  Automatic prediction of emotion promises to revolutionise human-computer
interaction. Recent trends involve fusion of multiple data modalities - audio,
visual, and physiological - to classify emotional state. However, in practice,
collection of physiological data `in the wild' is currently limited to
heartbeat time series of the kind generated by affordable wearable heart
monitors. Furthermore, real-world applications of emotion prediction often
require some measure of uncertainty over model output, in order to inform
downstream decision-making. We present here an end-to-end deep learning model
for classifying emotional valence from unimodal heartbeat time series. We
further propose a Bayesian framework for modelling uncertainty over these
valence predictions, and describe a probabilistic procedure for choosing to
accept or reject model output according to the intended application. We
benchmarked our framework against two established datasets and achieved peak
classification accuracy of 90%. These results lay the foundation for
applications of affective computing in real-world domains such as healthcare,
where a high premium is placed on non-invasive collection of data, and
predictive certainty.
