Heart beat parametric modeling based on Monte Carlo fitting techniques

Abstract

Synthesis of electrocardiogram (ECG) signals is closely linked to the modeling process since precise knowledge of the parameters of the heartbeat to be modeled is required. The knowledge of these parameters is achieved through methods of adjusting curves between simulated beats and real beats. These traditional optimization methods, such as nonlinear least squares or similar, suffer from the drawback of falling at local minima especially when the initial conditions are not given in an accurate fashion. In the present work, we have designed a novel method robust to deviations in the initial conditions based on Monte Carlo techniques derived from the ideas of the Particle Filtering. Our method allows to adjust the heart beat and to determine the parameters of a model already known in the literature that consists of the sum of five Gaussian curves. The method fits with errors very similar to the traditional method when the initial conditions are good, but better results are obtained in terms of squared error when the initial conditions are sufficiently degraded. Validation was carried out with real physiological and pathological ECG records from international databases.