Given a large enough time series signal from an ECG signal, it is possible to identify and classify heartbeats not only into normal and abnormal classes but into multiple classes including but not limited to Normal beat, Paced beat, Atrial Premature beat and Ventricular flutter as originally suggested by benchmark electrocardiogram (ECG) datasets like the MIT-BIH Arrhythmia Dataset. There are multiple approaches that target ECG classifications using Machine and Deep Learning like One Class SVM, ELM, Anogan etc. These approaches require either very high computational resources, fail to classify classes apart from normal/abnormal classes or fail to classify all classes with an equivalent or near-equivalent accuracy. With these limitations in mind, this paper proposes a deep learning approach using Convolutional Neural Networks (CNNs) to classify multiple classes of heartbeats in an efficient, effective, and generalized manner. By using the MIT-BIH Arrhythmia dataset to filter and segment individual correctly structured heartbeats, we have designed a network which can be trained on different classes of heartbeats and present robust, accurate and efficient results. The class imbalance prevalent in the MIT-BIH dataset has been dealt with using Synthetic Minority Over-sampling Technique (SMOTE). The robustness of the model is increased by adding techniques of loss minimization such as dropout and early stopping. The approach gives an accuracy of approximately 96% and an extremely short time span for class prediction(classification), i.e., less than 1 second. The results are also illustrated over multiple (10) classes to exemplify the generality of the model. We have illustrated these results over multiple (10) classes to exemplify generality of the model.