Automatic arrhythmia detection with multi-lead ECG signals based on heterogeneous graph attention networks
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 p...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2022-08-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2022581?viewType=HTML |
| _version_ | 1818481780981760000 |
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| author | MingHao Zhong Fenghuan Li Weihong Chen |
| author_facet | MingHao Zhong Fenghuan Li Weihong Chen |
| author_sort | MingHao Zhong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T11:39:37Z |
| format | Article |
| id | doaj.art-9c1bdfec50fc4084ba33f5ac0ac7985a |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-10T11:39:37Z |
| publishDate | 2022-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-9c1bdfec50fc4084ba33f5ac0ac7985a2022-12-22T01:50:18ZengAIMS PressMathematical Biosciences and Engineering1551-00182022-08-011912124481247110.3934/mbe.2022581Automatic arrhythmia detection with multi-lead ECG signals based on heterogeneous graph attention networksMingHao Zhong0Fenghuan Li 1Weihong Chen2School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, ChinaAutomatic 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.https://www.aimspress.com/article/doi/10.3934/mbe.2022581?viewType=HTMLarrhythmia detectionheterogeneous graphgraph attention networkmulti-leadecg signal |
| spellingShingle | MingHao Zhong Fenghuan Li Weihong Chen Automatic arrhythmia detection with multi-lead ECG signals based on heterogeneous graph attention networks Mathematical Biosciences and Engineering arrhythmia detection heterogeneous graph graph attention network multi-lead ecg signal |
| title | Automatic arrhythmia detection with multi-lead ECG signals based on heterogeneous graph attention networks |
| title_full | Automatic arrhythmia detection with multi-lead ECG signals based on heterogeneous graph attention networks |
| title_fullStr | Automatic arrhythmia detection with multi-lead ECG signals based on heterogeneous graph attention networks |
| title_full_unstemmed | Automatic arrhythmia detection with multi-lead ECG signals based on heterogeneous graph attention networks |
| title_short | Automatic arrhythmia detection with multi-lead ECG signals based on heterogeneous graph attention networks |
| title_sort | automatic arrhythmia detection with multi lead ecg signals based on heterogeneous graph attention networks |
| topic | arrhythmia detection heterogeneous graph graph attention network multi-lead ecg signal |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2022581?viewType=HTML |
| work_keys_str_mv | AT minghaozhong automaticarrhythmiadetectionwithmultileadecgsignalsbasedonheterogeneousgraphattentionnetworks AT fenghuanli automaticarrhythmiadetectionwithmultileadecgsignalsbasedonheterogeneousgraphattentionnetworks AT weihongchen automaticarrhythmiadetectionwithmultileadecgsignalsbasedonheterogeneousgraphattentionnetworks |