Name
Title | ||
|---|---|---|
Hidden Markov models compared to the wavelet transform for P-wave segmentation in EGC signals |
Abstract | ||
|---|---|---|
The aim of this study is to detect P-wave onset and end of electrocardiograms (ECG). This wave is important for detecting people prone to atrial fibrillation, one of the most frequent heart diseases, but the wave is very difficult to segment accurately because of its small amplitude and the very different shapes it can take. Two different methods are tested for the segmentation : the first one is based on Hidden Markov Models. Though results are good, some particular cases are not well segmented. However a second method based on the Continuous Wavelet Transform can solve those problems. |
Year | Venue | Keywords |
|---|---|---|
1998 | EUSIPCO | diseases,electrocardiography,hidden markov models,medical signal detection,medical signal processing,wavelet transforms,ecg signals,p-wave onset detection,p-wave segmentation,atrial fibrillation,continuous wavelet transform,electrocardiograms,heart diseases,kernel,databases,heart |
Field | DocType | ISBN |
Kernel (linear algebra),Pattern recognition,Segmentation,Speech recognition,Continuous wavelet transform,Artificial intelligence,Hidden Markov model,Amplitude,Mathematics,Wavelet transform | Conference | 978-960-7620-06-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laurent Clavier | 1 | 69 | 19.30 |
| Jean-Marc Boucher | 2 | 132 | 22.28 |
| Polard, E. | 3 | 0 | 0.34 |