In this work we present a machine learning approach that is able to classify 30 cardiac abnormalities from an arbitrary number of electrocardiogram (ECG) leads. Features extracted by a deep convolutional neural network are combined with hand-crafted features (demographic, morphological, and heart rate variability metrics) and fed into a multi-layer perceptron. We employ an Asymmetric Loss(ASL) function, which enables the model to focus on hard,but under-represented, samples. To mitigate the issue of ground-truth mislabeling and to provide robustness, we investigate the use of a self-learning label correction method that iteratively estimates correct labels during training. Leaderboard results show our team SMS+1 achieved challenge scores of 0.57 0.58 0.57.56 0.57 for twelve, six, four,three, and two-lead, respectively. Our model maintains the same diagnostic potential on both standard twelve-lead ECGs and reduced-lead ECGs