Temporal Convolutional Networks (TCNs) are among the most effective algorithms to deal with time series data To a time series can be also given the structure of directed graph, opening the doors to the usage of Graph Neural Networks (GNNs) in this context. In this paper we develop two distinct Geometric Deep Learning models that merge the capabilities of ordinary TCNs with the ones of GNNs, a supervised classifier and an autoencoder-like model that we apply to solve a quality detection problem on electrocardiogram signals.