Yazar "Oezbay, Yueksel" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Öğe
    Application of complex discrete wavelet transform in classification of Doppler signals using complex-valued artificial neural network
    (ELSEVIER, 2008) Ceylan, Murat; Ceylan, Rahime; Oezbay, Yueksel; Kara, Sadik
    Objective: In biomedical signal classification, due to the huge amount of data, to compress the biomedical waveform data is vital. This paper presents two different structures formed using feature extraction algorithms to decrease size of feature set in training and test data. Materials and methods: The proposed structures, named as wavelet transform-complex-valued artificial neural network (WT-CVANN) and complex wavelet transform-complex-valued artificial neural network (CWT-CVANN), use real and complex discrete wavelet transform for feature extraction. The aim of using wavelet transform is to compress data and to reduce training time of network without decreasing accuracy rate. In this study, the presented structures were applied to the problem of classification in carotid arterial Doppler ultrasound signals. Carotid arterial Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group included 22 mates and 16 females with an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal (tower extremity) angiographies (mean age, 59 years; range, 48-72 years). Healthy volunteers were young non-smokers who seem to not bear any risk of atherosclerosis, including 28 mates and 12 females (mean age, 23 years; range, 19-27 years). Results and conclusion: Sensitivity, specificity and average detection rate were calculated for comparison, after training and test phases of all structures finished. These parameters have demonstrated that training times of CVANN and real-valued artificial neural network (RVANN) were reduced using feature extraction algorithms without decreasing accuracy rate in accordance to our aim. (C) 2008 Elsevier B.V. All rights reserved.
  • Küçük Resim Yok
    Öğe
    A New Approach to Detection of ECG Arrhythmias: Complex Discrete Wavelet Transform Based Complex Valued Artificial Neural Network
    (SPRINGER, 2009) Oezbay, Yueksel
    This paper presents the new automated detection method for electrocardiogram (ECG) arrhythmias. The detection system is implemented with integration of complex valued feature extraction and classification parts. In feature extraction phase of proposed method, the feature values for each arrhythmia are extracted using complex discrete wavelet transform (CWT). The aim of using CWT is to compress data and to reduce training time of network without decreasing accuracy rate. Obtained complex valued features are used as input to the complex valued artificial neural network (CVANN) for classification of ECG arrhythmias. Ten types of the ECG arrhythmias used in this study were selected from MIT-BIH ECG Arrhythmias Database. Two different classification tasks were performed by the proposed method. In first classification task (CT-1), whether CWT-CVANN can distinguish ECG arrhythmia from normal sinus rhythm was examined one by one. For this purpose, nine classifiers were improved and executed in CT-1. Second classification task (CT-2) was to recognize ten different ECG arrhythmias by one complex valued classifier with ten outputs. Training and test sets were formed by mixing the arrhythmias in a certain order. Accuracy rates were obtained as 99.8% (averaged) and 99.2% for the first and second classification tasks, respectively. All arrhythmias in training and test phases were classified correctly for both of the classification tasks.
  • Küçük Resim Yok
    Öğe
    A new neural network with adaptive activation function for classification of ECG Arrhythmias
    (SPRINGER-VERLAG BERLIN, 2007) Tezel, Guelay; Oezbay, Yueksel
    This study presents a comparative study of the classification accuracy of ECG signals using a well-known neural network architecture named multilayered perception (MLP) with backpropagation training algorithm, and a new neural network with adaptive activation function (AAFNN) for classification of ECG arrhythmias. The ECG signals are taken from MIT-BIH ECG database, which are used to classify ten different arrhythmias for training. These are normal sinus rhythm, sinus bradycardia, ventricular tachycardia, sinus arrhythmia, atrial premature contraction, paced beat, right bundle branch block, left bundle branch block, atrial fibrillation and atrial flutter. For testing, the proposed structures were trained by backpropagation algorithm. Both of them tested using experimental ECG records of 10 patients (7 male and 3 female, average age is 33.8 +/- 16.4). The results show that neural network with adaptive activation function is more suitable for biomedical data like as ECG in the classification problems and training speed is much faster than neural network with fixed sigmoid activation function