In this paper a novel quick automatic method is proposed for electrocardiogram (ECG). Signal classification to three classes include: the normal heart beats from the left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. After noise reduction using wavelet threshold, appropriate features are extracted from the time-voltage waves including P, Q, S, and T waves in ECG signals. Novelty of this work is utilization of fast decision based on non-parametric statistical classifier and Multi Features Data Fusion (MFDF) strategy. Two stages of MFDF include feature classification into normal and abnormal categories. Based on decision template, first stage, and second part are voting and weighting the procedure. Post processing block is added for impulsive noise reduction in order to improve the results. We emphasized on the performance and efficiency of the optimized presented algorithm and minimum cost of system learning. The accuracy of final results is reliable and well performed.

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