ArticleSensors (Basel, Switzerland)2021
Detection of Myocardial Infarction Using ECG and Multi-Scale Feature Concatenate.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 37 citations in OpenAlex.
- Nonlinear Feature-Based MI Detection Supported by DWT and EMD on ECG: A High-Performance Decision Support Approach.Biosensors · 2026Article
- [Automatic detection and visualization of myocardial infarction in electrocardiograms based on an interpretable deep learning model].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Data Augmentation-Enhanced Myocardial Infarction Classification and Localization Using a ResNet-Transformer Cascaded Network.Biology · 2025Article
- [A myocardial infarction detection and localization model based on multi-scale field residual blocks fusion with modified channel attention].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- A Multi-Domain Feature Fusion CNN for Myocardial Infarction Detection and Localization.Biosensors · 2025Article
- Comprehensive Analysis of Cardiovascular Diseases: Symptoms, Diagnosis, and AI Innovations.Bioengineering (Basel, Switzerland) · 2024Review
- Electrocardiographic abnormalities and associated factors among HIV-infected adults on antiretroviral therapy.Frontiers in reproductive health · 2024Article
- [A review on intelligent auxiliary diagnosis methods based on electrocardiograms for myocardial infarction].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Review
- A Robustness Evaluation of Machine Learning Algorithms for ECG Myocardial Infarction Detection.Journal of clinical medicine · 2022Article
- Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review.Frontiers in cardiovascular medicine · 2022Review
- Intelligent Biosignal Analysis Methods.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However, issues, particularly overfitting and underfitting, were not being taken into account. In other words, it is unclear whether the network structure is too simple or complex. Toward this end, the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally, multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being extracted through pooling the signals. The best structure was obtained via tuning both the number of filters in the convolutional layers and the number of inputting signal scales. As a result, the N-Net reached a 95.76% accuracy in the MI detection task, whereas the MSN-Net reached an accuracy of 61.82% in the MI locating task. Both networks give a higher average accuracy and a significant difference of
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