ReviewJMIR medical informatics2022
State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.
Review in JMIR medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management.Biomedical engineering online · 2025Pooled it
- Direct Reconstruction of High-Fidelity Electrocardiogram Signals From Vector-Based PDF Files With Integrated Deep Learning for Multiparameter Estimation: Retrospective Methodological Study.JMIR formative research · 2026Article
- Advances in the Interpretation of the Electrocardiogram by Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2026Review
- Interpretable Multimodal AI to predict the Presence of Late Gadolinium Enhancement on Cardiac Magnetic Resonance Imaging in Cardiac Sarcoidosis Patients.Circulation. Arrhythmia and electrophysiology · 2026Article
- Artificial intelligence in cardiology in the current era: a narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Multimodal Integration of Ambulatory ECG and Clinical Features for Sudden Cardiac Death and Pump Failure Death PredictionmedRxiv : the preprint server for health sciences · 2026Article
- Foundation models enable wearable signal screening for cardiovascular disease among people living with HIV.Communications medicine · 2026Article
- KAN-Former: a lightweight ECG model for real-time atrial fibrillation detection on wearable devices.Frontiers in bioengineering and biotechnology · 2026Article
- Deep Learning-Based Multi-Lead ECG Reconstruction from Lead I with Metadata Integration and Uncertainty Estimation.Sensors (Basel, Switzerland) · 2025Article
- Systematic Review of Artificial Intelligence and Electrocardiography for Cardiovascular Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial Intelligence-Based Electrocardiogram Model as a Predictor of Postoperative Atrial Fibrillation Following Cardiac Surgery: Retrospective Cohort Study.Journal of medical Internet research · 2025Article
- Optimizing myocardial infarction detection: a hybrid CNN-GRU deep learning approach.BMC medical informatics and decision making · 2025Article
- ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning.Proceedings of machine learning research · 2025Article
- Profiling electric signals of electrogenic probiotic bacteria using self-attention analysis.Applied microbiology and biotechnology · 2025Article
- Deep learning for electrocardiogram interpretation: Bench to bedside.European journal of clinical investigation · 2025Review
- Establishment and validation of a ResNet-based radiomics model for predicting prognosis in cervical spinal cord injury patients.Scientific reports · 2025Article
- A Resting ECG Screening Protocol Improved with Artificial Intelligence for the Early Detection of Cardiovascular Risk in Athletes.Diagnostics (Basel, Switzerland) · 2025Article
- Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram-Applicable in Clinical Practice?-Critical Literature Review with Meta-Analysis.Healthcare (Basel, Switzerland) · 2025Review
- Sparse learned kernels for interpretable and efficient medical time series processing.Nature machine intelligence · 2024Article
- Machine learning in cardiac stress test interpretation: a systematic review.European heart journal. Digital health · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundElectrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient's health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals.
objectiveThis study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications.
methodsThe PubMed search engine was systematically searched by combining "deep learning" and keywords such as "ecg," "ekg," "electrocardiogram," "electrocardiography," and "electrocardiology." Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches.
resultsWe identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models.
conclusionsWe expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.