Developing machine learning (ML) algorithms for rare disease detection, such as Brugada syndrome (BrS), can be challenging because of the lack of data. For this reason, generative adversarial networks (GAN) can be employed to perform data augmentation, to obtain a larger dataset. In order to adopt GAN-generated data in training ML algorithms, their quality and physiological reliability must be investigated. In literature, many metrics are used for this purpose, but none of them is commonly recognized as a standardized evaluation criterion. The first purpose of this work is the evaluation of ECG signals previously generated with a state-of-the-art GAN. Six ECG morphological features are used to assess the physiological reliability of the generated signals.