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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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    Öğe
    Classification of sleep apnea by using wavelet transform and artificial neural networks (vol 37, pg 1600, 2010)
    (Pergamon-Elsevier Science Ltd, 2012) Akin, Mehmet; Sezgin, Necmettin
    [Abstract Not Available]
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    Öğe
    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.
  • Küçük Resim
    Öğe
    EEG ve EMG sinyalleriyle uyuklama seviyesinin modern yöntemlerle kestirimi
    (2016) Sezgin, Necmettin; Akın, Mehmet
    Beyinde üretilen elektriksel aktivitelere Elektroansefalogram(EEG). Kas hareketlerinden dolayı kaslarda üretilen biyosinyallere ise Elekromiyogram(EMG) denir. EEG işaretleri, beynin fiziksel ve zihinsel etkinliğine göre dört ana frekans bandına sahip, spektral bileşenler ( delta, teta, alfa, beta ) içermektedir. EMG işaretlerinde ise tonik EMG'lerin düşük veya yüksek seviyeli olmaları kasların aktiviteleri ile ilgilidir. Bu bileşenlere ve kas hareketlerine bakılarak vücut hakkında ve özellikle uyanık-uyku arsında bazı yorumlar yapılabilmektedir. Bu çalışmada kullanılan EEG ve EMG sinyalleri, Ankara Gülhane Askeri Tıp Akademisi(GATA) Ruh Sağlığı ve Hastalıkları Ana bilim Dalı Uyku Laboratuarında bazı deneklerden alınmıştır. Ölçümler Grass Model-78 polisomnograf kullanılarak sürekli form kağıtlara ve aynı zamanda kişisel bir bilgisayara da kaydedilmiştir. EEG ve EMG işaretleri bilgisayara kaydedilirken 12-bit'lik bir AD çevirici ile veriler sayısal olarak bilgisayara kaydedilmiştir. Bu çalışmada, Dalgacık Dönüşümü (Wavelet Transform(WT)) ve Yapay Sinir Ağları(YSA) yöntemlerini kullanarak, EEG ile birlikte EMG sinyallerin; de kullanarak uyuklamanın kestirimi amaçlanmıştır. EEG ve EMG sinyalleri 7 saatlik kayıtlarla, sayısal işaretler 20 dakikalık bloklar halinde bilgisayara aktarılmış ve 10 saniyelik bölütlere aynştırılmıştır. Dalgacık dönüşüm yöntemiyle EEG bölütleri delta, teta, alfa ve beta gibi dört altfrekans bandına aynştırılmıştır. EEG işaretleri ile eşzamanlı alınan EMG işaretleri de filtrelenerek gürültü ve EKG artifaktlarından arındırılmış ve spektrum analizleri incelenmiştir. Bu sayede uyanık, uyuklama ve uyku karakteristiği gösteren bölütler tespit edilmiş ve yapay sinir ağları yöntemiyle eğitim yapılmıştır. Daha sonra yüzlerce EEG ve EMG bölütü YSA programıyla test edilmiştir. Bu test sonuçlarından uyanık, uyuklama ve uyku bölütleri düşük bir hatn oranıyla sezilmiştir. Sonuç olarak sadece EEG ile kestirilen uyuklama seviyesi, EMG'nin de kullanılmasıyla çol- daha az bir hatayla kestirilmiştir. İşaret işleme tekniklerinin etkin olarak kullanımı ile birlikte bu çalışma, faydalı olabilecek yenilikleri ve yapılabilecek hataları en aza indirme olanağ beraberinde getirecektir.
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    Öğe
    Estimating vigilance level by using EEG and EMG signals
    (Springer London Ltd, 2008) Akin, Mehmet; Kurt, Muhammed B.; Sezgin, Necmettin; Bayram, Muhittin
    We developed a new method for estimation of vigilance level by using both EEG and EMG signals recorded during transition from wakefulness to sleep. Previous studies used only EEG signals for estimating the vigilance levels. In this study, it was aimed to estimate vigilance level by using both EEG and EMG signals for increasing the accuracy of the estimation rate. In our work, EEG and EMG signals were obtained from 30 subjects. In data preparation stage, EEG signals were separated to its subbands using wavelet transform for efficient discrimination, and chin EMG was used to verify and eliminate the movement artifacts. The changes in EEG and EMG were diagnosed while transition from wakefulness to sleep by using developed artificial neural network (ANN). Training and testing data sets consist of the subbanded components of EEG and power density of 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 was about 98-99% while the accuracy of the previous study, which uses only EEG, was 95-96%.
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    Öğe
    Estimation of Sleep Stages by an Artificial Neural Network Employing EEG, EMG and EOG
    (Springer, 2010) Tagluk, M. Emin; Sezgin, Necmettin; Akin, Mehmet
    Analysis and classification of sleep stages is essential in sleep research. In this particular study, an alternative system which estimates sleep stages of human being through a multi-layer neural network (NN) that simultaneously employs EEG, EMG and EOG. The data were recorded through polisomnography device for 7 h for each subject. These collective variant data were first grouped by an expert physician and the software of polisomnography, and then used for training and testing the proposed Artificial Neural Network (ANN). A good scoring was attained through the trained ANN, so it may be put into use in clinics where lacks of specialist physicians.
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    Time-Frequency analysis of Snoring Sounds in Patients With Simple Snoring And OSAS
    (Ieee, 2009) Tagluk, M. Emin; Akin, Mehmet; Sezgin, Necmettin
    In recent years variety of studies has been conducted towards the identification of correlation between Obstructive Sleep Apnea Syndrome (OSAS) and snoring. The features defected from time and frequency domain analysis of snores showed the differences between simple and OSAS patients. In this study the total episodes of 1500 snore records taken from 7 simple and 14 OSAS patients were evaluated through time-frequency analysis. From the time-frequency analysis the differences, particularly from the spectral bandwidth point of view, between the two groups were identified, and using this data the method was suggested as a cost effective and simple technique to be widely used in defection of OSAS from simple patients.
  • Küçük Resim
    Öğe
    Using bispectral analysis in OSAS estimation
    (2010) Sezgin, Necmettin; Tağluk, Mehmet Emin; Akın, Mehmet; 0000-0001-7789-6376
    In this study the possibility of estimation of Obstractive Sleep Apnea Syndrome (OSAS) through electroensephalographic (EEG) signals using higher order spectra was investigated. Biological structures usually exhibit nonlinear and non-Gaussian distributed characteristics which consequently generate signals embracing nonlinear components as well as phase relations occurring between componets oscillating in different frequencies whose phase couples together over a limited period of time, so called Quadratic Phase Coupling (QPC). In this case the second order power spectrum may not reflect the real characteristics of the biological system. Therefore, the bispectrum analysis was achieved to characterize the nonlinearities and QPCs in OSAS and normal EEGs. Through this analysis the differences in OSAS EEG and normal EEG were comparatively uncovered. From the analysis it was understood that the bispectrum can be used for estimation of OSAS from patients' EEG signals.