Electrocardiogram sampling frequency for the optimal performance of complexity analysis and machine learning models: Discrimination between patients with and without paroxysmal atrial fibrillation using sinus rhythm electrocardiograms

Based on these results, there is potential for 12-lead electrocardiogram signals to be recorded at 125 Hz as standard and used in conjunction with complexity analysis to aid in the detection of patients with AF.

Authors

Institutions

Publication Details

Journal
Heart Rhythm O2
Published
2024-11-08
DOI
https://doi.org/10.1016/j.hroo.2024.11.002
Citations
3
Primary Topic
ECG Monitoring and Analysis
Type
article
Field-Weighted Citation Impact
1.21

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Electrocardiogram sampling frequency for the optimal performance of complexity analysis and machine learning models: Discrimination between patients with and without paroxysmal atrial fibrillation using sinus rhythm electrocardiograms

Vadim Alexeenko, Philip J. Aston, Gregory Y.H. Lip, Gary Tse et al.
3 citations
Heart Rhythm O2
ECG Monitoring and Analysis
1.21
article

Electrocardiogram sampling frequency for the optimal performance of complexity analysis and machine learning models: Discrimination between patients with and without paroxysmal atrial fibrillation using sinus rhythm electrocardiograms

Vadim Alexeenko, Philip J. Aston, Gregory Y.H. Lip, Gary Tse, Kamalan Jeevaratnam, Steven Creasy
article en
3 citations

Abstract

Based on these results, there is potential for 12-lead electrocardiogram signals to be recorded at 125 Hz as standard and used in conjunction with complexity analysis to aid in the detection of patients with AF.

Heart Rhythm O2Vol. 6(1)
University of Liverpool (GB), University of Surrey (GB), Liverpool Heart and Chest Hospital (GB), eHealth Africa (US), Second Hospital of Tianjin Medical University (CN), Tianjin Medical University (CN), Liverpool John Moores University (GB), Aalborg University (DK)
National Institute for Health and Care Research
Reduced inequalities
Openalex Percentile: Top 18%
ECG Monitoring and Analysis
1.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Electrocardiogram sampling frequency for the optimal performance of complexity analysis and machine learning models: Discrimination between patients with and without paroxysmal atrial fibrillation using sinus rhythm electrocardiograms — Vadim Alexeenko, Philip J. Aston, et al. · Heart Rhythm O2 (2024) | TGRS Research Map | TGRS