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Peer-Reviewed Publication
J Electrocardiol2023;814-12.January 1, 2023Journal Article

Artificial intelligence cloud platform improves arrhythmia detection from insertable cardiac monitors to 25 cardiac rhythm patterns through multi-label classification.

Fabio Quartieri1, Manuel Marina-Breysse2, Raquel Toribio-Fernandez3, Carlos Lizcano3, Annalisa Pollastrelli4, Isabella Paini5, Roberto Cruz3, Andrea Grammatico4, José María Lillo-Castellano6
1Department of Cardiology, Ospedale S. Maria Nuova, Reggio Emilia, Italy. Electronic address: fabio.quartieri@ausl.re.it.
2IDOVEN Research, AI Team, Madrid, Spain; Advanced Development in Arrhythmia Mechanisms and Therapy Laboratory, Myocardial Pathophysiology Area, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain; Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain.
3IDOVEN Research, AI Team, Madrid, Spain.
4EMEA CRM Medical Affairs, Abbott, Rome, Italy.
5Department of Cardiology, Ospedale S. Maria Nuova, Reggio Emilia, Italy.
6IDOVEN Research, AI Team, Madrid, Spain; Advanced Development in Arrhythmia Mechanisms and Therapy Laboratory, Myocardial Pathophysiology Area, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain; Fundación Interhospitalaria Para la Investigación Cardiovascular (FIC), Madrid, Spain.
PubMed DOI

Abstract

BACKGROUND: Electrocardiogram (ECG) is the gold standard for the diagnosis of cardiac arrhythmias and other heart diseases. Insertable cardiac monitors (ICMs) have been developed to continuously monitor cardiac activity over long periods of time and to detect 4 cardiac patterns (atrial tachyarrhythmias, ventricular tachycardia, bradycardia, and pause). However, interpretation of ECG or ICM subcuta…

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