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Telecardiology and Teletreatment System Design for Heart Failures Using Type-2 Fuzzy Clustering Neural Networks
Rahime Ceylan , Yüksel Özbay, Bekir Karlik
Pages - 100 - 110     |    Revised - 31-01-2011     |    Published - 08-02-2011
Volume - 1   Issue - 4    |    Publication Date - December 2010  Table of Contents
Telecardiology, type-2 fuzzy c-means clustering, ECG, neural network, diagnosis
Proper diagnosis of heart failures is critical, since the appropriate treatments are strongly dependent upon the underlying cause. Furthermore, rapid diagnosis is also critical, since the effectiveness of some treatments depends upon rapid initiation. In this paper, a new web-based telecardiology system has been proposed for diagnosis, consultation, and treatment. The aim of this implemented telecardiology system is to help to practitioner doctor, if clinic findings of patient misgive heart failures. This model consists of three subsystems. The first subsystem divides into recording and preprocessing phase. Here, electrocardiography signal is recorded from emergency patient and this recorded signal is preprocessed for detection of RR interval. The second subsystem realizes classification of RR interval. In other words, this second subsystem is to diagnosis heart failures. In this study, a combined classification system has been designed using type-2 fuzzy c-means clustering (T2FCM) algorithm and neural networks. T2FCM was used to improve performance of neural networks which was obtained very high performance accuracy to classify RR intervals of ECG signals. This proposed automated telecardiology and diagnostic system assists to practitioner doctor to diagnosis heart failures easily. Training and testing data for this diagnostic system are included five ECG signal classes. The third subsystem is consultation and teletreatment between practitioner (or family) doctor and cardiologist worked in research hospital with prepared web page (www.telekardiyoloji.com). However, opportunity of signal’s evaluation is presented to practitioner and expert doctor with prepared interfaces. T2FCM is applied to the training data for the selection of best segments in the second subsystem. A new training set formed by these best segments was classified using the neural networks classifier which has backpropagation well-known algorithm and generalized delta rule learning. Recognition accuracy rate was found as 99% using proposed Type-2 Fuzzy Clustering Neural Networks (T2FCNN) method.
CITED BY (7)  
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Dr. Rahime Ceylan
Selcuk University - Turkey
Associate Professor Yüksel Özbay
Selcuk University - Turkey
Professor Bekir Karlik
- Turkey

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