MCA-net: A multi-task channel attention network for Myocardial infarction detection and location using 12-lead ECGs
Weibai Pan1, Ying An2, Yuxia Guan1
1School of Computer Science and Engineering, Central South University, Changsha, 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China.
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.
