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Multi-branch fusion network for Myocardial infarction screening from 12-lead ECG images
Pengyi Hao1, Xiang Gao1, Zhihe Li1
1College of Computer Science and Technology, Zhejiang University of Technology, hangzhou, China.
Insights
This study introduces an automated method for detecting myocardial infarction (MI) using 12-lead electrocardiogram (ECG) images. The developed multi-branch fusion framework achieves high accuracy in screening MI from ECG images.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Myocardial infarction (MI) is a life-threatening condition caused by blocked coronary arteries.
- Electrocardiograms (ECG) are crucial for diagnosing MI but require expert interpretation.
- Automated MI detection from ECGs is needed to improve diagnostic efficiency.
Purpose of the Study:
- To develop and evaluate an automated system for myocardial infarction (MI) screening using 12-lead ECG images.
- To create a multi-branch fusion framework capable of processing ECG images for MI detection.
- To compare the performance of the automated system against human physicians.
Main Methods:
- A multi-branch fusion framework was designed, incorporating text detection and position alignment to isolate 12 leads from ECG images.
- A shallow neural network formed the multi-branch network, generating 12 feature maps from the separated leads.
- Depth fusion concatenated feature maps, followed by a classification network for MI diagnosis.
Main Results:
- The proposed framework demonstrated effectiveness in screening MI from ECG images.
- Achieved high performance metrics: 94.73% accuracy, 96.41% sensitivity, 95.94% specificity, and 93.79% F1-score.
- Outperformed other networks and showed comparable results to physicians in practical use.
Conclusions:
- Analyzing 12-lead ECG images offers an effective alternative to traditional 1D ECG signals for MI screening.
- The proposed model represents a significant advancement in applying image analysis for MI detection.
- Automated extraction and analysis of 12 leads from ECG images show promise for clinical application.
Background And Objective:
Myocardial infarction (MI) is a myocardial anoxic incapacitation caused by severe cardiovascular obstruction that can cause irreversible injury or even death. In medical field, the electrocardiogram (ECG) is a common and effective way to diagnose myocardial infarction, which often requires a wealth of medical knowledge. It is necessary to develop an approach that can detect the MI automatically.
Methods:
In this paper, we propose a multi-branch fusion framework for automatic MI screening from 12-lead ECG images, which consists of multi-branch network, feature fusion and classification network. First, we use text detection and position alignment to automatically separate twelve leads from ECG images. Then, those 12 leads are input into the multi-branch network constructed by a shallow neural network to get 12 feature maps. After concatenating those feature maps by depth fusion, classification is explored to judge the given ECG is MI or not.
Results:
Based on extensive experiments on an ECG image dataset, performances of different combinations of structures are analyzed. The proposed network is compared with other networks and also compared with physicians in the practical use. All the experiments verify that the proposed method is effective for MI screening based on ECG images, which achieves accuracy, sensitivity, specificity and F1-score of 94.73%, 96.41%, 95.94% and 93.79% respectively.
Conclusions:
Rather than using the typical one-dimensional electrical ECG signal, this paper gives an effective model to screen MI by analyzing 12-lead ECG images. Extracting and analyzing these 12 leads from their corresponding ECG images is a good attempt in the application of MI screening.
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