Atrial fibrillation signatures on intracardiac electrograms identified by deep learning

Hindricks, 2010, Performance of a new leadless im-plantable cardiac monitor in detecting and quantifying atrial fibrillation: results of the XPECT trial, Circ Arrhythm Electrophysiol, 3, 141, 10.1161/CIRCEP.109.877852 V Perez, 2019, Large-scale Assessment of a smartwatch to identify atrial fibrillation, N. Engl. J. Med., 381, 1909, 10.1056/NEJMoa1901183 Guo, 2019, Mobile photoplethysmographic technology to detect atrial fibrillation, J. Am. Coll. Cardiol., 74, 2365, 10.1016/j.jacc.2019.08.019 Kaufman, 2012, Positive predictive value of device-detected atrial high-rate episodes at different rates and durations: an analysis from ASSERT, Heart Rhythm, 9, 1241, 10.1016/j.hrthm.2012.03.017 Bertaglia, 2019, Atrial high-rate episodes: prevalence, stroke risk, implications for management, and clinical gaps in evidence, Europace, 21, 1459, 10.1093/europace/euz172 Tomson, 2015, Management of device-detected atrial high-rate episodes, Card Electrophysiol Clin, 7, 515, 10.1016/j.ccep.2015.05.010 Krittanawong, 2019, Deep learning for cardiovascular medicine: a practical primer, Eur. Heart J., 40, 2058, 10.1093/eurheartj/ehz056 Topol, 2019, High-performance medicine: the convergence of human and artificial intelligence, Nat. Med., 25, 44, 10.1038/s41591-018-0300-7 Bumgarner, 2018, Smartwatch algorithm for automated detection of atrial fibrillation, J. Am. Coll. Cardiol., 71, 2381, 10.1016/j.jacc.2018.03.003 Tison, 2018, Passive detection of atrial fibrillation using a commercially available smartwatch, JAMA Cardiol, 3, 409, 10.1001/jamacardio.2018.0136 Honarbakhsh, 2017, Panoramic atrial mapping with basket catheters: a quantitative analysis to optimize practice, patient selection, and catheter choice, J. Cardiovasc. Electrophysiol., 28, 1423, 10.1111/jce.13331 Rodrigo M, 2021, Non-invasive spatial mapping of frequencies in atrial fibrillation: correlation with contact, Mapping” Front Physiol, 11, 611266, 10.3389/fphys.2020.611266 Alhusseini, 2020, Machine learning to classify intra-cardiac electrical patterns during atrial fibrillation: machine learning of atrial fibrillation, Circ Arrhythm Electrophysiol, 13, e008160, 10.1161/CIRCEP.119.008160 Feeny, 2020, Artificial intelligence and machine learning in arrhythmias and cardiac Electrophysiology, Circ Arrhythm Electrophysiol, 13, e007952, 10.1161/CIRCEP.119.007952 Rogers, 2020, Machine learned cellular phenotypes predict outcome in ischemic cardiomyopathy, Circ. Res., 128, 172, 10.1161/CIRCRESAHA.120.317345 Liaqat, 2020, Detection of atrial fibrillation using a machine learning approach, Information, 11, 549, 10.3390/info11120549 Ribeiro, 2016, Why should I trust you?, Explaining the Predictions of Any Classifier” arXiv, 1602 Murat, 2021, Review of deep learning-based atrial fibrillation detection studies, Int. J. Environ. Res. Publ. Health, 28, 11302, 10.3390/ijerph182111302 Ivanovic, 2019, Deep learning approach for highly specific atrial fibrillation and flutter detection based on RR intervals, Annu Int Conf IEEE Eng Med Biol Soc, 1780 Deb, 2021, Identifying atrial fibrillation mechanisms for personalized medicine, J. Clin. Med., 10, 5679, 10.3390/jcm10235679