Yazar "Yildiz, Abdulnasir" seçeneğine göre listele

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    Abnormal Heart Sound Detection Using Ensemble Classifiers
    (Ieee, 2018) Zan, Hasan; Yildiz, Abdulnasir
    Phonocardiogram is used for ambulatory diagnostic to assess health status of heart and detect cardiovascular disease. The goal of this study is to develop automatic classification method of PCG recordings collected from different databases and recorded in a different way. For this purpose, after various time and frequency domain features are extracted from PCG recordings obtained from two databases, recordings are subjected to pre-classification in order determine which database they are obtained from. Before final classification, various time, frequency and time-frequency domain features of classified recordings are extracted. These features are fed into four different classification ensembles trained with training dataset. With final decision rule, proposed algorithm achieved an accuracy of 98.9%, a sensitivity of 93.75% and a specify of 99.5%.
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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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    Automated Recognition of Epilepsy from EEG Signals
    (Ieee, 2017) Yildirim, Mehmet; Yildiz, Abdulnasir
    In this study, it is aimed to design an automatic pattern recognition system for the detection of epilepsy which distinguishes healthy and seizure electroencephalography (EEG) signals. During the study, 100 EEG signals from patients were used during the opened eyes and healthy epileptic seizures. Each EEG signal consisting of 4096 samples was divided into 256 samples and a total of 3200 signals were obtained. The designed pattern recognition system has been developed in 3 basic parts. In the first part, the power spectral density (PSD) estimation is performed with the periodogram and Welch methods and the frequency domain information of the EEG signals is obtained. In the second part, the feature vectors are found from the frequency domain information obtained in the periodogram and Welch PSD estimation. In the third part, healthy EEG signals from the eigenvectors obtained by using K-Nearest Neighbor Algorithm (K-NN) and Support Vector Machine (SVM) classifiers are distinguished from pathological EEG signals. 5-fold cross-validation method was used in evaluating the accuracy performance of the designed system. The total classification accuracy of the system was found to be 99.66% with K-NN, 99.72% with SVM for periodogram PSD estimation and 99.72% with K-NN, 99.75% with SVM for Welch PSD estimation. The results of the pattern recognition system designed in the study are promising because they are close to the work done with different approaches in the literature. The pattern recognition system designed here is not a diagnostic tool. It is foreseen that physicians may be useful in evaluating preliminary diagnosis.
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    Classification and analysis of epileptic EEG recordings using convolutional neural network and class activation mapping
    (Elsevier Sci Ltd, 2021) Yildiz, Abdulnasir; Zan, Hasan; Said, Sherif
    Electrical bio-signals have the potential to be used in different applications due to their hidden nature and their ability to facilitate liveness detection. This paper investigates the feasibility of using the Convolutional Neural Network (CNN) to classify and analyze electroencephalogram (EEG) data with their time-frequency representations and class activation mapping (CAM) to detect epilepsy disease. Several types of pre-trained CNNs are employed for a multi-class classification task (AlexNet, GoogLeNet, ResNet-18, and ResNet-50) and their results are compared. Also, a novel convolutional neural network architecture comprised of two horizontally concatenated GoogLeNets is proposed with two inputs scalograms and spectrogram of the eplictic EEG signal. Four segment lengths (4097, 2048, 1024, and 512 sampling points) with three time-frequency representations (shorttime Fourier, Wavelet, and Hilbert-Huang transform) are statistically evaluated. The dataset used in this research is collected at the University of Bonn. The dataset is reorganized as normal, interictal, and ictal. The maximum achieved accuracies for 4097, 2048, 1024, and 512 sampling points are 100 %, 100 %, 100 %, and 99.5 % respectively. The CAM method is used to analyze discriminative regions of time-frequency representations of EEG segments and networks' decisions. This method showed CNN models used different time and frequency regions of input images for each class with correct and incorrect predictions.
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    Classification of EEG Signals Using Hilbert-Huang Transform-Based Deep Neural Networks
    (Ieee, 2019) Zan, Hasan; Yildiz, Abdulnasir; Ozerdem, Mehmet Sirac
    Epilepsy is one of the most common neurologic disease. Electroencephalogram (EEG) contains physiologic and pathological information related human nervous system. EEG signals used in this study are obtained from Bonn University, Department of Epileptology EEG database. Original database has five subsets (A, B, C, D, E). Data have been reorganized into three groups which are healthy (AB), interictal (CD) and ictal EEG signals. Furthermore, in order to examine effect of signal length on classification performance, three different lengths are used. Hilbert-Huang transform is applied to the signals and they are represented as image files. Then, generated images are fed into deep neural networks with five different structures for classification. Accuracy is calculated for all cases to asses performance of proposed method. it is clear that successful results could be obtained using Hilbert-Huang transform along with deep learning networks.
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    Enhancing vehicle fault diagnosis through multi-view sound analysis: integrating scalograms and spectrograms in a deep learning framework
    (Springer London Ltd, 2025) Akbalik, Ferit; Yildiz, Abdulnasir; Ertugrul, Omer Faruk; Zan, Hasan
    This study presents a comprehensive framework for vehicle fault diagnosis using engine sound signals, leveraging deep learning models and a multi-view approach. Traditional methods for vehicle fault diagnosis often rely on the expertise of mechanics or diagnostic tools, which can be costly, time-consuming, and may not always provide accurate results. To address these limitations, we propose CarFaultNet, a multi-view model that processes both scalograms and spectrograms simultaneously to capture complementary information from these time-frequency representations. Our approach incorporates transfer learning with pretrained convolutional neural networks, including AlexNet, GoogLeNet, ShuffleNet, SqueezeNet, and MobileNet v2, as well as CarFaultNet, which combines two MobileNet networks. The results demonstrate that CarFaultNet outperforms traditional machine learning methods and single-view deep learning models, achieving a precision of 95.32%, recall of 94.83%, F1-score of 94.99%, and accuracy of 95.00%. Class activation mapping visualizations provide valuable insights into the model's decision-making process, highlighting the regions of the input images that are most influential for the classification of different vehicle faults. By leveraging a large, diverse dataset encompassing various vehicle models and real-world operating conditions, our approach addresses the drawbacks of previous studies and demonstrates the potential of deep learning for practical and effective vehicle fault diagnosis.
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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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    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.