Name
Abstract | ||
|---|---|---|
Two versions of a new detector for automatic real-time detection of atrial fibrillation in non-invasive ECG signals are introduced. The methods are based on beat to beat variability, tachogram analysis and simple signal filtering. The implementation on mobile devices is made possible due to the low demand on computing power of the employed analysis procedures. The proposed algorithms correctly identified 436 of 440 five minute episodes of atrial fibrillation or flutter and also correctly identified up to 302 of 342 episodes of no atrial fibrillation, including normal sinus rhythm as well as other cardiac arrhythmias. These numbers correspond to a sensitivity of 99.1% and a specificity of 88.3%. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-11721-3_20 | BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES |
Keywords | Field | DocType |
real time,mobile device | Atrial fibrillation,Data mining,Internal medicine,Computer science,Cardiology,Flutter,Filter (signal processing),Normal Sinus Rhythm,Speech recognition,Mobile device,Beat (music),Detector | Conference |
Volume | ISSN | Citations |
52 | 1865-0929 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefanie Kaiser | 1 | 0 | 0.68 |
| Malte Kirst | 2 | 5 | 2.61 |
| Christophe Kunze | 3 | 20 | 7.42 |