  Automated analysis of 12-lead electrocardiogram (ECG) plays a crucial role in
the early screening and management of cardiovascular diseases (CVDs). In
practice, it is common to see multiple co-occurring cardiac disorders, i.e.,
multi-label or multimorbidity in patients with CVDs, which increases the risk
for mortality. Most current research focuses on the single-label ECG
classification, i.e., each ECG record corresponds to one cardiac disorder,
ignoring ECG records with multi-label phenomenon. In this paper, we propose an
ensemble of attention-based temporal convolutional neural network (ATCNN)
models for the multi-label classification of 12-lead ECG records. Specifically,
a set of ATCNN-based single-lead binary classifiers are trained one for each
cardiac disorder, and the predictions from these classifiers with simple
thresholding generate the final multi-label decisions. The ATCNN contains a
stack of TCNN layers followed by temporal and spatial attention layers. The
TCNN layers operate at different dilation rates to represent the multi-scaled
pathological ECG features dynamics, and attention layers emphasize/reduce the
diagnostically relevant/redundant 12-lead ECG information. The proposed
framework is evaluated on the PTBXL-2020 dataset and achieved an average
F1-score of 76.51%. Moreover, the spatial and temporal attention weights
visualization provides the optimal ECG-lead subset selection for each disease
and model interpretability results, respectively. The improved performance and
interpretability of the proposed approach demonstrate its ability to screen
multimorbidity patients and help clinicians initiate timely treatment.
