Automatic detection of electrocardiogram (ECG) abnormality is a typical multi-label classification problem, which relies heavily on sufficient samples with high-quality abnormality labels for model training. Unfortunately, we often face ECG datasets with partial and incorrect labels, so how to clean a weakly-labelled datasets to obtain the clean datasets with all the correct abnormality labels is becoming a pressing concern. Under the assumption that we can have a small-sized example dataset with full and correct labels, we propose an abnormality-feature pattern (AFP) based method to automatically clean the weakly-labelled datasets, thus obtaining all the correct abnormality labels. The cleaning proceeds with two stages, clustering-based rule construction and iteration-based label cleaning. During the first stage, we construct a set of label inclusion and exclusion rules and a set of binary discriminators by exploiting the different abnormality-feature patterns which are identified through Dirichlet process mixture model (DPMM) clustering. During the second stage, we first identify the relevant abnormalities according to the label inclusion and exclusion rules, and then refine the relevant abnormalities with iterations. The AFP method takes advantage of the abnormality-feature patterns shared by the example dataset and weakly-labelled dataset, which is based on both the human intelligence and the correct label information from the weakly-labelled dataset. Further, the method stepwise removes the incorrect labels and fills in the missing ones with an iteration, thus ensuring a reliable cleaning process. The experiments on real and synthetic datasets prove the effectiveness of our method.