Automated Emotion Recognition (AER) is the process of programatically identifying and classifying affective responses to stimuli through the analysis of physiological signals. AER has applications in interpersonal communications via digital mediums, human-computer interactions, third-party monitoring and surveillance, personal health and wellness, and in physical and mental health treatment settings. In this paper, we demonstrate AER using deep learning for automated feature extraction from ECG signals using a novel application of temporal convolutional neural networks (TCNN) to resolve the time-dependent nature of the biomedical signal data. We achieve classification accuracy of 97.8\% for valence, and 100.0\% for arousal, validated using 10-fold cross validation, meeting or exceeding the prior state of the art.