Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 150, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2022.106199
Keywords
Electrocardiogram; Deep neural network; Multi-task learning; Myocardial infarction
Categories
Funding
- National Key Research and Development Program of China [2021YFF1201200]
- NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization [U1909208]
- 111 Project [B18059]
- Science and Technology Major Project of Changsha [kh2202004]
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This paper proposes a multi-task channel attention network (MCA-net) for myocardial infarction (MI) detection and location using 12-lead electrocardiograms (ECGs). By integrating features from different leads and introducing a multi-task framework, the MCA-net outperforms state-of-the-art methods in terms of accuracy. It effectively assists cardiologists in diagnosing and locating MI.
Problem: Myocardial infarction (MI) is a classic cardiovascular disease (CVD) that requires prompt diagnosis. However, due to the complexity of its pathology, it is difficult for cardiologists to make an accurate diagnosis in a short period. Aim: In the clinical, MI can be detected and located by the morphological changes on a 12-lead electrocardiogram (ECG). Therefore, we need to develop an automatic, high-performance, and easily scalable algorithm for MI detection and location using 12-lead ECGs to effectively reduce the burden on cardiologists. Methods: This paper proposes a multi-task channel attention network (MCA-net) for MI detection and location using 12-lead ECGs. It employs a channel attention network based on a residual structure to efficiently capture and integrate features from different leads. On top of this, a multi-task framework is used to additionally introduce the shared and complementary information between MI detection and location tasks to further enhance the model performance. Results: Our method is evaluated on two datasets (The PTB and PTBXL datasets). It achieved more than 90% accuracy for MI detection task on both datasets. For MI location tasks, we achieved 68.90% and 49.18% accuracy on the PTB dataset, respectively. And on the PTBXL dataset, we achieved more than 80% accuracy. Conclusion: Numerous comparison experiments demonstrate that MCA-net outperforms the state-of-the-art methods and has a better generalization. Therefore, it can effectively assist cardiologists to detect and locate MI and has important implications for the early diagnosis of MI and patient prognosis.
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