Yazar "Poyraz, Mustafa" seçeneğine göre listele

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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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    Öğe
    EKG kayıtlarından tıkayıcı uyku apne sendromunun otomatik teşhisi
    (2010) Yıldız, Abdulnasır; Akın, Mehmet; Poyraz, Mustafa
    Obstructive sleep apnoea syndrome (OSAS) is a highly prevalent sleep disorder. The traditional diagnosis methods of the disorder are cumbersome and expensive. The ability to automatically identify OSAS from ECG recordings is important for clinical diagnosis and treatment. In this study, we presented a system for the automatic recognition of patients with OSA from nocturnal electrocardiogram (ECG) recordings. The presented OSA recognition system comprises of three stages. In the first stage, an algorithm based on DWT was used to analyze ECG recordings for detection ECG-derived respiration (EDR) changes. In the second stage, a FFT based Power spectral density method was used for feature extraction from EDR changes. In the third stage, using a least squares support vector machine (LS-SVM) classifier; normal subjects were separated from subjects with OSA based on obtained features. Using 10 fold cross validation method, the accuracy of proposed system was found 96.7%. The results confirmed that the presented system can aid sleep specialists in the initial assessment of patients with suspected OSA.
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    Öğe
    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.