Name
Abstract | ||
|---|---|---|
One may monitor the heart normal activity by analyzing the electrocardiogram. We propose in this paper to combine the principle of kernel machines, that maps data into a high dimensional feature space, with the autoregressive (AR) technique defined using the Yule-Walker equations, which predicts future samples using a combination of some previous samples. A pre-image technique is applied in order to get back to the original space in order to interpret the predicted sample. The relevance of the proposed method is illustrated on real electrocardiogram from the MIT benchmark. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICTEL.2012.6221217 | Telecommunications |
Keywords | Field | DocType |
autoregressive processes,electrocardiography,medical signal processing,Yule-Walker equations,autoregressive technique,electrocardiogram modeling,high dimensional feature space,kernel machines principle,preimage technique,ECG signals,autoregressive model,kernel machines,nonlinear models,pre-image problem | Yule walker equations,Kernel (linear algebra),Autoregressive model,Time series,Feature vector,Pattern recognition,Computer science,Artificial intelligence | Conference |
ISBN | Citations | PageRank |
978-1-4673-0746-8 | 2 | 0.37 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kallas, M. | 1 | 2 | 0.37 |
| Clovis Francis | 2 | 34 | 11.20 |
| Honeine, P. | 3 | 11 | 1.92 |
| Amoud, H. | 4 | 7 | 0.82 |