CAETI
Centro de Altos Estudios en Tecnología Informática
Buenos Aires
Ingeniería de Software
Reconocimiento de patrones en señales biomédicas para la detección temprana de eventos no fisiológicos
Bayesian inversion approach for ECG denoising
Año: 2019
Idioma: Inglés
Resumen
Stochastic or Bayesian filtering is an inverse problem in the sense that from given noisy observations we want to estimate hidden state variables knowing models for states evolution and measurement noises. In the present work we propose a Particle Filter method for denoising ECG signals based on Monte Carlo filter techniques estimating the state (filtered signal value) from noisy observations simulated with different SNRs. We use Mc Sharry dynamical model whose solution trajectories reproduce realistic ECG waves. The improvement in the denoised signal is higher when the SNR in the input signal is lower. Particle Filter method allows any noise distribution to be considered. This property is very interesting for physiological signal processing, where the noise is often complex and non Gaussian.
Integrantes
Maria Lorena Bergamini (Autor)
Sergio Javier Liberczuk (Autor)
Archivos
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María Lorena Bergamini and Sergio Javier Liberczuk (2019). “Bayesian Inversion Approach for ECG Denoising” en Matema´tica Aplicada, Computacional e Industrial MACI Vol. 7, 2019. Proceedings of VII MACI 2019. Editado por Asociación Argentina de Matemática Aplicada, Computacional e Industrial, ASAMACI. Luis R. Ceballos, Claudia M. Gariboldi y Bruno A. Roccia (Eds). ISSN: 2314-3282, pp. 417-420, Mayo 2019.
XIII- Bergamini-Liberczuk - Revista MACI 2019.pdf