Deep neural networks (DNNs) are becoming a handy tool in the healthcare field. Research work in recent years has led to the creation of solutions that can effectively support the work of medical staff. This paper presents a comparison of deep learning architectures used for the classification of cardiovascular diseases. Models were trained and tested on the PTB-XL dataset, a large publicly available electrocardiography dataset containing electrocardiography (ECG) signals with disease labels assigned by qualified cardiologists. Some architectures achieved promising accuracy despite the poor computational capabilities of the consumer-grade GPU used for training DNNs. This study compares architectures, including LSTM, CNN, and GRU layers, inspired by the ones available in scientific publications. However, all of them were modified to be trained on a local machine, which has more computational limits than the ones used in professional AI field. The task to perform is multiclass & multi-label classification of five classes occurence (four cardiovascular diseases such as myocardial infarction, ST/T change, conduction disturbance, hypertrophy and one class for outcome of a healthy patient).