ArticleEuropean heart journal. Digital health2021
Atrial fibrillation risk prediction from the 12-lead electrocardiogram using digital biomarkers and deep representation learning.
Article in European heart journal. Digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence capabilities in identifying atrial fibrillation using baseline sinus rhythm ECG : a systematic review.Open heart · 2025Pooled it
- Artificial Intelligence-Enabled Electrocardiography Model for Left Ventricular Systolic and Diastolic Dysfunction Predict Incident Atrial Fibrillation in Patients with Sinus Rhythm: A Time-Dependent Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Modeling day-long ECG signals to predict heart failure risk with explainable AI.NPJ digital medicine · 2026Article
- Atrial Cardiomyopathy Detected by Electrocardiogram: Association with Stroke in a Brazilian Electronic Cohort.Arquivos brasileiros de cardiologia · 2026Article
- Artificial intelligence for atrial fibrillation prediction while in sinus rhythm: a narrative review.European heart journal. Digital health · 2026Review
- Tele-Electrocardiography and Mortality: Clinical Outcomes in Digital Electrocardiography Cohort-Data from Belo Horizonte, Brazil (CODE-BH).Global heart · 2026Article
- Short-term atrial fibrillation onset prediction using machine learning.European heart journal. Digital health · 2025Article
- Prediction of Atrial Fibrillation From the ECG in the Community Using Deep Learning: A Multinational Study.Circulation. Arrhythmia and electrophysiology · 2025Article
- Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction.IEEE transactions on big data · 2025Article
- Upscaling a regional telecardiology service to a nationwide coverage and beyond: the experience of the Telehealth Network of Minas Gerais.BMJ global health · 2025Article
- Artificial intelligence: Applications in cardio-oncology and potential impact on racial disparities.American heart journal plus : cardiology research and practice · 2024Article
- Evaluating regression and probabilistic methods for ECG-based electrolyte prediction.Scientific reports · 2024Article
- An artificial intelligence-enabled Holter algorithm to identify patients with ventricular tachycardia by analysing their electrocardiogram during sinus rhythm.European heart journal. Digital health · 2024Article
- Early warning of atrial fibrillation using deep learning.Patterns (New York, N.Y.) · 2024Article
- Adopting artificial intelligence in cardiovascular medicine: a scoping review.Hypertension research : official journal of the Japanese Society of Hypertension · 2024Article
- Cardiovascular Statistics - Brazil 2023.Arquivos brasileiros de cardiologia · 2024Article
- Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias.NPJ digital medicine · 2023Article
- Comparison of discrimination and calibration performance of ECG-based machine learning models for prediction of new-onset atrial fibrillation.BMC medical research methodology · 2023Article
- Screening for Chagas disease from the electrocardiogram using a deep neural network.PLoS neglected tropical diseases · 2023Article
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7 authors.
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Abstract
Aims: This study aims to assess whether information derived from the raw 12-lead electrocardiogram (ECG) combined with clinical information is predictive of atrial fibrillation (AF) development. Methods and results: We use a subset of the Telehealth Network of Minas Gerais (TNMG) database consisting of patients that had repeated 12-lead ECG measurements between 2010 and 2017 that is 1 130 404 recordings from 415 389 unique patients. Median and interquartile of age for the recordings were 58 (46-69) and 38% of the patients were males. Recordings were assigned to train-validation and test sets in an 80:20% split which was stratified by class, age and gender. A random forest classifier was trained to predict, for a given recording, the risk of AF development within 5 years. We use features obtained from different modalities, namely demographics, clinical information, engineered features, and features from deep representation learning. The best model performance on the test set was obtained for the model combining features from all modalities with an area under the receiver operating characteristic curve (AUROC) = 0.909 against the best single modality model which had an AUROC = 0.839. Conclusion: Our study has important clinical implications for AF management. It is the first study integrating feature engineering, deep learning, and Electronic medical record system (EMR) metadata to create a risk prediction tool for the management of patients at risk of AF. The best model that includes features from all modalities demonstrates that human knowledge in electrophysiology combined with deep learning outperforms any single modality approach. The high performance obtained suggest that structural changes in the 12-lead ECG are associated with existing or impending AF.
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