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](https://asset.jmir.pub/assets/636405e1ca28a06c24d9baa14239c8dd.png 480w,https://asset.jmir.pub/assets/636405e1ca28a06c24d9baa14239c8dd.png 960w,https://asset.jmir.pub/assets/636405e1ca28a06c24d9baa14239c8dd.png 1920w,https://asset.jmir.pub/assets/636405e1ca28a06c24d9baa14239c8dd.png 2500w)
Journals
- 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
- 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
- Dong Han , Fahimeh Mohagheghian , Ki H. Chon . Encyclopedia of Sensors and Biosensors. View