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1.
Automatic and interpretable prediction of the site of origin in outflow tract ventricular arrhythmias: machine learning integrating electrocardiograms and clinical data.
Front Cardiovasc Med
; 11: 1353096, 2024.
Artículo
en Inglés
| MEDLINE | ID: mdl-38572307
2.
Delineation of intracavitary electrograms for the automatic quantification of decrement-evoked potentials in the coronary sinus with deep-learning techniques.
Front Physiol
; 15: 1331852, 2024.
Artículo
en Inglés
| MEDLINE | ID: mdl-38818521
3.
Artificial intelligence for detection of ventricular oversensing: Machine learning approaches for noise detection within nonsustained ventricular tachycardia episodes remotely transmitted by pacemakers and implantable cardioverter-defibrillators.
Heart Rhythm
; 20(10): 1378-1384, 2023 10.
Artículo
en Inglés
| MEDLINE | ID: mdl-37406873
4.
Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias.
Front Physiol
; 13: 909372, 2022.
Artículo
en Inglés
| MEDLINE | ID: mdl-36035489
5.
Delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks.
Sci Rep
; 11(1): 863, 2021 01 13.
Artículo
en Inglés
| MEDLINE | ID: mdl-33441632
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