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

Autor: Xitian Pi, Lidan Fu, Hongying Liu, Peng Zhiyun, Bo Nie, Binchun Lu
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Time Factors
Computer science
12 lead electrocardiogram
convolutional neural network
02 engineering and technology
electrocardiogram
lcsh:Chemical technology
01 natural sciences
Biochemistry
Convolutional neural network
Article
Analytical Chemistry
bidirectional gated recurrent unit
Electrocardiography
Discriminative model
Robustness (computer science)
0202 electrical engineering
electronic engineering
information engineering
medicine
Humans
Attention
lcsh:TP1-1185
Myocardial infarction
Electrical and Electronic Engineering
Instrumentation
Electrodes
business.industry
Deep learning
010401 analytical chemistry
Reproducibility of Results
Pattern recognition
Signal Processing
Computer-Assisted
medicine.disease
Atomic and Molecular Physics
and Optics
0104 chemical sciences
myocardial infarction
020201 artificial intelligence & image processing
Artificial intelligence
Neural Networks
Computer
business
attention mechanism
Algorithms
Zdroj: Sensors, Vol 20, Iss 4, p 1020 (2020)
Sensors (Basel, Switzerland)
Sensors
Volume 20
Issue 4
ISSN: 1424-8220
Popis: 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.
Databáze: OpenAIRE
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