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Electrocardiogram is the most commonly used tool for the diagnosis of cardiologic diseases. In order to help cardiologists to diagnose the arrhythmias automatically, new methods for automated, computer aided ECG analysis are being developed. In this paper, a Modified Artificial Bee Colony (MABC) algorithm for ECG heart beat classification is introduced. It is applied to ECG data set which is obtained...
In this study, Artificial Bee Colony (ABC) algorithm based classifier is used. Also, in order to improve the effectiveness of ABC algorithm, some modifications are done. New method is called MABC algorithm. Both methods are applied on various real life data sets such as IRIS, WINE, PIMA, BUPA, ECG and results are compared. Those datasets are obtained from UCI Machine Learning Repository and MITBIH...
In order to diagnose the arrhythmias in electrocardiographic signals automatically, new methods for automated ECG analysis and heart beat type classification are being developed. In this paper, we proposed a new method Modified Artificial Bee Colony (MABC) algorithm for data clustering and it is applied to ECG signal analysis for arrhythmia classification. This new developed classifier based on MABC...
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