Yazar "Akin, Mehmet" 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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    Automated fuzzy optic disc detection algorithm using branching of vessels and color properties in fundus images
    (Elsevier, 2018) Nergiz, Mehmet; Akin, Mehmet; Yildiz, Abdulnasir; Takes, Omer
    Optic disc (OD) detection is a basic procedure for the image processing algorithms which intend to diagnose and track retinal disorders. In this study, a new OD localization approach is proposed, based on color and shape properties of OD as well as the convergence point of the main vessels. This study is comprised of two successive fundamental steps. At the first step, an algorithm finding the approximate convergent point of the vessels is used in order to roughly localize OD. At the second step, three new features are suggested and a fuzzy logic controller (FLC) whose input membership functions are designed based on these features is proposed. The proposed method is applied to the DRIVE, STARE, DIARETDB0 and DIRETDB1 datasets and the obtained results validate the improvement in the performance by attaining success rate of 100%, 91,35%, 90% and 100% respectively and detecting OD centers and contours precisely in a reasonable execution time. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
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    Classification of sleep apnea by using wavelet transform and artificial neural networks
    (Pergamon-Elsevier Science Ltd, 2010) Tagluk, M. Emin; Akin, Mehmet; Sezgin, Nemettin
    This paper describes a new method to classify sleep apnea syndrome (SAS) by using wavelet transforms and an artificial neural network (ANN) The network was trained and tested for different momentum coefficients. The abdominal respiration signals are separated into spectral components by using multi-resolution wavelet transforms. These spectral components are applied to the inputs of the artificial neural network. Then the neural network was configured to give three outputs to classify the SAS situation of the patient. 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 In this paper we aim to classify sleep apnea in one of three basic types: obstructive, central and mixed. (C) 2009 Elsevier Ltd. Ail rights reserved.
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    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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    The Comparison of Wavelet and Empirical Mode Decomposition Method in Prediction of Sleep Stages from EEG Signals
    (Ieee, 2017) Polat, Hasan; Akin, Mehmet; Ozerdem, Mehmet Sirac
    The aim of this study was to detect sleep stages of human by using EEG signals. In accordance with this purpose, discrete wavelet transforms (DWT) and empirical mode decomposition (EMD) were separately used for feature extraction. Subcomponents of EEG signals obtained by the two methods were assumed as feature vectors. Statistical parameters were used to reduce dimension of feature vectors. The same statistical parameters were used to compare performance of methods related to DWT and EMD. K nearest neighborhood (kNN) algorithm was used in classification final feature vectors that obtained EEG segments related to different sleep stages. The classification accuracies for feature vectors based on DWT and EMD were obtained as 100% and 88.13%, respectively.
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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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    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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    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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    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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    An expert system for automated recognition of patients with obstructive sleep apnea using electrocardiogram recordings
    (Pergamon-Elsevier Science Ltd, 2011) Yildiz, Abdulnasir; Akin, Mehmet; Poyraz, Mustafa
    Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder. The traditional diagnosis methods of the disorder are cumbersome and expensive. The ability to automatically identify OSA from electrocardiogram (ECG) recordings is important for clinical diagnosis and treatment. In this study, we proposed an expert system based on discrete wavelet transform (DWT), fast-Fourier transform (FFT) and least squares support vector machine (LS-SVM) for the automatic recognition of patients with OSA from nocturnal ECG recordings. Thirty ECG recordings collected from normal subjects and subjects with sleep apnea, each of approximately 8 h in duration, were used throughout the study. The proposed OSA recognition system comprises three stages. In the first stage, an algorithm based on DWT was used to analyze ECG recordings for the detection of heart rate variability (HRV) and ECG-derived respiration (EDR) changes. In the second stage, an FFT based power spectral density (PSD) method was used for feature extraction from HRV and EDR changes. Then, a hill-climbing feature selection algorithm was used to identify the best features that improve classification performance. In the third stage, the obtained features were used as input patterns of the LS-SVM classifier. Using the cross-validation method, the accuracy of the developed system was found to be 100% for using a subset of selected combination of HRV and EDR features. The results confirmed that the proposed expert system has potential for recognition of patients with suspected OSA by using ECG recordings. (C) 2011 Elsevier Ltd. All rights reserved.
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    Spectrum Estimation of Odor EEG Responses with Parametric-Nonparametric Spectral Analysis Methods
    (Ieee, 2018) Seker, Mesut; Akin, Mehmet; Ozerdem, Mehmet Sirac
    It is known that external stimulus such as visual, auditory and odor have effect on brain activity. Effects of odor stimuli, which has complex structure, on central nerveous system is lack of knowledge in literature. The goal of proposed study is to show how pleasant-unpleasant odors effect brain waves by using spectral analysis methods and discriminating different odors through statistical methods and a classifier. The EEG dataset used in study was taken from 6 participants while their eyes are closed and 4 odor (2 pleasant-2unpleasant) stimulus were applied to them using 14 chanelled EMOTIV-EPOC headset. Discrete Wavelet Transform (DWT) was used to pre-processed signals obtained from embedded filters to extract more meaningful EEG sub-bands (delta-tetha-alpha-beta). First of all, power spectrum graphics of these sub-bands was drawn using Welch's method to see how pleasant-unpleasant odor EEGs behave. Then, spectrum coefficients were gained by help of parametric (Burg, Yule-Walker, Covariance, Modified Covariance) and non-parametric (Welch's) methods. Selected feature vectors from these coefficients were classified. Selected features are min, max value and standard deviation. k-NN was chosen for classification algorithm. Avarage power spectrum analysis showed that unpleasant odor EEG has higher values than pleasant one with respect to all sub-bands. Parametric methods gave better results to discriminate odor EEGs. Burg method has highest classification rate among others.
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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.