Wos Kapalı Erişimli Yayınlar
Permanent URI for this collectionhttps://hdl.handle.net/11727/10753
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Item A Case of Transient Visual Field Defect following Administration of Pfizer-BioNTech COVID-19 Vaccine(2022) Sezenoz, Almila Sarigul; Gungor, Sirel Gur; Kibaroglu, Seda; https://orcid.org/0000-0002-7030-5454; https://orcid.org/0000-0001-6178-8362; https://orcid.org/0000-0002-3964-268X; 35404749; AAJ-4860-2021; AAD-5967-2021; AAJ-2956-2021Purpose To report a case of transient visual field (VF) defect after coronavirus disease-19 (COVID-19) vaccination. Case Report A 38-year-old Caucasian, otherwise healthy female patient, presented with a complaint of vision loss in the outer quadrant in her left eye after the second dose of Pfizer (R)-BioNTech (TM) COVID-19 vaccine. The Snellen visual acuity was 20/20 in both eyes. She did not have relative afferent pupillary defect nor disturbance of color vision. Her intraocular pressures, slit lamp and fundus examinations were normal. In the VF test, a temporal hemifield defect in the left eye and a nasal peripheral VF defect in the right eye were detected. Other imaging characteristics and neurological examination were normal. She was followed without any treatment. One week later, the patient was re-evaluated and complete resolution of the VF defect was observed. Conclusion Clinicians should be aware that patients can experience transient visual symptoms following COVID-19 vaccination.Item A novel electroencephalography based approach for Alzheimer's disease and mild cognitive impairment detection(2021) Oltu, Burcu; Aksahin, Mehmet Feyzi; Kibaroglu, Seda; 0000-0002-3964-268X; AAJ-2956-2021Background and objective: Alzheimer's disease (AD) is characterized by cognitive, behavioral and intellectual deficits. The term mild cognitive impairment (MCI) is used to describe individuals whose cognitive impairment departing from their expectations for the age that does not interfere with daily activities. To diagnose these disorders, a combination of time-consuming, expensive tests that has difficulties for the target population are evaluated, moreover, the evaluation may yield subjective results. In the presented study, a novel methodology is developed for the automatic detection of AD and MCI using EEG signals. Methods: This study analyzed the EEGs of 35 subjects (16 MCI, 8 AD, 11 healthy control) with the developed algorithm. The algorithm consists of 3 methods for analysis, discrete wavelet transform(DWT), power spectral density (PSD) and coherence. In the first approach, DWT is applied to the signals to obtain major EEG sub-bands, afterward, PSD of each sub-band is calculated using Burg's method. In the second approach, interhemispheric coherence values are calculated. The variance and amplitude summation of each sub-bands' PSD and the amplitude summation of the coherence values corresponding to the major sub-bands are determined as features. Bagged Trees is selected as a classifier among the other tested classification algorithms. Data set is used to train the classifier with 5-fold cross-validation. Results: As a result, accuracy, sensitivity, and specificity of 96.5%, 96.21%, 97.96% are achieved respectively. Conclusion: In this study, we have investigated whether EEG can provide efficient clues about the neuropathology of Alzheimer's Disease and mild cognitive impairment for early and accurate diagnosis. Accordingly, a decision support system that produces reproducible and objective results with high accuracy is developed.Item Neurodegenerative disease detection and severity prediction using deep learning approaches(2021) Erdas, Cagatay Berke; Sumer, Emre; Kibaroglu, Seda; 0000-0002-3964-268X; AAJ-2956-2021; AGA-5711-2022Neurodegenerative diseases (NDDs) such as amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and Parkinson's disease (PD) can manifest themselves anatomically by degeneration in the brain as well as motor symptoms. The motor symptoms can affect walking dynamics in a disease-specific fashion; characteristically they disrupt gait. As the severity of the disease increases, walking ability deteriorates. We examined the effect of NDDs such as ALS, HD, and PD on gait and developed a convolutional long short-term memory (ConvLSTM) and threedimensional convolutional learning network (3D CNN)-based approach to detecting neurodegenerative conditions and predicting disease severity.