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

A Dual-scale Lead-seperated Transformer With Lead-orthogonal Attention And Meta-information For Ecg Classification