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Öğe Analysis of the relationship between tuberculosis-related mortality and nitrous oxide emission levels in the world with the environmental Kuznets curve method(Tubitak Scientific & Technological Research Council Turkey, 2022) Torun, Serife; Yilmaz, Kadir; Ozkaya, Sevket; Yosunkaya, Sebnem; Akcay, SuleBackground/aim: It was aimed to analyze the relationship between tuberculosis-related mortality and nitrous oxide emission levels in the world with the Environmental Kuznets Curve (EKC) Method. Materials and methods: WHO ICD-10 mortality list data and the World Bank Country Data (WBCD) were used between 1997 and 2017 for 12 countries. Cubic regression analysis was used for EKC Analysis. Results: The difference between male and female deaths between 1996 and 1998 has increased sharply since 1999. Male deaths consistently occurred significantly more than female deaths. There was a significant and negative correlation between Nitrous oxide emissions (% change from 1990) and tuberculosis-related deaths, whereas there were significant and positive correlations between Nitrous oxide emissions in the energy sector (% of total) and tuberculosis-related deaths (p < 0.01). EKC analysis results showed that there is a U shaped between tuberculosis-related mortality and nitrous oxide emission levels in the world. Conclusion: Research results show that the relationship between nitrous oxide change and mortality is negative in the short term and positive in the long term. Therefore, although nitrous oxide gases cause respiratory diseases and mortality, it may be possible to transform a harmful environmental factor into a positive by developing devices or methods that will convert these gases into free radicals.Öğe Automated elimination of EOG artifacts in sleep EEG using regression method(Tubitak Scientific & Technological Research Council Turkey, 2019) Dursun, Mehmet; Ozsen, Seral; Gunes, Salih; Akdemir, Bayram; Yosunkaya, SebnemSleep electroencephalogram (EEG) signal is an important clinical tool for automatic sleep staging process. Sleep EEG signal is effected by artifacts and other biological signal sources, such as electrooculogram (EOG) and electromyogram (EMG), and since it is effected, its clinical utility reduces. Therefore, eliminating EOG artifacts from sleep EEG signal is a major challenge for automatic sleep staging. We have studied the effects of EOG signals on sleep EEG and tried to remove them from the EEG signals by using regression method. The EEG and EOG recordings of seven subjects were obtained from the Sleep Research Laboratory of Meram Medicine Faculty of Necmettin Erbakan University. A dataset consisting of 58 h and 6941 epochs was used in the research. Then, in order to see the consequences of this process, we classified pure sleep EEG and artifact-eliminated EEG signals with artificial neural networks (ANN). The results showed that elimination of EOG artifacts raised the classification accuracy on each subject at a range of 1%-1.5%. However, this increase was obtained for a single parameter. This can be regarded as an important improvement if the whole system is considered. However, different artifact elimination strategies combined with different classification methods for another sleep EEG artifact may give higher accuracy differences between original and purified signals.Öğe Automatic detection of sleep spindles with the use of STFT, EMD and DWT methods(Springer London Ltd, 2018) Yucelbas, Cuneyt; Yucelbas, Sule; Ozsen, Seral; Tezel, Gulay; Kuccukturk, Serkan; Yosunkaya, SebnemSleep staging is a significant process to diagnose sleep disorders. Like other stages, several parameters are required for the determination of N-REM2 stage. Sleep spindles (SSs) are among them. In this study, a methodology was presented to automatically determine starting and ending positions of SSs. To accomplish this, short-time Fourier transform-artificial neural networks (STFT-ANN), empirical mode decomposition (EMD) and discrete wavelet transform (DWT) methods were used. Two considerable methods which were determination envelope and thresholding of the decomposed signals by EMD and DWT were also presented in this study. A database from the EEG signals of nine healthy subjects-which consisted of 100 epochs including 172 SSs in total-was prepared. According to the test results, the highest sensitivity rate was obtained as 100 and 99.42 % for EMD and DWT methods. However, the sensitivity rate for the STFT-ANN method was recorded as 55.93 %. The results indicated that the EMD method could be confidently used in the automatic determination of SSs. Thanks to this study, the sleep experts will be able to reliably find out the epochs with SSs and also know the places of SSs in these epochs, automatically. Another important point of the study was that the sleep staging process-tiring, time-consuming and high fallibility for the experts-could be performed in less time and at higher accuracy rates.Öğe Automatic sleep staging based on SVD, VMD, HHT and morphological features of single-lead ECG signal(Pergamon-Elsevier Science Ltd, 2018) Yucelbas, Sule; Yucelbas, Cuneyt; Tezel, Guley; Ozsen, Seral; Yosunkaya, SebnemElectroencephalogram (EEG) signals, which are among the primary polysomnography (PSG) signals used for sleep staging, are difficult to obtain and interpret. However, it is much easier to obtain and interpret electrocardiogram (ECG) signals. The use of ECG signals for automatic sleep staging systems could bring practicality to these systems. In this study, ECG signals were used to identify the wake (W), non-rapid eye movement (NREM) and rapid eye movement (REM) stages of the sleep data from two different databases with 17,758 epochs of 28 subjects (21 healthy subjects and 7 obstructive sleep apnea (OSA) patients) in total. Four different methods were used to extract features from these signals: Singular Value Decomposition (SVD), Variational Mode Decomposition (VMD), Hilbert Huang Transform (HHT), and Morphological method. As a result of applying the methods separately, four different data sets were obtained. The four different datasets were given to the Wrapper Subset Evaluation system with the best-first search algorithm. After the feature selection procedure, the datasets were separately classified by using the Random Forest classifier. The results were interpreted by using the essential statistical criteria. Among the methods, morphological method was the most successful and it was followed by SVD in terms of success rate for both two databases. For the first database, the mean classification accuracy rate, Kappa coefficient and mean F-measure value of the Morphological method were found as 87.11%, 0.7369, 0.869 for the healthy and 78.08%, 0.5715, 0.782 for the patient, respectively. For the second database, the same statistical measures were determined as 77.02%, 0.4308, 0.755 for the healthy and 76.79%, 0.5227, 0.759 for the patient, respectively. The performance results of the study, which is consistent with real life applications, were compared with the previous studies in this field listed in the literature. (C) 2018 Elsevier Ltd. All rights reserved.Öğe Clinical and Polysomnographic Properties of Patients with Positional Obstructive Sleep Apnea Syndrome(Aves, 2012) Yosunkaya, Sebnem; Ozturk, KayhanAim: Positional patients (PP) were patients with obstructive sleep apnea (OSA) whose apnea hypopnea index (AHI) becomes at least twice as high in the supine position as in the lateral position and an AHI that normalizes (AHI<5) in the non-supine position. Non-positional patients (NPP) were characterised by an AHI in the supine position less than twice as high as in the lateral position. In this study we aimed to compare clinical, polysomnographic and physical characteristics of patients with non-positional and positional sleep apnea. Materials and Methods: Medical records of 500 patients diagnosed with OSA syndrome (OSAS) by polysomnography (PSG) in our clinic, between 2006-2008, were retrospectively reviewed, and 230 of these were included in the study. Patients were separated into two groups according to polisomnographic results as PP and NPP. The clinical, polysomnographic and physical characteristics of the two groups were compared. Results: 112 of the 230 patients (48.6%) were classified as PP. These were younger, with lower body mass index and neck circumflex and a milder OSAS as compared to the NPP. AHI in 31% of the PP fell below 5 in the lateral recumbent position. Excessive daytime sleepiness (EDS), as assessed by the Epworth Sleepiness Scale, was lighter in the PP group. Also, frequency of complaint from year-long nasal blockage and frequency of finding turbinate hypertrophy were more common in the PP as compared to the NPP. Conclusion: In this study, basic features of patients diagnosed as positional OSAS over a two-year period were documented by grouping as PP and NPP. Some differences were identified at the physical examination, clinical and polysomnographic features of the positional and non-positional cases.Öğe Comparison of Clinical and Laboratory Findings and Computed Tomography Findings of SARS-CoV-2 Infected Patients Followed-up in a Tertiary University Hospital(Galenos Publ House, 2021) Kurt, Esma Kepenek; Kandemir, Bahar; Erayman, Ibrahim; Vatansev, Hulya; Zamani, Adil; Yosunkaya, Sebnem; Demirbas, SonerIntroduction: The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection is a pandemic, a major global health concern. In this study, it was aimed to compare the clinical, laboratory and computed tomography (CT) findings of patients with SARS-CoV-2 infection followed up in our hospital. Materials and Methods: In this study, reverse transcriptase-polymerase chain reaction (RT-PCR) positive patients hospitalized between 01.03.2020-31.05.2020 were retrospectively analyzed. Computed tomography images of the patients were grouped as typical, indeterminate, atypical, and no pneumonia based on the Radiological Society of North America. After recording patient information on SPSS, clinical and laboratory findings of the patients were analyzed by comparing them to CT findings. Results: Among 237 RT-PCR positive patients, 104 (43.9%) were female and 133 (56.1%) were male. The mean age of the patients was 50.46 +/- 17.26 (18-92) years and the mean symptom onset time of the patients was 3.75 +/- 2.72 (median: 3) days. Eighty-seven of the patients (36.7%) had contact stories. Twenty-three (9.7%) patients were healthcare professionals. Of the patients, 49.8% had a comorbid disease. The most common referral complaint was cough with 66.7%. The most common treatment that patients received was hydroxychloroquine (96.2%). Anemia was detected in 61 (25.7%) patients, leukopenia in 104 (43.9%), lymphopenia in 25 (10.5%) and thrombocytopenia in 14 (5.9%). High rates were detected for C-reactive protein (CRP) in 221 (84%) patients, ferritin in 190 (80.2%) patients, D-dimer in 144 (60.8%) patients, fibrinogen in 147 (62%) patients and sedimentation (SED) in 172 (72.6%) patients. Headache was detected higher in patients with typical pneumonia findings in thorax CT (p=0.006). A statistically significant difference wasn't detected between other symptoms and CT findings. Leukocyte and neutrophil counts, SED, CRP, ferritin, D-dimer, fibrinogen, aspartate aminotransferase, and lactate dehydrogenase (p=0.001) levels were observed to be higher in patients with typical pneumonia findings on thorax CT. Conclusion: Some laboratory parameters, especially acute phase reactants, were found to be higher in patients with typical pneumonia on thorax CT compared to patients without pneumonia. In this viral infection, patients should be evaluated together with clinical, laboratory and CT findings.Öğe Comparison of Some Spectral Analysis Methods in Detection of Sleep Spindles Using YSA(IEEE, 2015) Ozsen, Seral; Dursun, Mehmet; Yosunkaya, SebnemSleep spindle is a very determinant factor for detection of Non-REM2 stage in sleep staging studies. When it is considered that about half of the sleep consists of Non-REM2 stage, the importance of automatic sleep spindle detection stands out. In this study, three different spectral analysis method- FFT, Welch and AR have been used to estimate the frequency spectrum of sleep EEG signal and feature extraction from this spectrum has been realized. Obtained features have been used in ANN to classify EEG epochs as epochs with spindle and epochs without spindle. It has been observed that least classification error was obtained with FFT as 15.16%.Öğe Complex sleep apnea syndrome in a child with Chiari malformation type 1(Turkish J Pediatrics, 2013) Yosunkaya, Sebnem; Pekcan, SevgiThere are few reports of a patient presenting with symptoms of obstructive sleep apnea syndrome (OSAS) as the sole manifestation of Chiari malformation type 1 (CM1). In the literature, complex sleep apnea syndrome (CompSAS) was also reported as a rare condition related to CM1 patients. We report the case of a 13-year-old patient with the complaint of snoring and difficulty in breathing during sleep, but otherwise healthy. After an initial polysomnography, the patient was diagnosed with OSAS and nocturnal continuous hypoxemia. The child underwent titration to pressure of continuous positive airway pressure (CPAP); obstructive apnea improved but central apnea (i.e., CompSAS) and nocturnal continuous hypoxemia persisted. Magnetic resonance imaging led to diagnosis of CM1. Her central apnea and nocturnal hypoxemia resolved following bi-level positive airway pressure- spontaneous-timed (S/T) (BiPAP-S/T) treatment. We emphasize that the CM1 cases can admit with only breathing problems during sleep without concomitant neurological findings, and this malformation can cause central apnea resistant to CPAP.Öğe Detection of the Electrode Disconnection in Sleep Signals(IEEE, 2015) Yucelbas, Cuneyt; Ozsen, Seral; Yucelbas, Sule; Tezel, Gulay; Dursun, Mehmet; Yosunkaya, Sebnem; Kuccukturk, SerkanSleep staging process that is performed in sleep laboratories in hospitals has an important role in diagnosing some of the sleep disorders and disturbances which are seen in sleep. And also it is an indispensable method. It is usually performed by a sleep expert through examining during the night of the patients (6-8 hours) recorded Electroencephalogram (EEG), Electrooculogram (FOG), Electromyogram (EMG), electrocardiogram (ECG) and other some signals of the patients and determining the stages of sleep in different time sections named as epochs. Manual sleep staging is preferred among the sleep experts but because it is rather tiring and time consuming task, automatic sleep stage scoring studies has come to the fore. However, none of the so far made automatic sleep staging was not accepted by the experts. The most important reason is that the results of the automated systems are not desired accuracy. There are many factors that affecting the accuracy of the systems, such as noise, the inter-channel interference, excessive body movements and disconnection of electrodes. In this study, we examined the written an algorithm to be able to determine to what extent the disconnection of electrodes in EEG signal that obtained one healthy person at the sleep laboratory of Meram Medicine Faculty of Necmettin Erbakan University. According to the obtained application results, the electrodes disconnection in EEG signal could be detected maximum of 100% and minimum of 99.12% accuracy. Accordingly, based on the success achieved in the study, this algorithm is thought to contribute positively to the researchers that the work on and will work on sleep staging problems and increase the success of automatic sleep staging systems.Öğe EFFECT OF SOME POWER SPECTRAL DENSITY ESTIMATION METHODS ON AUTOMATIC SLEEP STAGE SCORING USING ARTIFICIAL NEURAL NETWORKS(Iadis, 2013) Yucelbas, Cuneyt; Ozsen, Seral; Gunes, Salih; Yosunkaya, SebnemSleep staging has an important role in diagnosing sleep disorders. It is usually done by a sleep expert through examining sleep Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG) signals of the patients and determining the stages of sleep in different time sections named as epochs. Manual sleep staging is preferred among the sleep experts but because it is rather tiring and time consuming task, automatic sleep stage scoring systems get popularity. In this study, we obtained EEG, EMG and EOG signals of four healthy people at sleep laboratory of Meram Medicine Faculty of Necmettin Erbakan University to use them in sleep staging and extracted 20 different features by using some power spectral density estimation methods which are: Fast Fourier Transform (FFT), Welch and Autoregressive (AR). We evaluated the effects of these methods on sleep staging through using ANN classifier. Comparison between these methods was done on each individual whose data were utilized separately from others. According to the results, the maximum test classification accuracy was reported as 79.72% by using of FFT method for subject1. Also, mean of test classification accuracies for all of subjects were obtained as 74.14%, 71,58 and 70.34% with use of FFT, Welch and AR, respectively.Öğe Effect of the Hilbert-Huang Transform Method on Sleep Staging(IEEE, 2017) Yucelbas, Cuneyt; Yucelbas, Sule; Ozsen, Seral; Tezel, Gulay; Yosunkaya, SebnemSleep scoring is performed by examining the recorded electroencephalogram (EEC) and some other signals recorded by a polysomnography (PSG) device. This process is considered more reliable as it is done manually by experts. However, due to the fact that experts may also be mistaken, it has led to an increase in the importance given to automatic sleep staging studies. Many methods have been tested on the signals in order to increase the success of these systems. In this study, an automatic sleep staging system was implemented using the Hilbert-Huang transformation method which is a new time-frequency analysis type. In the study, EEG signals from 5 subjects were used in the sleep laboratory. In the 5-class (Alpha, Beta, Theta, Delta and Spindle bands) applications, the highest classification success was 84.75% as a result of sequential feature selection method.Öğe Elimination of EMG Artifacts From EEG Signal in Sleep Staging(IEEE, 2016) Ozsen, Seral; Yucelbas, Cuneyt; Yucelbas, Sule; Tezel, Gulay; Yosunkaya, Sebnem; Kuccukturk, SerkanSleep staging is a tiring and time-consuming process for the experts. Thus, attention given to automatic sleep staging studies is increasing gradually. Many factors such as effects of EOG and EKG signals to EEG result in contaminated signals rather than clear recorded signals. EMG contamination to EEG is among that kind of factors. In this study, some filters and Discrete Wavelet Transform based EMG artifact elimination process were evaluated on the performance of sleep staging process. Features were extracted from cleaned EEG signals and subjected to a classifier to conduct sleep staging. By using test classification accuracy as a measure of performance, the method giving highest accuracy was tried to be found. Artificial Neural Networks was used in the applications and Discrete Wavelet Transform was found to be the method giving the highest accuracy.Öğe Evaluation of coagulation with TEG in patients diagnosed COVID-19(Tubitak Scientific & Technological Research Council Turkey, 2022) Vatansev, Hulya; Karaselek, Mehmet Ali; Yilmaz, Resul; Kuccukturk, Serkan; Topal, Ahmet; Yosunkaya, Sebnem; Kucuk, AdemBackground and aim: A high D-dimer level may indicate the risk of coagulopathy and mortality in COVID-19 patients. Thromboelastography (TEG) is a test that evaluates clot formation and fibrinolysis in real-time, unlike routine coagulation tests. The study aimed to investigate the coagulation process with TEG in patients diagnosed with COVID-19. Materials and Methods: The study was performed at our university hospital, chest diseases outpatient clinic as a cross-section study. A total of 51 patients with 23 high D-dimer levels group (HDG) and 28 low D-dimers group (LDG) were included in the study. TEG analysis was performed at the pretreatment evaluation in these two groups. Results: D-dimer and fibrinogen levels of the HDG were higher than those of the LDG (550 vs. 90 ng/mL, p < 0.001; 521 vs. 269 mg/ dL, p < 0.001, respectively). In TEG analysis, HDG's R and K values were lower than LDG, and HDG's Angle, MA, and CI values were higher than LDG (p = 0.037; p < 0.001; p < 0.001; p < 0.001; p < 0.001, respectively). ROC curve analysis suggested that the optimum TEG parameters cut-off points for thrombosis risk were as below: for K was <_2.1 min, for R was <_6.1 min, for Angle was >62 degrees, MA was 60.4 mm. Conclusion: Our study showed that the risk of thrombosis might increase in COVID-19 patients who are not hospitalized in the intensive care unit. Thrombosis risk should be investigated with TEG analysis and laboratory tests in every patient diagnosed with COVID-19, and treatment should be started for risky patients.Öğe The Evaluation of Retinal Nerve Fiber Layer Thickness in Patients with Obstructive Sleep Apnea Syndrome(Hindawi Ltd, 2013) Adam, Mehmet; Okka, Mehmet; Yosunkaya, Sebnem; Bozkurt, Banu; Kerimoglu, Hurkan; Turan, MeydanAim. To evaluate the retinal nerve fiber layer (RNFL) thickness in patients with obstructive sleep apnea syndrome (OSAS) by optical coherence tomography (OCT). Materials and Method. We studied 43 new diagnosed OSAS patients and 40 healthy volunteers. Patients underwent an overnight sleep study in an effort to diagnose and determine the severity of OSAS. RNFL analyses were performed using Stratus OCT. The average and the four-quadrant RNFL thickness were evaluated. Results. There was no difference between the average and the four-quadrant RNFL thickness in OSAS and control groups. There was no correlation between apnea-hypopnea index and intraocular pressure. Body mass index of patients with moderate and severe OSAS was significantly higher in patients with mild OSAS. Conclusion. Mean RNFL thickness did not differ between the healthy and the OSAS subjects, however, the parameters were more variable, with a larger range in OSAS patients compared to controls.Öğe Frequency of sarcopenia and associated outcomes in patients with chronic obstructive pulmonary disease(Tubitak Scientific & Technological Research Council Turkey, 2020) Demircioglu, Havva; Cihan, Fatma Goksin; Kutlu, Ruhusen; Yosunkaya, Sebnem; Zamani, AdilBackground/aim: We aimed to evaluate the prevalence of sarcopenia and associated outcomes in patients with chronic obstructive pulmonary disease (COPD). Materials and methods: This cross-sectional study was performed on 219 patients aged 50 years and over who were diagnosed with chronic obstructive pulmonary disease (COPD) according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines. The study included 196 (89.5%) male and 23 (10.5%) female patients. The mean age of the patients was 66.9 +/- 10.1 years. To diagnose sarcopenia, muscle function was determined by a gait speed test. Muscle strength was assessed with a hand dynamometer and muscle mass was measured with a bioelectrical impedance analysis device. Pulmonary function tests and six-min walking tests were also performed. The modified Medical Research Council (mMRC) dyspnoea scale was used to evaluate all the participants. Our sample consisted of sarcopenic patients at different stages (17 presarcopenic patients (7.8%), 32 patients with sarcopenia (14.6%), 65 patients with severe sarcopenia (29.7%), and 105 nonsarcopenic patients (47.9%). Results: Sarcopenia was significantly associated with age, BODE (body mass index (BMI), airflow obstruction, dyspnoea, and exercise capacity) index, GOLD spirometric classification, mMRC dyspnoea scale score, BMI, and educational status. Sarcopenia in COPD patients was firmly related to the severity of the disease and its prognosis. The prevalence of sarcopenia increased in severe and very severe COPD cases. The dyspnoea score was higher, and exercise capacities were lower in sarcopenic patients. Conclusions: Sarcopenia in COPD patients was closely related to the severity of COPD and a negative prognosis. The frequency of sarcopenia increased in severe and very severe COPD cases. Dyspnoea scores were higher and exercise capacities were lower in patients with sarcopenia. In patients with COPD, a diagnosis of sarcopenia should be considered, and preventive measures should be taken before irreversible changes develop.Öğe Identification of full-night sleep parameters using morphological features of ECG signals: A practical alternative to EEG and EOG signals(Elsevier Sci Ltd, 2024) Yucelbas, Sule; Yucelbas, Cueneyt; Tezel, Guelay; Ozsen, Seral; Yosunkaya, SebnemElectroencephalogram (EEG) signals, which are among the most important recordings used in Polysomnography for sleep staging, are more challenging and demanding than electrocardiography (ECG) signals, both in terms of acquisition and interpretation. When examining the studies of other researchers on sleep parameters in the literature, it is evident that EEG signals are predominantly used for determining arousal (AR), K-complex (Kc), and sleep spindle (Ss) parameters. Furthermore, it is understood that electrooculography (EOG) signals are employed for detecting slow eye movements (SEM) and rapid eye movements (REM) parameters.This study is a continuation of our previous research, where we used only EEG signals for Kc and Ss detection. In this study, an approach that includes ECG signals in the determination of sleep parameters to bring practicality to sleep staging studies was adopted. For this purpose, firstly, 16 morphological features were extracted from ECG recordings taken from a total of 24 subjects after various preprocessing steps. Subsequently, these data were used to work on the detection of five different sleep parameters: AR, Kc, Ss, SEM, and REM, using the Random Subspace (RaSE) ensemble learning algorithm. The results were calculated according to various statistical criteria and a classification accuracy of over 78 % was obtained in all parameters. As a result, the sleep parameters that could be determined most successfully using the ECG signal were SEM and arousal, respectively. In addition, feature elimination was performed for these datasets using Symmetric Uncertainty (SU) ranking. As a result of the reclassification process using 9 and 12 features, the effectiveness of which was determined for both datasets, respectively, significant increases were observed in the performance outputs. Experimental results have shown that ECG signals can be used as an alternative to EEG and EOG signals in the determination of full-night sleep parameters.Öğe Impact of Continuous Positive Airway Pressure Treatment on Leptin Levels in Patients with Obstructive Sleep Apnea Syndrome(Mary Ann Liebert, Inc, 2015) Yosunkaya, Sebnem; Okur, Hacer Kuzu; Can, Ummugulsum; Zamani, Adil; Kutlu, RuhusenBackground: There is growing evidence that leptin regulation is altered in obstructive sleep apnea syndrome (OSAS). Several potential mechanisms have been purported to explain how sleep apnea may alter leptin levels. We investigated whether repeated apneas, hypoxia, or excessive daytime sleepiness influenced the levels of leptin in OSAS patients. We also evaluated whether a 3-month continuous positive airway pressure (CPAP) treatment affected leptin levels in patients. Methods: Randomly selected 31 untreated, otherwise healthy male, overweight [body mass index (BMI) >25kg/m(2)] obstructive sleep apnea syndrome (OSAS) patients [apnea-hypopnea index (AHI) 15] and 25 control (AHI <5) were included in this study. To confirm the diagnosis, all subjects underwent standard polysomnography. Serum samples were taken at 07:00-08:00 a.m. after overnight fasting. The OSAS patients that had regular CPAP treatment (n=26) were re-evaulated 3 months later. Results: Leptin levels (50.517.5 grams/L in OSAS and 56.3 +/- 25.5 grams/L in controls) and lipid profiles (TC, TGs, HDL-C, and LDL-C) between patient and control groups did not differ (P>0.05). Leptin levels were not correlated with the AHI, oxygen saturation, or excessive daytime sleepiness. CPAP treatment did not significantly change the (BMI), waist and neck circumference, or leptin levels in OSAS patients. Furthermore, we found no correlation between the decrease in serum leptin levels and parameters that were improved by CPAP treatment. Conclusion: Leptin levels and lipid profile of overweight subjects with and without OSAS were not different, and our results suggest that OSAS-related parameters and CPAP treatment do not play a significant role in the serum leptin levels.Öğe The investigation of levels of endothelial cell-specific molecule, progranuline, clusterin, and human epididymis protein 4 in the differential diagnosis of malignant pleural effusions(Lippincott Williams & Wilkins, 2022) Demirbas, Soner; Yerlikaya, Fatma Humeyra; Yosunkaya, Sebnem; Can, Ummugulsum; Celalettin, KorkmazBackground:Progranulin (PGRN), endothelial cell-specific molecule-1, clusterin (CLU), and human epididymis protein 4 (HE-4) are novel proteins reported to have diagnostic and prognostic potential in lung cancer. Here, we aimed to identify the markers with high sensitivity and specificity in distinguishing malignant pleural fluids from other pleural fluids. Methods:This prospective, descriptive study was conducted at a medical faculty hospital between 2016 and 2019. The study population consisted of 90 patients <18 years of age with pleural effusion (PE). Levels of pleural fluids of PGRN, endothelial cell-specific molecule-1, CLU, and HE-4 were measured with enzyme-linked immunosorbent assay kits under the manufacturer's manual. Results:Of 90 patients, 54 were men, and 36 were women (mean age 65 +/- 16 years). Of pleural fluids investigated, 23 (25%) and 67 (74%) were transudates and exudates, respectively. Of exudates, while 27 (40%) and 19 (28%) were parapneumonic PE and tuberculous PE, respectively, 20 (29%) were malignant pleural effusion (MPE). Levels of all biomarkers in exudate fluids were found significantly higher than those of transudate fluids. CLU, HE-4, and PGRN levels in MPE were also found significantly higher than benign fluids (P < .05). Cutoff values were achieved by receiver operating characteristics analysis for CLU, HE-4, and PGRN to distinguish between malignant and benign groups. For diagnosis of MPE, the sensitivity and specificity values were found as 0.66 and 0.67 for a cutoff value of CLU of 18.29 mg/L (P = .00), as 0.76 and 0.76 for a cutoff value of HE-4 of 9.33 mg/L (P = .00), and as 0.66 and 0.67 for a cutoff value of PGRN of 105.91 mg/L (P = .001). Conclusion:HE-4 having high sensitivity and specificity can be a potential diagnostic marker in distinguishing between malignant and benign effusions, and these findings can constitute a basis for future research.Öğe A new approach to eliminating EOG artifacts from the sleep EEG signals for the automatic sleep stage classification(Springer, 2017) Dursun, Mehmet; Ozsen, Seral; Yucelbas, Cuneyt; Yucelbas, Sule; Tezel, Gulay; Kuccukturk, Serkan; Yosunkaya, SebnemInterference between EEG and EOG signals has been studied heavily in clinical EEG signal processing applications. But, in automatic sleep stage classification studies these effects are generally ignored. Thus, the objective of this study was to eliminate EOG artifacts from the EEG signals and to see the effects of this process. We proposed a new scheme in which EOG artifacts are separated from electrode or other line artifacts by a correlation and discrete wavelet transform-based rule. Also, to discriminate the situation of EEG contamination to EOG from EOG contamination to EEG, we introduced another rule and integrated this rule to our proposed method. The proposed method was also evaluated under two different circumstances: EOG-EEG elimination along the whole 0.3-35 Hz power spectrum and EOG-EEG elimination with discrete wavelet transform in 0-4 Hz frequency range. To see the consequences of EOG-EEG elimination in these circumstances, we classified pure EEG and artifact-eliminated EEG signals for each situation with artificial neural networks. The results on 11 subjects showed that pure EEG signals gave a mean classification accuracy of 60.12 %. The proposed EOG elimination process performed in 0-35 Hz frequency range resulted in a classification accuracy of 63.75 %. Furthermore, conducting EOG elimination process by using 0-4 Hz DWT detail coefficients caused this accuracy to be raised to 68.15 %. By comparing the results obtained from all applications, we concluded that an improvement about 8.03 % in classification accuracy with regard to the uncleaned EEG signals was achieved.Öğe A novel system for automatic detection of K-complexes in sleep EEG(Springer London Ltd, 2018) Yucelbas, Cuneyt; Yucelbas, Sule; Ozsen, Seral; Tezel, Gulay; Kuccukturk, Serkan; Yosunkaya, SebnemSleep staging process is applied to diagnose sleep-related disorders by sleep experts through analyzing sleep signals such as electroencephalogram (EEG), electrooculogram and electromyogram of subjects and determining the stages in 30-s-length time parts of sleep named as epochs. Subjects enter several stages during the sleep, and N-REM2 is one of them which has also the highest duration among the other stages. Approximately half of the sleep consists of N-REM2. One of the important parameters in determining N-REM2 stage is K-complex (Kc). In this study, some time and frequency analysis methods were used to determine the locations of Kcs, automatically. These are singular value decomposition (SVD), variational mode decomposition and discrete wavelet transform. The performance of them in detecting Kcs was compared. Furthermore, systems with combinations of these methods were presented with logic AND operations. The EEG recordings of seven subjects were obtained from the Sleep Research Laboratory of Necmettin Erbakan University. A database with total 359 Kcs in 320 epochs was prepared from the recordings. According to the results, the highest average recognition rate was found as 92.29% for the SVD method. Thanks to this study, the sleep experts can find out whether there were Kcs in related epochs and also know their locations in these epochs, automatically. Also, it will help automatic sleep stage classification systems.
