Classification of Electrocardiogram (ECG) signals in manual or traditional way is an area which could be improved by having such automated classification system for ECG signals. In this work, enhanced Computer-Aided Diagnosis software system is introduced for automated classification of cardiac ECG signals. Total of 480 ECG signals were taken as dataset for the purpose of this study from MIT-BIH Arrhythmia Database; those dataset signals included 96 Normal ECG signals, as well as 384 Abnormal ECG signals belonging to four types of cardiac abnormalities which are Ventricular Couplet, Ventricular Tachycardia, Ventricular Bigeminy, and Ventricular Fibrillation, where each one of those types has 96 ECG signals as well. Then, re-sampling has been done for all given signals at 360 samples per second, except for VF signals, which have been re-sampled at 250 samples per second. After that, iterative feature extraction process has been applied with the help of Classification Learner App existed in MATLAB.