NONLINEAR FILTERS TO RECONSTRUCT ELECTROCARDIOGRAM SIGNALS

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 activ...

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Autores Principales: Infante, Saba, Sánchez, Luis, Cedeño, Fernando
Formato: Artículo
Idioma: Español
Publicado: 2015
Materias:
Acceso en línea: http://revistas.ucr.ac.cr/index.php/matematica/article/view/15182
http://hdl.handle.net/10669/13054
Sumario: 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 algo- rithms: generic particle filter (GPF), resampling particle filter (RPF), un- scented 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 recon- struction 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 simu- lated 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.