Artificial intelligence has demonstrated great potential in the cardiac field, due to its ability to automatically extract complex patterns and achieve high diagnostic accuracy. However, traditional supervised learning relies on large, annotated datasets, which are often time-consuming and costly to obtain, limiting its scalability and broader clinical implementation. To overcome this limitation, Self-Supervised Learning (SSL) has emerged as a more efficient option, learning from unlabeled data while requiring just a minimal number of labeled samples for training.    The goal of this thesis is to optimize a SSL based algorithm to learn robust ECG representations from unlabeled data. Using SimCLR as SSL framework, the main objective is to evaluate the effectiveness of various data augmentation techniques in enabling the model to extract meaningful representations suitable for various downstream tasks, even with limited sized datasets.