<p>The classification of cardiac-abnormality patterns with ECG data plays a crucial role in the diagnosis as well as treatment and prognosis of diseases related to the human heart. With the advent of deep learning techniques, particularly convolutional, recurrent, and generative neural networks, there has been a significant advancement in the accuracy and efficiency of cardiac-abnormality pattern classification with electrocardiogram (ECG) data. However, with the availability of multitudes of freely available multi-source ECG data today, more attempts are required to develop new models that can handle and perform well on these datasets simultaneously. In this study, an attempt is made to develop a novel deep learning classification model with multi-source ECG dataset for cardiac abnormality pattern classification. The model uses the power of Transformer networks in their ability to produce low inductive bias towards learning representations and the power of Recurrence networks to memorize a compressed representation of a sequence is that it is beneficial for generalization. The transformers are the fast-stream component due to their sensitivity to sensory input and the RNs are slow-stream component due to their long-term memory sustenance. The multi-source ECG dataset is composed of 4 different and popular 12-lead ECG datasets available publicly for research purposes. The proposed model performed satisfactorily overall on a 27-class classification scenario.</p>