Early detection of heart diseases/abnormalities can prolong life and enhance the quality of living through\r\nappropriate treatment; thus classifying cardiac signals will be helped to immediate diagnosing of heart beat\r\ntype in cardiac patients. The present paper utilizes the case base reasoning (CBR) for classification of ECG\r\nsignals. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia\r\nbeat and atrial fibrillation beat) obtained from the PhysioBank database was classified by the proposed\r\nCBR model. The main purpose of this article is classifying heart signals and diagnosing the type of heart\r\nbeat in cardiac patients that in proposed CBR (Case Base Reasoning) system, Training and testing data for\r\ndiagnosing and classifying types of heart beat have been used. The evaluation results from the model are\r\nshown that the proposed model has high accuracy in classifying heart signals and helps to clinical\r\ndecisions for diagnosing the type of heart beat in cardiac patients which indeed has high impact on\r\ndiagnosing the type of heart beat aided computer.
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