Nonlinear filters to reconstruct electrocardiogram signals
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| Autores: | , , |
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| Formato: | artículo original |
| Estado: | Versión publicada |
| Fecha de Publicación: | 2014 |
| Descripción: | ECG signals have been used in cardiac pathology to detect disease heart. The main objective of this paper is to propose signal filtering techniques to reduce noise, extract information, to reconstruct the states and properties Morphological heartbeat. In addition, aims to represent the cardiac activity in a simple, informative, accurate, and easy to interpret for cardiologists. To achieve these objectives are proposed to implement the following algorithms: generic particle filter (GPF), resampling particle filter (RPF), unscented Kalman filter (UKF) and the unscented particle filter (UFP) con- sidering the basic structure of synthetic dynamic model McSharry et al. (2003) [16]. The results show that filter performs very well in the reconstruction of the states heart rate system, while introducing small variations in the variances of the noises of the equation observation, ie, the methods have the ability to reproduce the original signal the synthetic model simulated and the synthetic model with real data accurately. Finally evaluates the performance of the filters in terms of the empirical standard deviation, showing little variability among the estimated errors and fast execution of algorithms. |
| País: | Portal de Revistas UCR |
| Institución: | Universidad de Costa Rica |
| Repositorio: | Portal de Revistas UCR |
| Lenguaje: | Español |
| OAI Identifier: | oai:portal.ucr.ac.cr:article/15182 |
| Acceso en línea: | https://revistas.ucr.ac.cr/index.php/matematica/article/view/15182 |
| Palabra clave: | synthetic ECG model nonlinear filters morphology of waves modelo sintético ECG filtros no lineales morfología de las ondas |