Automatic detection and classification of cardiac disorders play a critical role in the analysis of clinical electrocardiogram (ECG). Deep learning methods are effective for automated feature extraction and have shown promising results in ECG classification. In this work, we proposed a deep spatio-temporal ECG network (ST-ECGNet) to extract robust spatio-temporal features for detecting multiple cardiac disorders from the multi-lead ECG data. The proposed ST-ECGNet combines a Convolutional Neural Network (CNN) module for extracting local spatial features, an attention module for capturing global spatial features, and a Bi-directional Gated Recurrent Unit (Bi-GRU) module for extracting temporal features from ECG data. Specifically, the attention mechanism enables our deep learning architecture to focus on the most important and useful parts of the input to make more accurate predictions. In PhysioNet/Computing in Cardiology Challenge 2021, our deep learning architecture (Team ‘Leicester-Fox’) achieved the challenge metric scores of 0.414 / 0.417 / 0.427 / 0.434 / 0.419 and accuracy of 0.481 / 0.482 / 0.495 / 0.505 / 0.484 respectively for 12 / 6 / 4 / 3 / 2 ECG-lead configurations on the validation dataset. We were not ranked in the challenge because a submission issue with the docker appeared near the deadline.