This paper is in the following e-collection/theme issue:

Cardiac Arrhythmia, Atrial Fibrillation (85) Big Data (240) Intensive Care Unit (ICU) (112) Decision Support for Health Professionals (1185) Clinical Information and Decision Making (1407) Electronic Health Records (1053) Tools, Programs and Algorithms (266)

Published on in Vol 5, No 1 (2021): Jan-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18840, first published .
Development and Validation of an Automated Algorithm to Detect Atrial Fibrillation Within Stored Intensive Care Unit Continuous Electrocardiographic Data: Observational Study

Development and Validation of an Automated Algorithm to Detect Atrial Fibrillation Within Stored Intensive Care Unit Continuous Electrocardiographic Data: Observational Study

Development and Validation of an Automated Algorithm to Detect Atrial Fibrillation Within Stored Intensive Care Unit Continuous Electrocardiographic Data: Observational Study

Authors of this article:

Allan J Walkey1 Author Orcid Image ;   Syed K Bashar2 Author Orcid Image ;   Md Billal Hossain2 Author Orcid Image ;   Eric Ding3 Author Orcid Image ;   Daniella Albuquerque3 Author Orcid Image ;   Michael Winter4 Author Orcid Image ;   Ki H Chon2 Author Orcid Image ;   David D McManus3 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

Journals

  1. Chen B, Maslove D, Curran J, Hamilton A, Laird P, Mousavi P, Sibley S. A deep learning model for the classification of atrial fibrillation in critically ill patients. Intensive Care Medicine Experimental 2023;11(1) View
  2. Moghaddasi H, Hendriks R, van der Veen A, de Groot N, Hunyadi B. Classification of De novo post-operative and persistent atrial fibrillation using multi-channel ECG recordings. Computers in Biology and Medicine 2022;143:105270 View

Books/Policy Documents

  1. Dong Han , Fahimeh Mohagheghian , Ki H. Chon . Encyclopedia of Sensors and Biosensors. View