Modeling electrocardiogram using Yule-Walker equations and kernel machines

Maya Kallas (1, 2) , Clovis Francis (1) , Paul Honeine (2) , Hassan Amoud (1) , Cédric Richard (3)

1 LU / ULB - الجامعة اللبنانية [بيروت] = Lebanese University [Beirut] = Université libanaise [Beyrouth]
2 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
3 LAGRANGE - Joseph Louis LAGRANGE

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

Domaines: Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Maya Kallas, Clovis Francis, Paul Honeine, Hassan Amoud, Cédric Richard. Modeling electrocardiogram using Yule-Walker equations and kernel machines. Proc. 19th International Conference on Telecommunications (ICT), 2012, Jounieh, Lebanon. pp.1-5, ⟨10.1109/ICTEL.2012.6221217⟩. ⟨hal-01966017⟩