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In this paper, rough sets (RS) and quantum neural network (QNN) are used to recognize electrocardiogram (ECG) signals. Firstly, wavelet transform (WT) is used as a feature extraction after normalization of these signals. Then the attribute reduction of RS has been applied as preprocessor so that we could delete redundant attributes and conflicting objects from decision making table but remain efficient information lossless. We realized classification modeling and forecasting test based on QNNdoi:10.14257/ijmue.2014.9.2.37 fatcat:2hsze4chvrb3pbkm2isq2l76zy