Name
Abstract | ||
|---|---|---|
A new electrocardiogram (ECG) delineation method is proposed, which uses a hidden Markov tree model. The aim of this approach is, on the one hand, to use wavelet coefficients to characterize the different ECG waves, and, on the other hand, to link these coefficients by a tree structure enabling wave change to be detected. By associating this method with a fusion method between scales based on the concept of context, good results are obtained on a special database created for the risk analysis of atrial fibrillation, particularly in P-wave delineation. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/TIM.2005.858568 | Instrumentation and Measurement, IEEE Transactions |
Keywords | Field | DocType |
electrocardiography,hidden Markov models,medical signal detection,tree data structures,wavelet transforms,ECG wave delineation,atrial fibrillation,electrocardiogram,hidden Markov tree model,risk analysis,wavelet coefficients,wavelet tree,ECG wave delineation,P-wave,T-wave,hidden Markov model,segmentation,wavelet tree | Hidden markov tree model,Pattern recognition,Segmentation,Tree (data structure),Wavelet Tree,Tree structure,Artificial intelligence,Hidden Markov model,Mathematics,Wavelet,Wavelet transform | Journal |
Volume | Issue | ISSN |
54 | 6 | 0018-9456 |
Citations | PageRank | References |
15 | 1.27 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salim Graja | 1 | 15 | 1.27 |
| Jean-Marc Boucher | 2 | 132 | 22.28 |