Accessibility settings

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

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
  3. Ding X, Zhang L, Guo F, Pan X, Wei X, Su L, Wu L, Min L, Zhang M, Han L. Application of multimodal nanotechnology and standardized nursing management in ventricular arrhythmia. BioMedical Engineering OnLine 2026 View

Books/Policy Documents

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