Hybrid Network with Attention Mechanism for Detection and Location of Myocardial Infarction Based on 12-Lead Electrocardiogram Signals
Hybrid Network with Attention Mechanism for Detection and Location of Myocardial Infarction Based on 12-Lead Electrocardiogram Signals
复制标题
基于 12 导联心电图信号的具有注意力机制的混合网络用于心肌梗死的检测和定位
DOI：
10.3390/s20041020
复制
发表时间：
2020-02-01
期刊：
影响因子：
3.9
通讯作者：
Pi, Xitian
中科院分区：
文献类型：
--
作者：
Fu, Lidan;Lu, Binchun;Pi, Xitian
心电图（ECG）是一种无创、廉价且有效的心肌梗死（MI）诊断工具。传统的检测算法需要扎实的领域专业知识，并且严重依赖手工制作的特征。尽管之前的工作研究了用于提取特征的深度学习方法，但这些方法仍然忽略了不同导联之间的关系以及心电信号的时间特征。为了解决这些问题，提出了一种与卷积神经网络（CNN）和双向门控循环单元（BiGRU）框架集成的新型多导联注意（MLA）机制（MLA-CNN-BiGRU），通过12导联心电图记录来检测和定位心肌梗死。具体来说，MLA机制根据不同线索的贡献自动衡量并分配权重。二维 CNN 模块利用线索之间的相关特征并提取有区别的空间特征。此外，BiGRU 模块提取每条导联内的基本时间特征。这两个模块的空间和时间特征融合在一起作为全局特征进行分类。在实验中，在患者内方案和患者间方案下进行心肌梗死定位和检测，以测试所提出框架的稳健性。实验结果表明，我们的智能框架取得了令人满意的性能，并具有重要的临床意义。
The electrocardiogram (ECG) is a non-invasive, inexpensive, and effective tool for myocardial infarction (MI) diagnosis. Conventional detection algorithms require solid domain expertise and rely heavily on handcrafted features. Although previous works have studied deep learning methods for extracting features, these methods still neglect the relationships between different leads and the temporal characteristics of ECG signals. To handle the issues, a novel multi-lead attention (MLA) mechanism integrated with convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) framework (MLA-CNN-BiGRU) is therefore proposed to detect and locate MI via 12-lead ECG records. Specifically, the MLA mechanism automatically measures and assigns the weights to different leads according to their contribution. The two-dimensional CNN module exploits the interrelated characteristics between leads and extracts discriminative spatial features. Moreover, the BiGRU module extracts essential temporal features inside each lead. The spatial and temporal features from these two modules are fused together as global features for classification. In experiments, MI location and detection were performed under both intra-patient scheme and inter-patient scheme to test the robustness of the proposed framework. Experimental results indicate that our intelligent framework achieved satisfactory performance and demonstrated vital clinical significance.