Name
Abstract | ||
|---|---|---|
Early detection of Atrial Fibrillation (AFib) is crucial to prevent stroke recurrence. New tools for monitoring cardiac rhythm are important for risk stratification and stroke prevention. As many of new approaches to longterm AFib detection are now based on photoplethysmogram (PPG) recordings from wearable devices, ensuring high PPG signal-to-noise ratios is a fundamental requirement for a robust ... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/JBHI.2019.2909065 | IEEE Journal of Biomedical and Health Informatics |
Keywords | Field | DocType |
Biomedical monitoring,Stroke (medical condition),Quality assessment,Electrocardiography,Monitoring,Atrial fibrillation,Rhythm | Computer vision,Pattern recognition,Photoplethysmogram,Computer science,Artificial intelligence | Journal |
Volume | Issue | ISSN |
24 | 3 | 2168-2194 |
Citations | PageRank | References |
2 | 0.38 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tânia Pereira | 1 | 24 | 8.61 |
| Kais Gadhoumi | 2 | 2 | 1.05 |
| Mitchell Ma | 3 | 2 | 0.38 |
| Liu Xiuyun | 4 | 2 | 0.38 |
| Ran Xiao | 5 | 7 | 2.09 |
| Rene Colorado | 6 | 2 | 1.05 |
| Kevin J. Keenan | 7 | 2 | 0.71 |
| Karl Meisel | 8 | 2 | 1.05 |
| Xiao Hu | 9 | 72 | 13.64 |