  Auxiliary diagnosis of cardiac electrophysiological status can be obtained
through the analysis of 12-lead electrocardiograms (ECGs). This work proposes a
dual-scale lead-separated transformer with lead-orthogonal attention and
meta-information (DLTM-ECG) as a novel approach to address this challenge. ECG
segments of each lead are interpreted as independent patches, and together with
the reduced dimension signal, they form a dual-scale representation. As a
method to reduce interference from segments with low correlation, two group
attention mechanisms perform both lead-internal and cross-lead attention. Our
method allows for the addition of previously discarded meta-information,
further improving the utilization of clinical information. Experimental results
show that our DLTM-ECG yields significantly better classification scores than
other transformer-based models,matching or performing better than
state-of-the-art (SOTA) deep learning methods on two benchmark datasets. Our
work has the potential for similar multichannel bioelectrical signal processing
and physiological multimodal tasks.
