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Open AccessArticle

Hybrid Network with Attention Mechanism for Detection and Location of Myocardial Infarction Based on 12-Lead Electrocardiogram Signals

1
Chongqing University-University of Cincinnati Joint Co-op Institute, Chongqing University, Chongqing 400030, China
2
Key Laboratory of Biotechnology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, China
3
State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400030, China
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(4), 1020; https://doi.org/10.3390/s20041020
Received: 7 January 2020 / Revised: 28 January 2020 / Accepted: 11 February 2020 / Published: 14 February 2020
(This article belongs to the Special Issue ECG Sensors)
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. View Full-Text
Keywords: myocardial infarction; electrocardiogram; attention mechanism; convolutional neural network; bidirectional gated recurrent unit myocardial infarction; electrocardiogram; attention mechanism; convolutional neural network; bidirectional gated recurrent unit
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MDPI and ACS Style

Fu, L.; Lu, B.; Nie, B.; Peng, Z.; Liu, H.; Pi, X. Hybrid Network with Attention Mechanism for Detection and Location of Myocardial Infarction Based on 12-Lead Electrocardiogram Signals. Sensors 2020, 20, 1020.

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