Yalcin Isler

Izmir Katip Celebi, Biomedical Engineering, Faculty Member

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Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, and the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009.
He was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000), Software Engineer (2000-2002), research assistant in Bulent Ecevit University (2002-2003) and Dokuz Eylul University (2003-2010), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012). He has been Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University since 2012. His main research interests are in the field of biomedical signal processing, medical device design, and microcontroller-based board design and he has participated in several national projects on these areas. He has been IEEE Senior Member since 2014. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014). He has been also participated as society members of Engineering in Medicine and Biology (2011-present), Signal Processing (2011-present) and Circuits & Systems (2014-present).
Phone: +905077016050

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Papers by Yalcin Isler

Comparison of the Effects of Cross-validation Methods on Determining Performances of Classifiers Used in Diagnosing Congestive Heart Failure

Measurement Science Review, 2015

Determination of the exact value of the regularization parameter in smoothness priors method with respect to the corresponding cut-off frequencies for designing filters

2013 8th International Symposium on Health Informatics and Bioinformatics, 2013

ABSTRACT A filter is an electrical network or software that alters the amplitude and/or phase cha... more ABSTRACT A filter is an electrical network or software that alters the amplitude and/or phase characteristics of a signal with respect to frequency. Recently, a new detrending method has been presented to remove the slow nonstationary trends from biomedical signals, which is equivalent to high-pass filtering that removes very low frequency components from the given signal. Although many recently published papers, related to the analysis of biomedical signals like the heart rate variability signal, have used the smoothness priors detrending method, there is no given exact relationship between the regularization parameter and the cut-off frequency of the corresponding high pass filter. In this study, we present this relationship by an empirical formula which would allow the researchers to calculate the parameter from the desired frequency response for not only a high pass filter but also other filter types.

Nöronların Tek-Bölmeli Modelinin Simülasyonu için bir Bilgisayar Yazılımı

Investigating the Effects of Wavelet Entropy in Heart Rate Variability Analysis for Diagnosing of Congestive Heart Failure

2006 IEEE 14th Signal Processing and Communications Applications, 2000

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Classification of Branch Block Beats using Higher Order Spectral Analysis and Neural Networks

2006 IEEE 14th Signal Processing and Communications Applications, 2000

In this study, it is aimed to classify branch block beats. A total number of 6170 beats related t... more In this study, it is aimed to classify branch block beats. A total number of 6170 beats related to 3 types of classes are extracted from MIT/BIH arrhythmia database and their bispectrums are calculated using TOR method. The area defined by the frequency values where the value of the energy of bispectrum is 95% of the maximum value in both

Nöronların Simülasyonu İçin Yeni Bir Bilgisayar Yazılımı

Final Prediction Error of Autoregressive Model as a New Feature in the Analysis of Heart Rate Variability

2007 IEEE 15th Signal Processing and Communications Applications, 2007

ABSTRACT The aim of this study is to offer a new heart rate variability (HRV) index that increase... more ABSTRACT The aim of this study is to offer a new heart rate variability (HRV) index that increases the accuracy in the discrimination of patients with congestive heart failure (CHF) from the control group. For this purpose, final prediction errors (FPE), which shows the quality of the conformity of autoregressive (AR) model, are calculated for model degrees from 1 to 100. Although the optimal AR model order and FPE values are widely used in the literature, they have not been used as possible HRV indices. In this study, we used FPE as an HRV feature for discriminating the patients with CHF from normal subjects and made a comparison with the other common HRV indices. As a result, we showed that FPE of AR model is a possible significant HRV feature.

Investigating Effects of Wavelet Entropy Detailed Measures in Heart Rate Variability Analysis

2007 IEEE 15th Signal Processing and Communications Applications, 2007

ABSTRACT In this study, wavelet entropy, which is calculated from the wavelet transform coefficie... more ABSTRACT In this study, wavelet entropy, which is calculated from the wavelet transform coefficients obtained from heart rate variability data, is used to distinguish the control group from the patients with congestive heart failure. Wavelet entropies are obtained from 29 patients with congestive heart failure and 54 subjects in the control group. In addition, standard heart rate variability (HRV) indices are also calculated for the whole dataset. Then, the performance of these indices in classifying these two groups is evaluated using k-Nearest Neighbor classifier and genetic algorithm. As a result, the subset of the HRV indices that increase the performance of the classifier is obtained. Using the optimal subset of HRV measures gives discrimination accuracy of 97.59%.

Diagnosis of congestive heart failure patients using Poincare measures derived from ECG signals

2009 14th National Biomedical Engineering Meeting, 2009

ABSTRACT In this study, it is investigated whether Poincare map measures can be used in discrimin... more ABSTRACT In this study, it is investigated whether Poincare map measures can be used in discriminating the patients with congestive heart failure (CHF) from normal subjects and determining the type of CHF as diastolic and systolic. Poincare map measures are calculated from ECG data that is freely available over the Internet for CHF patients and normal subjects and statistically compared using Student-t test. Similar measures are also calculated from ECG data obtained from clinics for discriminating diastolic and systolic CHF patients and statistically compared. As a result, Poincare map measures of SD1, SD2, and SD1/SD2 are found to be statistically significant to discriminate patients with CHF from normals whereas only the SD1/SD2 measure is found to be statistically significant for discriminating diastolic CHF patients from systolic ones.

Investigating the effects of the methods used in frequency domain measures in heart rate variability analysis

2008 IEEE 16th Signal Processing, Communication and Applications Conference, 2008

In this study, the effects of methods and techniques of frequency domain measures in the analysis... more In this study, the effects of methods and techniques of frequency domain measures in the analysis of heart rate variability (HRV) that are used in discriminating the patients with congestive heart failure (CHF) from normal subjects are investigated. Frequency domain HRV measures are obtained from 29 CHF patients and 54 normals. These measures are calculated using wavelet entropy in addition to standard FFT and LOMB algorithms based periodograms. Euclidean and City distance metrics are used as distance and obtained measures are investigated by k-means clustering using three different normalization techniques. As a result, the subset of the frequency domain HRV measures are high frequency components obtained from LOMB periodogram. Using the optimal subset of HRV measures gives discrimination accuracy of 81.93%.

Nöronların Tek-Bölmeli Modelinin Simülasyonu için bir Bilgisayar Yazılımı

A Computer Software For Simulating Passive Dendrite Properties Based On The Cable Theory

Pasif Dendrit Elektriksel Özelliklerinin Benzetimi İçin Kablo Kuramına Dayalı Bir Bilgisayar Yazılımı

Hodgkin-Huxley Nöron Modelinin Benzetimi için Bir Bilgisayar Yazılımı

A New Control Circuit and Computer Software for Controling Photovoltaic Systems

Nöronlarin Simulasyonu İçin Yeni Bir Bilgisayar Yazilimi

Kalp Hızı Değişkenliği Analizi Frekans Alanı Ölçümlerinde Kullanılan Yöntemlerin Etkilerinin Araştırılması

Comparision of classifier performances in diagnosing congestive heart failure using heart rate variability

ABSTRACT In this study, the performance of different discrimination algorithms in the analysis of... more ABSTRACT 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.