State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review
importance 6/10
ai assisted
ECG Monitoring and Analysis cardiology diagnostics stage: research
Problem
Deep learning is being widely applied to ECG data for clinical diagnosis, but the state of the field needed systematic assessment.
Why it matters
ECG is one of the most common non-invasive diagnostic tools, so better automated analysis affects many patients.
What the AI did
A systematic review of deep learning methods applied to ECG data across clinical applications, screening PubMed for studies with quantitative evaluations.
Results
not stated in source
Impact
Summarizes the state of the art of deep learning on ECG data; no new results.
Record
| Venue | JMIR Medical Informatics |
|---|---|
| Published | 2022 |
| Work type | review |
| Authors | Γεώργιος Πετμεζάς, Leandros Stefanopoulos, Vassilis Kilintzis, Andreas Tzavelis, John A. Rogers, Aggelos K. Katsaggelos, Nicos Maglaveras |
| Citations | 106 (OpenAlex, at import time) |
| References | 375 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.2196/38454 |
| Open access | gold |
| Keywords | ECG, deep learning, systematic review, diagnostics, clinical applications |
Importance rating
6
significant — thousands to millions affected
Abstract states ECG is one of the most common noninvasive diagnostic tools, so automated analysis affects many patients; paper is a review with no new results.