 48 participants-18-25-year-old university students-to find differences between sustained and divided attention. A Fourier-based filter was used to get a 0.01-30-Hz frequency band. Fractal dimensions, entropy values, power spectral densities, and Hjorth parameters from electroencephalography and P300 components from evoked potentials were calculated as features. To decrease the size of the feature set, some features, which yield less detail level for data, were eliminated. The visual and auditory stimuli in selective attention were compared with the divided attention state, and the best accuracy was found to be 88.89% on a support vector machine with linear kernel. As a result, it was seen that divided attention could be more difficult to determine from selective attention, but successful classification could be obtained with appropriate methods. Contrary to literature, the study deals with the infrastructure of attention types by working on a completely healthy and attention-high group.