论文标题
双尺度铅分离的变压器，具有铅 - 正交注意力和元信息，以进行ECG分类
A Dual-scale Lead-seperated Transformer With Lead-orthogonal Attention And Meta-information For Ecg Classification
论文作者
论文摘要
心脏电生理状态的辅助诊断可以通过分析12铅心电图（ECG）获得。这项工作提出了一种双尺度分离的变压器，具有铅 - 正交注意力和元信息（DLTM-ECG），作为应对这一挑战的新方法。每个铅的ECG段都被解释为独立斑块，并与降低信号一起形成双尺度表示。作为减少相关性低段的干扰的一种方法，两种组注意机制既执行铅内部和交叉铅的注意力。我们的方法允许添加先前废弃的元信息，从而进一步改善临床信息的利用。实验结果表明，我们的DLTM-ECG比其他基于变压器的模型的分类得分明显好得多，与两个基准数据集上的最先进的深度学习方法相匹配或表现更好。我们的工作有可能进行类似的多通道生物电信信号处理和生理多模式任务。
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.
