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.
翻译:通过分析12个铅型心电图(ECGs),可以取得对心脏电电生理状态的辅助诊断。这项工作建议使用一个双尺度的铅分离变压器,以铅-垂直关注和元信息(DLTM-ECG),作为应对这一挑战的新办法。每种铅的ECG部分被解释为独立的补丁,加上尺寸降低的信号,它们形成一种双重代表制。作为一种减少来自低相关性部分的干扰的方法,两个群体关注机制既可以发挥铅-内部作用,也可以发挥交叉作用。我们的方法允许添加先前废弃的元信息,进一步改善临床信息的利用。实验结果表明,我们的DLTM-ECG的分类分数比其他基于变压器的模型要高得多,比其他在两个基准数据集上采用的最先进的(SOTA)深学习方法要好。我们的工作具有类似的多频道生物电信号处理和生理多式任务的潜力。