Most of ECG analysis based emotion recognition studies use different machine learning techniques. Main problem with these methods is lack of accuracy in classifying various emotions. The proposed method uses a large public dataset to increase accuracy and implements a Convolutional Neural Network to identify emotions. ECG data signals are preprocessed to increase the number of instances and important features are extracted using feature extraction methods and then features are fed to the CNN. Three CNN models are trained to predict the valence, arousal and the dominance values of the ECG signal, which are used to finalize the emotion by mapping those values to the valence-arousal-dominance 3D plane. The classification CNN models implemented in this proposed method result in a maximum accuracy of 80.87%.