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    ECG Based Human Identification Using Logspace Grid Analysis of Second Order Difference Plot
    (Ieee, 2015) Altan, Gokhan; Kutlu, Yakup
    In this study, Second Order Difference Plot (SODP) features are used for ECG based human identification. SODP is a method that allows to determine the features with the statistical analysis of the situations obtained from distributions and the distribution of each of successive points on an unstable and linear signals. ECG records of 90 individuals in Physionet ECG-Id database are used in the study. These records are divided into segments with logarithmic grid, number of points in each segment was examined. Extracted features are classified using 2 Fold Cross Validation with kNN classifier, speed and performance of identification system were investigated. As a result, ECG based Human identification using Logspace Grid Analysis of SODP was performed in a very short time with 91.52% success.
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    ECG based Human identification using Logspace Grid Analysis of Second Order Difference Plot
    (Institute of Electrical and Electronics Engineers Inc., 2015) Altan, Gokhan; Kutlu, Yakup
    In this study, Second Order Difference Plot (SODP) features are used for ECG based human identification. SODP is a method that allows to determine the features with the statistical analysis of the situations obtained from distributions and the distribution of each of successive points on an unstable and linear signals. ECG records of 90 individuals in Physionet ECG-Id database are used in the study. These records are divided into segments with logarithmic grid, number of points in each segment was examined. Extracted features are classified using 2 Fold Cross Validation with kNN classifier, speed and performance of identification system were investigated. As a result, ECG based Human identification using Logspace Grid Analysis of SODP was performed in a very short time with 91.52% success. © 2015 IEEE.
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    A new approach to early diagnosis of congestive heart failure disease by using Hilbert-Huang transform
    (Elsevier Ireland Ltd, 2016) Altan, Gokhan; Kutlu, Yakup; Allahverdi, Novruz
    Congestive heart failure (CHF) is a degree of cardiac disease occurring as a result of the heart's inability to pump enough blood for the human body. In recent studies, coronary artery disease (CAD) is accepted as the most important cause of CHF. This study focuses on the diagnosis of both the CHF and the CAD. The Hilbert-Huang transform (HHT), which is effective on nonlinear and non-stationary signals, is used to extract the features from R-R intervals obtained from the raw electrocardiogram data. The statistical features are extracted from instinct mode functions that are obtained applying the HHT to R-R intervals. Classification performance is examined with extracted statistical features using a multilayer perceptron neural network. The designed model classified the CHF, the CAD patients and a normal control group with rates of 97.83%, 93.79% and 100%, accuracy, specificity and sensitivity, respectively. Also, early diagnosis of the CHF was performed by interpretation of the CAD with a classification accuracy rate of 97.53%, specificity of 98.18% and sensitivity of 97.13%. As a result, a single system having the ability of both diagnosis and early diagnosis of CHF is performed by integrating the CAD diagnosis method to the CHF diagnosis method. (C) 2016 Elsevier Ireland Ltd. All rights reserved.
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    A Review on Respiratory Sound Analysis using Machine Learning
    (Ieee, 2016) Altan, Gokhan; Kutlu, Yakup
    Auscultation of the respiratory sounds is an inexpensive and effective method for diagnosing cardiopulmonary disorders using lung sounds from chest and back. Nowadays, high system performances in the management of robust processes that require great attention were increased using the computer-aided analysis methods and the developments of the diagnosis system. Analysis of the respiratory sounds with computer-aided systems allows objective and useful assessments. In this study, a brief description of the abnormal respiratory sounds was presented. The main aims of the study are performing a systematic review about methods and the machine learning algorithms that are used to classify the abnormal respiratory sounds for diagnosis of cardio-pulmonary disorders and evaluating the development of possible methods on respiratory sounds in the future.