Narin, Ali | İşler, Yalçın | Özer, Mahmut

Other | 2014 | Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi16 ( 48 ) , pp.1 - 8

Konjestif kalp yetmezliği (KKY) vücudun ihtiyaç duyduğu miktarda kanın pompalanamaması durumudur. Bu tanıya sahip hastaların ölüm oranı çok yüksek olduğundan erken teşhis önemlidir. Bu amaçla gerçekleştirilen sınıflandırma algoritmalarının performanslarının ölçümü için farklı doğrulama yöntemleri vardır. Bu çalışmada, KKY hastalarının teşhisinde sık kullanılan 5 farklı sınıflandırıcının performans ölçümleri için k-parçalı ve birisi-dışarıda çapraz doğrulama yöntemleri denenmiştir. Her algoritma 100 defa denenerek ortalama başarım ve standart sapmaları kayıt edilmiştir. Sonuç olarak, çapraz doğrulamada kullanılan veri parçası sayısı . . .arttıkça ortalama başarımın arttığı ve başarım değişkenliğinin azaldığı tespit edilmiştir. Congestive heart failure (CHF) occurs when the heart is unable to provide sufficient pump action to maintain blood flow to meet the needs of the body. Early diagnosis is important since the mortality rate of the patients with CHF is very high. There are different validation methods to measure performances of classifier algorithms designed for this purpose. In this study, k-fold and leave-one-out cross validation methods were tested for performance measures of five distinct classifiers in the diagnosis the patients with CHF. Each algorithm was run 100 times and the average and the standard deviation of classifier performances were recorded. As a result, it was observed that average performance was enhanced and variability of performances was decreased when the number of data sections used in the cross validation method was increased Daha fazlası Daha az

Narin, Ali | İşler, Yalçın | Özer, Mahmut | Perc, Matjaž

Article | 2018 | Physica A: Statistical Mechanics and its Applications509 , pp.56 - 65

Atrial fibrillation (AF) is the most common arrhythmia type and its early stage is paroxysmal atrial fibrillation (PAF). PAF affects negatively the quality of life by causing dyspnea, chest pain, feeling of excessive fatigue, and dizziness. In this study, our aim is to predict the onset of paroxysmal atrial fibrillation (PAF) events so that patients can take precautions to prevent PAF events. We use an open data from Physionet, Atrial Fibrillation Prediction Database. We construct our approach based on the heart rate variability (HRV) analysis. Short-term HRV analysis requires 5-minute data so that each dataset was divided into 5-mi . . .nute data segments. HRV features for each segment are calculated from time-domain measures and frequency-domain measures using power spectral density estimations of fast Fourier transform, Lomb–Scargle, and wavelet transform methods. Different combinations of these HRV features are selected by Genetic Algorithm and then applied to k-nearest neighbors classification algorithm. We compute the classifier performances by the 10-fold cross-validation method. The proposed approach results in 92% sensitivity, 88% specificity and 90% accuracy in the 2.5–7.5 min time interval priors to PAF event. The proposed method results in better classification performance than the similar studies in literature. Comparing the existing studies, we propose that our approach provide better tool to predict PAF events. © 2018 Elsevier B.V Daha fazlası Daha az

Narin, Ali | Özer, Mahmut | İşler, Yalçın

Proceedings | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017 , pp.56 - 65

Paroxysmal Atrial Fibrillation (PAF) is a very common rhythm disorder that causes rapid and irregular impulses in the heart. In this study, it is aimed to determine whether patients can be warned before PAF events. 30-minute HRV data used in this study. Each piece of data was divided into 10 pieces of 5-minute parts. Time domain measurements from linear measurements of HRV and Poincare measurements from nonlinear measurements of HRV were used for each segment. Detecting performances were measured for each segment using k-nearest neighbor classifier. Particularly linear measurements have been shown to achieve up to 82% success in pre . . .dicting PAF attack and was observed that PAF attack could be detected 12,5 minutes earlier. © 2017 IEEE Daha fazlası Daha az

Narin, Ali | İşler, Yalçın | Özer, Mahmut

Proceedings | 2017 | 2016 Medical Technologies National Conference, TIPTEKNO 2016 , pp.56 - 65

Paroxysmal Atrial Fibrillation (PAF) is a very common heart disease caused by irregular impulses of atrial tissue in adult. Diagnosing in the early stages of this disorder is very important for the patients to stop the progression of the disease and to improve the life quality. In this study, it is aimed to predict the PAF event before the realization of the PAF which in 5 minutes for the PAF patients. 30-minute data used in the study were divided into 5-minute parts. Fast Fourier Transform of frequency domain measures of heart rate variability obtained easily and practically is used for each part. The statistical significances amon . . .g segments and discriminating performances of k-Nearest Neighbors classifier were obtained for each segment using these measurements. Consequently, As a result of statistical analysis, it is shown that patients may be warned 12.5 minutes earlier than a PAF attack. © 2016 IEEE Daha fazlası Daha az

Narin, Ali | Özer, Mahmut | İşler, Yalçın

Proceedings | 2013 | 2013 21ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) , pp.56 - 65

In this study, the performance of different discrimination algorithms in the analysis of heart rate variability that are used in discriminating the patients with congestive heart failure from normal subjects were investigated. Classifier algorithms of linear discriminant analysis, k-nearest neighbors, multilayer perceptron, radial basis functions and support vector machines were examined with different parameter values. As a result, the maximum classification accuracy of 91.56% was achieved by using multilayer perceptron with 11 neurons in hidden layer.

Narin, Ali | İşler, Yalçın | Özer, Mahmut

Article | 2014 | Computers in Biology and Medicine45 ( 1 ) , pp.72 - 79

In this study, the best combination of short-term heart rate variability (HRV) measures was investigated to distinguish 29 patients with congestive heart failure from 54 healthy subjects in the control group. In the analysis performed, wavelet packet transform based frequency-domain measures and several non-linear parameters were used in addition to standard HRV measures. The backward elimination and unpaired statistical analysis methods were used to select the best one among all possible combinations of these measures. Five distinct typical classifiers with different parameters were evaluated in discriminating these two groups usin . . .g the leave-one-out cross validation method. Each algorithm was tested 30 times to determine the repeatability of the results. The results imply that the backward elimination method gives better performance when compared to the statistical significance method in the feature selection stage. The best performance (82.75%, 96.29%, and 91.56% for the sensitivity, specificity, and accuracy) was obtained by using the SVM classifier with 27 selected features including non-linear and wavelet-based measures. © 2013 Elsevier Ltd Daha fazlası Daha az

Narin, Ali | İşler, Yalçın | Özer, Mahmut

Proceedings | 2018 | Proceedings - 2018 Innovations in Intelligent Systems and Applications Conference, ASYU 2018 , pp.72 - 79

The heart is very important to pump in a healthy way, but any disease that can occur in the heart has vital preventive measures. One of the most important of these diseases is Atrial Fibrillation (AF). This disease is a disturbance caused by excitations that occur outside of the sinoatrial node that occurs in the atrium of the heart. Paroxysmal Atrial Fibrillation (PAF) is the first stage of AF. Early prediction of this disease prevents the disease from passing to the other heavier stages. In this study, it was aimed to develop a warning system that warns PAF patients before an attack begins. Starting from the PAF, 99 pieces of data . . . consisting of 10 parts in 5 minutes were used. Time domain measurements and poincare plot measurements were obtained over the data. the features that best distinguish the classes have been determined by choosing a feature with a genetic algorithm. As a result, PAF can be predicted up to 7.5 minutes before the attack occurs using the selected features. © 2018 IEEE Daha fazlası Daha az

İşler, Yalçın | Narin, Ali | Özer, Mahmut

Article | 2015 | Measurement Science Review15 ( 4 ) , pp.196 - 201

Congestive heart failure (CHF) occurs when the heart is unable to provide sufficient pump action to maintain blood flow to meet the needs of the body. Early diagnosis is important since the mortality rate of the patients with CHF is very high. There are different validation methods to measure performances of classifier algorithms designed for this purpose. In this study, k-fold and leave-one-out cross-validation methods were tested for performance measures of five distinct classifiers in the diagnosis of the patients with CHF. Each algorithm was run 100 times and the average and the standard deviation of classifier performances were . . . recorded. As a result, it was observed that average performance was enhanced and the variability of performances was decreased when the number of data sections used in the cross-validation method was increased. © by Yalcin Isler 2015 Daha fazlası Daha az

İşler, Yalçın | Narin, Ali | Özer, Mahmut | Perc, Matjaž

Article | 2019 | Chaos, Solitons and Fractals118 , pp.145 - 151

In this study, we propose an automatic system to diagnose congestive heart failure using short-term heart rate variability analysis. The system involves a multi-stage classifier. The features of heart rate variability are computed from time-domain and frequency-domain measures through power spectral density estimations of different transform methods. Nonlinear heart rate variability measures are also calculated by using Poincare plot, symbolic dynamics, detrended fluctuation analysis, and sample entropy. Different combinations of heart rate variability features are selected according to their statistical significance levels and then . . . applied to the classifier. The first two stages of the classifier consist of simple perceptron classifiers that are trained by a genetic algorithm. Five different classifiers, namely k-nearest neighbors, linear discriminant analyses, multilayer perceptron, support vector machines, and radial basis function artificial neuronal network, are tested for the third stage. The proposed system results in a classification performance of an accuracy of 98.8%, specificity of 98.1%, and sensitivity of 100%. We show that our approach provides an effective and computationally efficient tool to automatically diagnose congestive heart failure patients. © 2018 Elsevier Lt Daha fazlası Daha az