Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review
Problem
Early detection and localization of myocardial infarction via ECG is life-saving, but population-wide ECG screening is too labor-intensive without automation.
Why it matters
Myocardial infarction is a common, deadly cardiovascular disorder where early diagnosis saves lives.
What the AI did
This literature review surveys 59 deep learning studies (CNN, LSTM, CRNN, GRU, ResNet, autoencoder) applying ECG analysis to MI detection and localization over five years.
Results
not stated in source
Impact
The review maps recent deep learning progress toward feasible ECG-based MI screening.
Record
| Venue | Frontiers in Cardiovascular Medicine |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | Ping Xiong, Simon Ming‐Yuen Lee, Ging Chan |
| Citations | 104 (OpenAlex, at import time) |
| References | 163 |
| Model or method | CNN, LSTM, CRNN, GRU, ResNet, autoencoder (reviewed studies) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3389/fcvm.2022.860032 |
| Open access | gold |
| Keywords | myocardial infarction, ECG, deep learning, literature review, CNN |
Importance rating
major — millions of lives, an industry or a field
Abstract states myocardial infarction is a common cardiovascular disorder and early diagnosis is critical for lifesaving; ECG screening could enable population-based detection.