6533b7dcfe1ef96bd12727b6
RESEARCH PRODUCT
Intra-cardiac Signatures of Atrial Arrhythmias Identified by Machine Learning and Traditional Features
Miguel RodrigoRafael SebastianBenjamin PaganoSumiran TakurAlejandro LiberosSanjiv M. Narayansubject
business.industrycardiovascular systemMedicineSinus rhythmcardiovascular diseasesAtrial arrhythmiasArtificial intelligencebusinessMachine learningcomputer.software_genrecomputerIntracardiac injectiondescription
Intracardiac devices separate atrial arrhythmias (AA) from sinus rhythm (SR) using electrogram (EGM) features such as rate, that are imperfect. We hypothesized that machine learning could improve this classification.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-01-01 |