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Automatic detection and classification of electrocardiogram (ECG) signals is of great importance for diagnosis of cardiac abnormalities. A method is proposed here to classify different cardiac abnormalities like Cardiomyopathy, Myocardial infarction, Dysrhythmia, Myocardial hypertrophy and Valvular heart disease. Support Vector Machine (SVM) has been used to classify the patterns inherent in the features extracted through Continuous Wavelet Transform (CWT) of different ECG signals. CWT allows a time domain signal to be transformed into time-frequency domain such that frequency characteristics and the location of particular features in a time series may be highlighted simultaneously. Thus it allows accurate extraction of feature from non-stationary signals like ECG. SVM transforms the multi-dimensional feature space into a linearly separable feature space with the help of Kernel function. In the present work, SVM in regression mode has been successfully applied for the classification of cardiac abnormalities with good diagnostic accuracy.