Bayesian inversion approach for ECG denoising

Abstract

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.