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

• URL: http://arxiv.org/abs/2211.12777v1
• Date: Wed, 23 Nov 2022 08:45:34 GMT
• Title: A Dual-scale Lead-seperated Transformer With Lead-orthogonal Attention And Meta-information For Ecg Classification
• Authors: Yang Li, Guijin Wang, Zhourui Xia, Wenming Yang, Li Sun
• Abstract summary: This work proposes a dual-scale lead-separated transformer with lead-orthogonal attention and meta-information (DLTM-ECG) ECG segments are interpreted as independent patches, and together with the reduced dimension signal, they form a dual-scale representation. Our work has the potential for similar multichannel bioelectrical signal processing and physiological multimodal tasks.
• Score: 26.07181634056045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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