Automatic arrhythmia detection with multi-lead ECG signals based on heterogeneous graph attention networks

MingHao Zhong; Fenghuan Li; Weihong Chen; MingHao Zhong; Fenghuan Li; Weihong Chen

doi:10.3934/mbe.2022581

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 12: 12448-12471. doi: 10.3934/mbe.2022581

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Research article

Automatic arrhythmia detection with multi-lead ECG signals based on heterogeneous graph attention networks

School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China

Academic Editor: Weining Wu

Received: 18 June 2022 Revised: 01 August 2022 Accepted: 05 August 2022 Published: 26 August 2022

Automatic arrhythmia detection is very important for cardiovascular health. It is generally performed by measuring the electrocardiogram (ECG) signals of standard multiple leads. However, the correlations of multiple leads are often ignored. In addition, an extensive and complex feature extraction process is usually needed in most existing studies. Therefore, these challenges will not only lead to the loss of overall lead information, but also cause the detection performance to depend on the quality of features. To solve these challenges, a novel multi-lead arrhythmia detection model based on a heterogeneous graph attention network is proposed in this paper. We have modeled the multi-lead data as a heterogeneous graph to integrate diverse information and construct intra-lead and inter-lead correlations in multi-lead data, providing a reasonable and effective the data model. A heterogeneous graph network with a dual-level attention strategy has been utilized to capture the interactions among diverse information and information types. At the same time, our model does not require any feature extraction process for the ECG signals, which avoids out complex feature engineering. Extensive experimental results show that multi-lead information and complex correlations can be well captured, thus confirming that the proposed model results in significant improvements in multi-lead arrhythmia detection.
- arrhythmia detection,
- heterogeneous graph,
- graph attention network,
- multi-lead,
- ECG signal
Citation: MingHao Zhong, Fenghuan Li, Weihong Chen. Automatic arrhythmia detection with multi-lead ECG signals based on heterogeneous graph attention networks[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 12448-12471. doi: 10.3934/mbe.2022581

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Abstract

Automatic arrhythmia detection is very important for cardiovascular health. It is generally performed by measuring the electrocardiogram (ECG) signals of standard multiple leads. However, the correlations of multiple leads are often ignored. In addition, an extensive and complex feature extraction process is usually needed in most existing studies. Therefore, these challenges will not only lead to the loss of overall lead information, but also cause the detection performance to depend on the quality of features. To solve these challenges, a novel multi-lead arrhythmia detection model based on a heterogeneous graph attention network is proposed in this paper. We have modeled the multi-lead data as a heterogeneous graph to integrate diverse information and construct intra-lead and inter-lead correlations in multi-lead data, providing a reasonable and effective the data model. A heterogeneous graph network with a dual-level attention strategy has been utilized to capture the interactions among diverse information and information types. At the same time, our model does not require any feature extraction process for the ECG signals, which avoids out complex feature engineering. Extensive experimental results show that multi-lead information and complex correlations can be well captured, thus confirming that the proposed model results in significant improvements in multi-lead arrhythmia detection.

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