Yazar "Kirbas, Gokhan" seçeneğine göre listele

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    The ANN-based computing of drowsy level
    (Pergamon-Elsevier Science Ltd, 2009) Kurt, Muhammed B.; Sezgin, Necmettin; Akin, Mehmet; Kirbas, Gokhan; Bayram, Muhittin
    We have developed a new method for automatic estimation of vigilance level by using electroencephalogram (EEG), electromyogram (EMG) and eye movement (EOG) signals recorded during transition from wakefulness to sleep. In the previous studies, EEG signals and EEG signals with EMG signals were used for estimating vigilance levels. In the present study, it was aimed to estimate vigilance levels by using EEG, EMG and EOG signals. The changes in EEG, EMG and EOG were diagnosed while transiting from wakefulness to sleep by using wavelet transform and developed artificial neural network (ANN). EEG signals were separated to its subbands using wavelet transform, LEOG (Left EOG), REOG (Right EOG) and chin EMG was used in ANN process for increasing the accuracy of the estimation rate by evaluating their tonic levels and also used in data preparation stage to verify and eliminate the movement artifacts. Then, training and testing data sets consist of the EEG subbands (delta, theta, alpha and beta); LEOG, REOG and EMG signals were applied to the ANN for training and testing the system which gives three Situations for the vigilance level of the subject: Awake, drowsy, and sleep. The accuracy of estimation is about 97-98% while the accuracy of the previous study which used only EEG was 95-96% and the study which used EEG with EMG was 98-99%. The reason of decreasing the percentage of present study according to the last study is because of the increase of the input data. (C) 2008 Elsevier Ltd. All rights reserved.
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    Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction
    (Pergamon-Elsevier Science Ltd, 2009) Yildiz, Abdulnasir; Akin, Mehmet; Poyraz, Mustafa; Kirbas, Gokhan
    This paper presents the application of adaptive neuro-fuzzy inference system (ANFIS) model for estimation of vigilance level by using electroencephalogram (EEG) signals recorded during transition from wakefulness to sleep. The developed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. This study comprises of three stages. In the first stage, three types of EEG signals (alert signal, drowsy signal and sleep signal) were obtained from 30 healthy subjects. In the second stage, for feature extraction, obtained EEG signals were separated to its sub-bands using discrete wavelet transform (DWT). Then, entropy of each sub-band was calculated using Shannon entropy algorithm. In the third stage, the ANFIS was trained with the back-propagation gradient descent method in combination with least squares method. The extracted features of three types of EEG signals were used as input patterns of the three ANFIS classifiers. In order to improve estimation accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The performance of the ANFIS model was tested using the EEG data obtained from 12 healthy subjects that have not been used for the training. The results confirmed that the developed ANFIS classifier has potential for estimation of vigilance level by using EEG signals. (C) 2008 Elsevier Ltd. All rights reserved.
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    Association between burnout, anxiety and insomnia in healthcare workers: a cross-sectional study Burnout, anxiety and insomnia in healthcare workers
    (Routledge Journals, Taylor & Francis Ltd, 2022) Aydin Guclu, Ozge; Karadag, Mehmet; Akkoyunlu, Muhammed Emin; Acican, Turan; Sertogullarindan, Bunyamin; Kirbas, Gokhan; Selimoglu Sen, Hatice
    All healthcare workers (HCWs) encounter stress during in their working lives, and are constantly exposed to adverse conditions. The present study evaluates the relationship between burnout syndrome, anxiety levels and insomnia severity among healthcare workers, who mostly work in shifts. The Maslach Burnout Inventory, the Insomnia Severity Index and the Beck Anxiety Inventory were used to measure burnout, insomnia severity and anxiety status, respectively. This cross sectional study included a total of 1,011 HCWs and 679 (67.2%) of the study respondents were women. The respondents were aged 20-72, with a mean age of 35.67 +/- 8.61 years. Fifty-eight percent (n = 589) of the participants were rotating shift workers. Working on-call led to a significant difference in all burnout parameters (for each, <0.001). Age and on-call duty were seen to lead to a significant difference in the severity of insomnia (p = 0.028, p < 0.001, respectively). The total ISI score was found to be statistically significant positively correlated with the MBI subscales and the total BAI score (for each, <0.001). An increased awareness of the impact of sleep deprivation, burnout and anxiety among HCWs and meaningful interventions promoting change within the healthcare system are needed.
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    The correlation analysis between airflow and oxygen saturation in obstructive sleep apnea events using correlation function
    (Ieee, 2007) Sezgin, Necmettin; Kirbas, Gokhan; Akin, Mehmet
    Diagnosis of Sleep apnea syndrome (SAS) is currently performed by a full night polysomnography study at sleep laboratories. The majority of apnea patients are treated by constant positive airway pressure (CPAP) device. The apnea can be broadly classified into three types: obstructive sleep apnea (OSA), central sleep apnea (CSA) and mixed sleep apnea (MSA). During OSA, the airway is blocked while respiratory efforts continue. During CSA the airway is open, however there are no respiratory efforts. There is an efficient correlation between airflow and SaO(2) in sleep apnea events. In this paper, it is aimed to find the correlation degree between airflow and oxygen saturation by using cross corelation function in obstructive sleep apnea events. In the future studies the correlation will be able to detect the sleep apnea and controlling CPAP device automatically as an intelligent system.
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    Estimation of Alertness Level by Using Wavelet Transform Method and Entropy
    (Ieee, 2009) Yildiz, Abdulnasir; Akin, Mehmet; Poyraz, Oguz; Kirbas, Gokhan
    In this study, developing of a different model estimating of alertness level has been studied by using electroencephalogram (EEG) signals recorded during transition front wakefulness to sleep. Developed model is composed of discrete wavelet transform-entropy pair (feature extractor) and multilayer perceptron neural network (classifier). This study, basically, comprises of two stages. In the first stage, EEG signals taken from 10 healty subjects were separated as alert, drowsy, and sleep signals in the form of 5 s epochs with the aid of expert doctor. In the second stage, feature vectors Delta, Theta, Alpha, and Beta sub-bands of EEG signals separated into epochs were obtained by using discrete wavelet transform. After then, entropy was used to reduce dimensions of feature vectors. Obtained vectors were chosen as input feature vectors of multilayer neural network which used as classifier. Total classification accuracy obtained in the test results of proposed model showed that model can be used in the estimating of vigilance level.
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    Evaluation of Anthropometric and Metabolic Parameters in Obstructive Sleep Apnea
    (Hindawi Ltd, 2015) Yildirim, Yasar; Yilmaz, Sureyya; Guven, Mehmet; Kilinc, Faruk; Kara, Ali Veysel; Yilmaz, Zulfukar; Kirbas, Gokhan
    Aims. Sleep disorders have recently become a significant public health problem worldwide and have deleterious health consequences. Obstructive sleep apnea (OSA) is the most common type of sleep-related breathing disorders. We aimed to evaluate anthropometric measurements, glucose metabolism, and cortisol levels in patients with obstructive sleep apnea (OSA). Materials and Methods. A total of 50 patients with a body mass index >= 30 and major OSA symptoms were included in this study. Anthropometric measurements of the patients were recorded and blood samples were drawn for laboratory analysis. A 24-hour urine sample was also collected from each subject for measurement of 24-hour cortisol excretion. Patients were divided equally into 2 groups according to polysomnography results: control group with an apnea-hypopnea index (AHI) <5 (n = 25) and OSA group with an AHI = 5 (n = 25). Results. Neck and waist circumference, fasting plasma glucose, HbA1c, late-night serum cortisol, morning serum cortisol after 1 mg dexamethasone suppression test, and 24-hour urinary cortisol levels were significantly higher in OSA patients compared to control subjects. Newly diagnosed DM was more frequent in patients with OSA than control subjects (32% versus 8%, p = 0.034). There was a significant positive correlation between AHI and neck circumference, glucose, and latenight serum cortisol. Conclusions. Our study indicates that increased waist and neck circumferences constitute a risk for OSA regardless of obesity status. In addition, OSA has adverse effects on endocrine function and glucose metabolism.
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    The results of OSAS patients in our clinic
    (European Respiratory Soc Journals Ltd, 2018) Yilmaz, Sureyya; Sen, Haidce Selimoglu; Taylan, Mahsuk; Demir, Melike; Kirbas, Gokhan
    [Abstract Not Available]
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    The role of inflammatory biomarkers in obstructive sleep apnea syndrome
    (European Respiratory Soc Journals Ltd, 2018) Yilmaz, Sureyya; Sen, Hadice Selimoglu; Taylan, Mahsuk; Demir, Melike; Topcu, Fusun; Senyigit, Abdurrahman; Kirbas, Gokhan
    [Abstract Not Available]
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    Serum bicarbonate level improves specificity of Berlin Sleep Questionnaire for obstructive sleep apnea
    (Taylor & Francis Ltd, 2020) Dursun, Mazlum; Selimoglu Sen, Hadice; Yilmaz, Sureyya; Demir, Melike; Kirbas, Gokhan; Taylan, Mahsuk
    Several questionnaires have been developed to assist the diagnostic process in obstructive sleep apnea syndrome (OSAS). Berlin Sleep Questionnaire (BSQ) represents a validated screening tool for OSAS. Totally 450 patients admitted to the Sleep Center at Dicle University Medical Faculty were included prospectively. A risk analysis was performed for presence of OSAS using the BSQ. Arterial blood gas measurements were performed including bicarbonate (HCO3) level. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of BSQ for presence of OSAS and severe OSAS were determined. In patients with arterial HCO3>24.94 mEq/L; sensitivity, specificity, PPV and NPV, of the BSQ were 93.04, 57.1, 98.3, and 23.5%, respectively. The addition of arterial HCO(3)value increased the sensitivity of the BSQ in detecting OSAS patients. Although the cost of sleep studies is high for false positives from the BSQ plus arterial HCO3 level, this cost should be compared with the loss of work efficiency and severe healthcare costs of undiagnosed cases in the future. Therefore, finding possible OSAS cases in primary care health centers is important and adding serum HCO3 value to BSQ questionnaire may contribute to this topic.