Automatic ECG analysis assists doctors in accurate diagnosis of cardiac diseases, thereby reducing false alarms, especially for continuous monitoring. As ECG recordings are complex, automatic assessment of signal quality improves the reliability of monitoring devices. Moreover, since various cardiovascular diseases can occur concurrently in one single ECG signal, therefore multi-label classification has received much attention recently in the studies. The work of the study applies supervised machine learning to classify ECG data, using datasets to evaluate methods such as MLkNN, MLARAM, MLSVM, and binary relevance combined.