Yazar "Yosunkaya, Sebnem" seçeneğine göre listele
Listeleniyor 1 - 20 / 20
Sayfa Başına Sonuç
Sıralama seçenekleri
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 Comparison of different classifier algorithms on the automated detection of obstructive sleep apnea syndrome(SPRINGER, 2008) Polat, Kemal; Yosunkaya, Sebnem; Gunes, SalihIn this paper, we have compared the classifier algorithms including C4.5 decision tree, le artificial neural network (ANN), artificial immune recognition system (AIRS), and adaptive neuro-fuzzy inference system (ANFIS) in the diagnosis of obstructive sleep apnea syndrome (OSAS), which is an important disease that affects both the right and the left cardiac ventricle. The goal of this study was to find the best classifier model on the diagnosis of OSAS. The clinical features were obtained from Polysomnography device as a diagnostic tool for obstructive sleep apnea in patients clinically suspected of suffering this disease in this study. The clinical features are arousals index, apnea-hypopnea index (AHI), SaO(2) minimum value in stage of rapid eye movement, and percent sleep time in stage of SaO(2) intervals bigger than 89%. In our experiments, a total of 83 patients (58 with a positive OSAS (AHI > 5) and 25 with a negative OSAS such that normal subjects) were examined. The decision support systems can help to physicians in the diagnosing of any disorder or disease using clues obtained from signal or images taken from subject having any disorder. In order to compare the used classifier algorithms, the mean square error, classification accuracy, area under the receiver operating characteristics curve (AUC), and sensitivity and specificity analysis have been used. The obtained AUC values of C4.5 decision tree, ANN, AIRS, and ANFIS classifiers are 0.971, 0.96, 0.96, and 0.922, respectively. These results have shown that the best classifier system is C4.5 decision tree classifier on the diagnosis of obstructive sleep apnea syndrome.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 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 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 The Evaluation of Retinal Nerve Fiber Layer Thickness in Patients with Obstructive Sleep Apnea Syndrome(HINDAWI PUBLISHING CORPORATION, 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 Examining the Relevance with Sleep Stages of Time Domain Features of EEG, EOG, and Chin EMG signals(IEEE, 2009) Gunes, Salih; Polat, Kemal; Dursun, Mehmet; Yosunkaya, SebnemSleep staging has an important role in determining sleep disorders such as sleepiness, human fatigue etc. Sleep staging is generally done according to Rechtschaffen and Kales standard (RKS) using EEG signal obtained from PSG signals taken from patient subjects who come with any sleep disorders. Sleep stages are generally divided into three stages including awake, REM and N-REM (stage 1, stage 2, and stage 3). In this study, time domain features of EEG, EOG of right and left eyes, and chin EMG signals belonging to sleep stages were investigated and correlation between these time domain features and sleep stages was calculated. The used time domain features are mean value, standard deviation, peak value, skewness, kurtosis, and shape factor belonging to EEG, EOG of right and left eyes, and chin EMG signals. In experimental studies, PSG recordings of 3 subjects were taken and average recording time of 6.22 h, total recording time was 18.67 h. When investigated correlation coefficients, it is seen that skewness feature in time domain features of EEG signal, standard deviation feature in time domain features of EOG signals belonging to right and left eyes, and mean value feature in time domain features of chin EMG signal were more correlated with sleep stages than other features. Consequently, a feature vector can be constituted combining features determined from time domain features of EEG, EOG belonging to right and left eyes, and chin EMG signals. This obtained feature vector can be easily used in distinguishing sleep stages.Öğe Lipid peroxidation and paraoxonase activity in nocturnal cyclic and sustained intermittent hypoxia(SPRINGER HEIDELBERG, 2013) Okur, Hacer Kuzu; Pelin, Zerrin; Yuksel, Meral; Yosunkaya, SebnemObstructive sleep apnea (OSA) and chronic obstructive pulmonary disease (COPD) have been known to be associated with atherosclerosis and hypoxia which was suggested to have an important role in this process by the way of increased oxidative stress. In the present study, we aimed to evaluate the effects of nocturnal hypoxia pattern (intermittent versus sustained) on serum lipid peroxidation and paraoxonase (PON) activity. Blood collections were performed in 44 OSA, 11 non-apneic, nocturnal desaturated COPD, and 14 simple snorer patients after full-night polysomnographic recordings. Nocturnal sleep and respiratory parameters, oxygen desaturation indexes, serum malondialdehyde (MDA) levels by measuring with the help of the formation of thiobarbituric acid reactive substances (TBARS), and PON activity were assessed in all subjects. OSA and COPD patients showed nocturnal hypoxemia, with a minimum oxygen saturation (SaO(2)) in ranges of 53-92 % and 50-87 %, respectively. The mean levels of TBARS was 15.7 +/- 3.6 nmol and 15.3 +/- 3.4 nmol malondialdehyde (MDA)/ml in OSA and COPD patients, respectively, while the mean level of the control group was 4.1 +/- 1.2 nmol MDA/ml. The mean PON activity was found to be 124.2 +/- 35.5 U/l in OSA patients and 124.6 +/- 28.4 U/l in COPD patients. The mean PON activity of the control group was 269.0 +/- 135.8 U/l. The increase in TBARS levels and the decrease in PON1 levels were statistically significant in both OSA and COPD patients according to controls (p < 0.001 for TBARS as well as PON1). The results of this study revealed that both OSA and non-apneic, nocturnal desaturated COPD patients showed increased levels of lipid peroxidation and decreased PON activity despite the differences in nocturnal hypoxia pattern.Öğe A New Approach to Diagnosing of Importance Degree of Obstructive Sleep Apnea Syndrome: Pairwise AIRS and Fuzzy-AIRS Classifiers(SPRINGER, 2008) Polat, Kemal; Yosunkaya, Sebnem; Guenes, SalihArtificial Immune Recognition System (AIRS) classifier algorithm is robust and effective in medical dataset classification applications such as breast cancer, heart disease, diabetes diagnosis etc. In our previous work, we have proposed a new resource allocation mechanism called fuzzy resource allocation in AIRS algorithm both to improve the classification accuracy and to decrease the computation time in classification process. Here, AIRS and Fuzzy-AIRS classifier algorithms and one against all approach have been combined to increase the classification accuracy of obstructive sleep apnea syndrome (OSAS) that is an important disease that influences both the right and the left cardiac ventricle. The OSAS dataset consists of four classes including of normal (25 subjects), mild OSAS (AHI (Apnea and Hypoapnea Index) =5-15 and 14 subjects), moderate OSAS (AHI < 15-30 and 18 subjects), and serious OSAS (AHI < 30 and 26 subjects). In the extracting of features that is characterized the OSAS disease, the clinical features obtained from Polysomnography used diagnostic tool for obstructive sleep apnea in patients clinically suspected of suffering from this disease have been used. The used clinical features are Arousals Index (ARI), Apnea and Hypoapnea Index (AHI), SaO2 minimum value in stage of REM, and Percent Sleep Time (PST) in stage of SaO2 intervals bigger than 89%. Even though AIRS and Fuzzy-AIRS classifiers have been used in the classifying multi-class problems, theirs classification performances are low in the case of multi-class classification problems. Therefore, we have used two classes in AIRS and Fuzzy-AIRS classifiers by means of one against all approach instead of four classes comprising the healthy subjects, mild OSAS, moderate OSAS, and serious OSAS. We have applied the AIRS, Fuzzy-AIRS, AIRS with one against all approach (Pairwise AIRS), and Fuzzy-AIRS with one against all approach (Pairwise Fuzzy-AIRS) to OSAS dataset. The obtained classification accuracies are 63.41%, 63.41%, 87.19%, and 84.14% using the above methods for 200 resources, respectively. These results show that the best method for diagnosis of OSAS is the combination of AIRS and one against all approach (Pairwise AIRS).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 New Data Pre-processing on Assessing of Obstructive Sleep Apnea Syndrome: Line Based Normalization Method (LBNM)(SPRINGER-VERLAG BERLIN, 2008) Akdemir, Bayram; Guenes, Salih; Yosunkaya, SebnemSleep disorders are a very common unawareness illness among public. Obstructive Sleep Apnea Syndrome (OSAS) is characterized with decreased oxygen saturation level and repetitive upper respiratory tract obstruction episodes during full night sleep. In the present study, we have proposed a novel data normalization method called Line Based Normalization Method (LBNM) to evaluate OSAS using real data set obtained from Polysomnography device as a diagnostic tool in patients and clinically suspected of suffering OSAS. Here, we have combined the LBNM and classification methods comprising C4.5 decision tree classifier and Artificial Neural Network (ANN) to diagnose the OSAS. Firstly, each clinical feature in OSAS dataset is scaled by LBNM method in the range of [0,I]. Secondly, normalized OSAS dataset is classified using different classifier algorithms including C4.5 decision tree classifier and ANN, respectively. The proposed normalization method was compared with min-max normalization, z-score normalization, and decimal scaling methods existing in literature on the diagnosis of OSAS. LBNM has produced very promising results on the assessing of OSAS. Also, this method could be applied to other biomedical datasets.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.Öğe Obstructive Sleep Apnea Syndrome May Be a Risk Factor for the Development of Osteoporosis in Men at an Early Age?(AVES, 2015) Aslan, Saadet Han; Yosunkaya, Sebnem; Kiyici, Aysel; Sari, OktayObjective: Chronic intermittent hypoxia due to respiratory events occurring during sleep and sleep fragmentation due to arousals in obstructive sleep apnea syndrome (OSAS) may affect bone mineral density (BMD) directly or may be by causing a change in BMD through effects on hormones. We aimed to investigate whether any BMD change or any change in the level of hormones [growth hormone (GH), insulinlike growth factor-1 (IGF-1), free testosterone, total testosterone, and sex hormone-binding globulin (SHBG)], which may be related to BMD, occurs in middle-aged male patients with OSAS and compare the same with normal individuals. Material and Methods: Blood samples were collected from the participants in the morning (07.00-08.00 AM) after applying polysornnography for diagnosis. CH, IGF-1, total testosterone, and SHBG levels were measured using the enzyme-linked imrnunosorbent assay method, whereas the free testosterone level was measured using the radioimmunoassay method. BMD was measured at the femoral neck and lumbar vertebra using the Dual energy X-ray absorptiornetry (DEXA) method. Results: Between the two groups of hormones levels and T-score values statistically significant difference was not obtained. There was a statistically significant positive relationship between age and T-score femur (p<0.001) and T-score vertebra (p=0.017) and between rapid eye movement sleep time and T-score femur (p=0.032) in the OSAS group. Although patients who have BMD <-2.5 in the OSAS group (5/24) was detected to be higher than the control group (0/22), the difference was not statistically significant (p=0.05). Conclusion: In this study, we demonstrated that OSAS may not be a risk factor in the development of osteoporosis in middle-aged male patients. In addition, there was no direct relation between BMD and chronic intermittent hypoxia, apnea hypopnea index, or excessive sleepiness. Furthermore, we could not obtain any distinct relationship between OSAS and hormonal parameters that affects BMD.Öğe Pairwise ANFIS approach to determining the disorder degree of obstructive sleep apnea syndrome(SPRINGER, 2008) Polat, Kemal; Yosunkaya, Sebnem; Gunes, SalihObstructive sleep apnea syndrome (OSAS) is an important disease that affects both the right and the left cardiac ventricle. This paper presents a novel classification method called pairwise ANFIS based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and one against all method for detecting the obstructive sleep apnea syndrome. In order to extract the features related with OSAS, we have used the clinical features obtained from Polysomnography device as a diagnostic tool for obstructive sleep apnea (OSA) in patients clinically suspected of suffering from this disease. The clinical features obtained from Polysomnography Reports are Arousals Index (ARI), Apnea and Hypoapnea Index (AHI), SaO(2) minimum value in stage of REM, and Percent Sleep Time (PST) in stage of SaO(2) intervals bigger than 89%. Since ANFIS has output with one class, we have extended the output of ANFIS to multi class by means of one against all method to diagnose the OSAS that has four classes consisting of normal (25 subjects), mild OSAS (AHI=5-15 and 14 subjects), middle OSAS (AHI < 15-30 and 18 subjects), and heavy OSAS (AHI > 30 and 26 subjects). The classification accuracy, sensitivity and specifity analysis, mean square error, and confusion matrix have been used to test the performance of proposed method. The obtained classification accuracies are 82.92%, 82.92%, 85.36%, and 87.80% for each class including normal, mild OSAS, middle OSAS, and heavy OSAS using ANFIS with one against all method with 50-50% train-test split, respectively. Combining ANFIS and one against all method that is firstly proposed by us was firstly applied for diagnosing the OSAS. The proposed method has produced very promising results in the detecting the obstructive sleep apnea syndrome.Öğe Pre-determination of OSA degree using morphological features of the ECG signal(PERGAMON-ELSEVIER SCIENCE LTD, 2017) Yucelbas, Sule; Yucelbas, Cuneyt; Tezel, Gulay; Ozsen, Seral; Kuccukturk, Serkan; Yosunkaya, SebnemObstructive sleep apnea (OSA) is a very common, but a difficult sleep disorder to diagnose. Recurrent obstructions form in the airway during sleep, such that OSA can threaten a breathing capacity of patients. Clinically, continuous positive airway pressure (CPAP) is the most specific and effective treatment for this. In addition, these patients must be separated according to its degree, with CPAP treatment applied as a result. In this study, 30 OSA patients from two different databases were automatically classified using electrocardiogram (ECG) data, identified as mild, moderate, and severe. One of the databases was original recordings which had 9 OSA patients with 8303 epochs and the other one was Physionet benchmark database which had 21 patients with 20,824 epochs. Fifteen morphological features could be identified when apnea was seen, both before and after it presented. Five data groups in total for first dataset and second dataset were prepared with these features and 10-fold cross validation was used to effectively determine the test data. Then, sequential backward feature selection (SBFS) algorithm was applied to understand the more effective features. The prepared data groups were evaluated with artificial neural networks (ANN) to obtain optimum classification performance. All processes were repeated for ten times and error deviation was calculated for the accuracy. Furthermore, different classifiers which are frequently used in the literature were tested with selected features. The degree of OSA was estimated from three epochs in pre-apnea data, yielding the success rates of 97.20 +/- 2.15% and 90.18 +/- 8.11% with the SBFS algorithm for the first and second datasets, respectively. Also, SVM classifier followed ANN system in the success rates of 96.23 +/- 3.48% and 88.75 +/- 8.52% for used datasets. (C) 2017 Elsevier Ltd. All rights reserved.Öğe Primary nasopharyngeal tuberculosis in a patient with symptoms of obstructive sleep apnea(ELSEVIER SCIENCE BV, 2008) Yosunkaya, Sebnem; Ozturk, Kayhan; Maden, Emin; Cetin, Tuba[Abstract not Available]Öğe Sleep spindles recognition system based on time and frequency domain features(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Gunes, Salih; Dursun, Mehmet; Polat, Kemal; Yosunkaya, SebnemSleep spindle is the one of important components determining N-REM (Non-Rapid Eye Movement) stage 2 in the sleep stages. The symptoms of N-REM stage 2 are sleep spindle and K-complex and here sleep spindles are automatically recognized by using time and frequency domain features belonging to EEG (Electroencephalograph) signals obtained from three patient subjects. In this study, the proposed method consists of two steps. In the first step, six time domain features have been extracted from raw EEG signals. As for the extraction of frequency domain features from raw EEG signals, Welch spectral analysis has been used and applied to raw EEG signals. By this way, 65 frequency domain features have been extracted and reduced from 65 to 4 features by using statistical measures including minimum, maximum, standard deviation, and mean values. Three feature sets including only time domain, only frequency domain, and both time and frequency domain features have been used and the numbers of these feature sets are 6, 4, and 10, respectively. In the second step, artificial neural network (ANN) with LM (Levenberg-Marquardt) has been used to classify the sleep spindles evaluated beforehand by sleep expert physicians. The obtained classification accuracies for three features sets in the classification of sleep spindles are 100%, 56.86%, and 93.84% by using LM-ANN (for ten node in hidden layer). The obtained results have presented that the proposed recognition system could be confidently used in the automatic classification of sleep spindles. (C) 2010 Elsevier Ltd. All rights reserved.Öğe TOLERABILITY OF NIMESULIDE IN PATIENTS WITH HISTORIES OF ADVERSE REACTIONS TO ACETYLSALICYLIC ACID AND NONSTEROIDAL ANTI-INFLAMMATORY DRUGS(NOBEL ILAC, 2014) Tepetam, Fatma Merve; Colakoglu, Bahattin; Ozer, Faruk; Maden, Emin; Yosunkaya, Sebnem; Duman, DildarObjective: Analgesic and anti-inflammatory treatment in patients with a positive history of ASA (acetyl salicylic acid) /NSAID (non-steroidal anti-inflammatory drugs) intolerance is a significant problem in clinical practice. Therefore, there is a need to identify an alternative drug that is safe and reliable. Our aim was to determine the safety of nimesulide, a preferential COX-2 inhibitor. Material and Method: A single blind, placebo-controlled oral challenge procedure was applied to 95 patients (37 male, 58 female; with a mean age of 40.19 +/- 13.94 years) who had suffered from adverse reactions to ASA/NSAIDs. Results: According to patient histories, the majority of intolerance reactions were due to NSAIDs, and isolated cutaneous symptoms were the most common presenting symptom in 43 subjects (45.2%). While isolated respiratory symptoms were experienced in only 6 (6.3%) patients. Nimesulide was well tolerated in 90 out of 95 patients (95.2%) and only 5 of the 95 patients (4.8%) presented an adverse reaction, which was a slight urticaria. Two of the five patients were suffering from chronic urticaria, one patient had asthma and rhinosinusitis, one was atopic and one had a history of allergic reaction to a beta-lactam. Conclusion: Nimesulide can be a good option for NSAID-intolerant patients: it should first be tested in an allergy unit. However, the results of the current study need further clinical studies to evaluate the effects of higher doses or the prolonged use of nimesulide and whether nimesulide could be used in patients with asthma and with a history of chronic urticaria.
