ReviewFrontiers in cardiovascular medicine2022
Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review.
Review in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 88 citations in OpenAlex.
- Heart failure detection in electrocardiograms using artificial intelligence and pragmatic labelling.NPJ digital medicine · 2026Article
- Nonlinear Feature-Based MI Detection Supported by DWT and EMD on ECG: A High-Performance Decision Support Approach.Biosensors · 2026Article
- Toward robust automated cardiovascular arrhythmia detection using self-supervised learning and 1-dimensional vision transformers.Scientific reports · 2026Article
- Multimodal deep learning for acute myocardial infarction detection from 12-lead electrocardiogram: a multi-centre study with cross-hospital validation.European heart journal. Digital health · 2026Article
- A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet.Scientific reports · 2026Article
- Explainable hybrid deep learning framework with Grad-CAM for heartbeat-level arrhythmia classification.Frontiers in physiology · 2026Article
- Bionic Wearable ECG with Multimodal Large Language Models: Coherent Temporal Modeling for Early Ischemia Warning and Reperfusion Risk Stratification.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- Machine learning-based migraine analysis using retinal vessel diameters from optical coherence tomography: an alternative approach.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Article
- ISENet: a deep learning model for detecting ischemic ST changes in long-term ECG monitoring.Medical & biological engineering & computing · 2025Article
- Optimizing myocardial infarction detection: a hybrid CNN-GRU deep learning approach.BMC medical informatics and decision making · 2025Article
- Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities.Journal of clinical medicine · 2025Article
- Review
- Artificial Intelligence for Myocardial Infarction Detection via Electrocardiogram: A Scoping Review.Journal of clinical medicine · 2025Review
- [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
- Addressing myocardial infarction in South-Asian populations: risk factors and machine learning approaches.NPJ cardiovascular health · 2025Review
- Comparison of Neural Network Structures for Identifying Shockable Rhythm During Cardiopulmonary Resuscitation.Journal of clinical medicine · 2025Article
- Article
- Exploring the feasibility of real-time on-device ECG biometric classification using quantized neural networks.Frontiers in digital health · 2025Article
- Deep learning-based prediction of atrial fibrillation from polar transformed time-frequency electrocardiogram.PloS one · 2025Article
- Foundation model of ECG diagnosis: Diagnostics and explanations of any form and rhythm on ECG.Cell reports. Medicine · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Myocardial infarction is a common cardiovascular disorder caused by prolonged ischemia, and early diagnosis of myocardial infarction (MI) is critical for lifesaving. ECG is a simple and non-invasive approach in MI detection, localization, diagnosis, and prognosis. Population-based screening with ECG can detect MI early and help prevent it but this method is too labor-intensive and time-consuming to carry out in practice unless artificial intelligence (AI) would be able to reduce the workload. Recent advances in using deep learning (DL) for ECG screening might rekindle this hope. This review aims to take stock of 59 major DL studies applied to the ECG for MI detection and localization published in recent 5 years, covering convolutional neural network (CNN), long short-term memory (LSTM), convolutional recurrent neural network (CRNN), gated recurrent unit (GRU), residual neural network (ResNet), and autoencoder (AE). In this period, CNN obtained the best popularity in both MI detection and localization, and the highest performance has been obtained from CNN and ResNet model. The reported maximum accuracies of the six different methods are all beyond 97%. Considering the usage of different datasets and ECG leads, the network that trained on 12 leads ECG data of PTB database has obtained higher accuracy than that on smaller number leads data of other datasets. In addition, some limitations and challenges of the DL techniques are also discussed in this review.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.