Browsing by Author "Toprak, Munire Kilinc"

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Association Between Mitral Valve Prolapse, Migraine, and White Matter Hyperintensities on Magnetic Resonance Imaging
(2018) Avci, Aynur Yilmaz; Toprak, Munire Kilinc; Lakadamyali, Liatioe; Akinci, Sinan
Objective: Migraine is linked with an elevation in vascular risk factors, ischemic stroke, and a variety of constitutional brain lesions. However, the pathogenesis of this relationship is still inexplicit. The link between cardiac diseases and comorbid migraine-ischemic stroke might be a vascular disease involving both heart and brain. In this study, an association between mitral valve prolapse (MVP), migraine, and the presence of brain white matter hyperintensities (WMHs) were evaluated among adult subjects with migraine headache devoid of any traditional vascular risk factors. Materials and Methods: Four hundred subjects (200 subjects with migraine headache, 200 healthy controls; age range 18-50 years) were incorporated in the retrospective study. Existence of a headache compatible with migraine was diagnosed according to the International Headache Society-2 criteria. The participants were devoid of any known comorbid diseases, vascular risk factors or inflammatory diseases. All patients, both those with migraine and controls were screened with echocardiography to assess for MVP and with brain magnetic resonance imaging co evaluate the presence of any WMHs. Results: The prevalence of MVP was found to be higher in the migraine group (p<0.011). The odds ratio (OR) for the presence of MVP in patients with migraine compared with controls was 2.44 [95% confidence interval (CI): 1.25-4.74; p=0.0086]. The OR for the presence of WMHs in patients with migraine compared with controls was 5.88 (95% CI: 3.42-10.10; p<0.0001). After modifying for confounding factors, multiple linear regression analyses revealed that migraine was independently and positively associated with MVP (p=0.044), tricuspid regurgitation (p=0.003), and WMHs (p<0.001), and mitral regurgitation and migraine was independently and positively connected with WMHs (p<0.005 and p<0.001, respectively). Conclusion: MVP is found CO be independently associated with migraine when compared with controls. Therefore, we suggest that MVP might have an association with migraine. Nevertheless, we could not demonstrate any correlation between MVP and WMHs. Hence, we suggest that MVP might nor be involved in the evolution of WMHs in migraine
Clinical and Radiological Characteristics of Six Patients with Tumefactive Demyelinating Lesions
(2014) Toprak, Munire Kilinc; Kibaroglu, Seda; Derle, Eda; Benli, Ulku Sibel; https://orcid.org/0000-0001-7979-0276; https://orcid.org/0000-0002-3964-268X; https://orcid.org/0000-0003-2122-1016; https://orcid.org/0000-0002-9975-3170; AAJ-8674-2021; AAJ-2956-2021; AAI-8830-2021; AAJ-4403-2021
HEADACHE CHARACTERISTICS IN GERIATRIC AGE GROUP ACCORDING TO ICHD-3 BETA VERSION
(2018) Ciftci, Eda Derle; Toprak, Munire Kilinc; Yildirim, Irem
Introduction: The aim of this study was to screen patients aged >= 65 years admitted to the Baskent University Hospital Neurology Polyclinic between 2011 and 2013 and classify them according to the International Classification of Headache Disorders, 3rd edition. Materials and Method: In total, 175 patients (126 females and 49 males) were screened. First, they were separated into primary and secondary headache groups, and the subgroup headache types were then defined within these groups according to the International Classification of Headache Disorders, 3rd edition; then, the concurrence of accompanying chronic diseases were examined. Results: In the primary headache group, 21.1% of the patients had migraine, 39.4% had tension-type headache, and 51% had trigeminal neuralgia; in the secondary headache group, 24.2% of the patients had headache attributed to hypertension, 18.2% had headache attributed to temporal arteritis, 18.2% had headache attributed to cervical pathology, and 12.1% had headache attributed to intracranial mass. Conclusion: This study was conducted to detect the most commonly seen headache types in geriatric population, and the results of this study are important in terms of increasing awareness of the evaluation of both primary and secondary headaches in geriatric patients. Because the study was conducted with a less patient population, it is important in terms of more accurately demonstrating the factors constituting the etiology of headache in this patient group.
Parkinson's Disease Monitoring from Gait Analysis via Foot-Worn Sensors
(2018) Asuroglu, Tunc; Acici, Koray; Erdas, Cagatay Berke; Toprak, Munire Kilinc; Erdem, Hamit; Ogul, Hasan; https://orcid.org/0000-0002-3821-6419; https://orcid.org/0000-0001-7979-0276; AAC-7834-2020; HDM-9910-2022; AAJ-8674-2021
Background: In Parkinson's disease (PD), neuronal loss in the substantia nigra ultimate in dopaminergic denervation of the stiratum is followed by disarraying of the movements' preciseness, automatism, and agility. Hence, the seminal sign of PD is a change in motor performance of affected individuals. As PD is a neurodegenerative disease, progression of disability in mobility is an inevitable consequence. Indeed, the major cause of morbidity and mortality among patients with PD is the motor changes restricting their functional independence. Therefore, monitoring the manifestations of the disease is crucial to detect any worsening of symptoms timely, in order to maintain and improve the quality of life of these patients. Aim: The changes in motion of patients with PD can be ascertained by the help of wearable sensors attached to the limbs of subjects. Then analysing the recorded data for variation of signals would make it possible to figure an individualized profile of the disease. Advancement of such tools would improve understanding of the disease evolution in the long term and simplify the detection of precipitous changes in gait on a daily basis in the short term. In both cases the apperception of such events would contribute to improve the clinical decision making process with reliable data. To this end, we offer here a computational solution for effective monitoring of PD patients from gait analysis via multiple foot-worn sensors. Methods: We introduce a supervised model that is fed by ground reaction force (GRF) signals acquired from these gait sensors. We offer a hybrid model, called Locally Weighted Random Forest (LWRF), for regression analysis over the numerical features extracted from input signals to predict the severity of PD symptoms in terms of Universal Parkinson Disease Rating Scale (UPDRS) and Hoehn and Yahr (H&Y) scale. From GRF signals sixteen time-domain features and seven frequency-domain features were extracted and used. Results and conclusion: An experimental analysis conducted on a real data acquired from PD patients and healthy controls has shown that the predictions are highly correlated with the clinical annotations. Proposed approach for severity detection has the best correlation coefficient (CC), mean absolute error (MAE) and root mean squared error (RMSE) values with 0.895, 4.462 and 7.382 respectively in terms of UPDRS. The regression results for H&Y Scale discerns that proposed model outperforms other models with CC, MAE and RMSE with values 0.960, 0.168 and 0.306 respectively. In classification setup, proposed approach achieves higher accuracy in comparison with other studies with accuracy and specificity of 99.0% and 99.5% respectively. Main novelty of this approach is the fact that an exact value of the symptom level can be inferred rather than a categorical result that defines the severity of motor disorders. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
A Random Forest Method to Detect Parkinson's Disease via Gait Analysis
(2017) Acici, Koray; Erdas, Cagatay Berke; Asuroglu, Tunc; Toprak, Munire Kilinc; Erdem, Hamit; Ogul, Hasan; 0000-0001-7979-0276; 0000-0003-4153-0764; 0000-0002-3821-6419; 0000-0003-3467-9923; AAJ-8674-2021; AAC-7834-2020; ITV-2441-2023; HDM-9910-2022
Remote care and telemonitoring have become essential component of current geriatric medicine. Intelligent use of wireless sensors is a major issue in relevant computational studies to realize these concepts in practice. While there has been a growing interest in recognizing daily activities of patients through wearable sensors, the efforts towards utilizing the streaming data from these sensors for clinical practices are limited. Here, we present a practical application of clinical data mining from wearable sensors with a particular objective of diagnosing Parkinson's Disease from gait analysis through a sets of ground reaction force (GRF) sensors worn under the foots. We introduce a supervised learning method based on Random Forests that analyze the multi-sensor data to classify the person wearing these sensors. We offer to extract a set of time-domain and frequency-domain features that would be effective in distinguishing normal and diseased people from their gait signals. The experimental results on a benchmark dataset have shown that proposed method can significantly outperform the previous methods reported in the literature.