Modeling electrocardiogram using Yule-Walker equations and kernel machines
Résumé
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.
Mots clés
machine learning
adaptive filtering
medical signal processing
electrocardiogram modeling
Yule-Walker equations
kernel machines principle
high dimensional feature space
autoregressive technique
preimage technique
Kernel
Mathematical model
Equations
Electrocardiography
Heart
Time series analysis
Autoregressive processes
kernel machines
ECG signals
autoregressive model
nonlinear models
pre-image problem
| Origine | Fichiers produits par l'(les) auteur(s) |
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