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Automated classification of cardiac rhythms from electrocardiogram (ECG) signals is significant for diagnosis of cardiovascular dysfunctioning. A biggest challenge in automated ECG classification is to address the task's specific characteristics, such as time dependencies between observations and a strong class imbalance. To address these issues, this work proposes machine learning ensemble techniques (Random Forest, Support Vector Machine, Xgboost, Adaboost and Stacked Ensemble Classifier) and an ensemble of convolutional neural network (CNN) and bi-directional long short-term memory (Bi-LSTM) architecture for classification of cardiac arrhythmias in ECG signals. The proposed model has been trained and tested on the MIT-BIH arrhythmias database which contains total 109443 ECG beats with 90589 normal beats (NB), 8039 supraventricular beats (SB), 7236 ventricular beats (VB), 2776 fusion …