Noise Filtering Using FFT, Bayesian Model and Trend Model for Time Series Data

Abstract

The applicability of noise filtering methods, FFT, Bayesian model and trend model was evaluated using multiple nonstationary frequencies of noise and electrocardiogram with large signal-to-noise ratio. The results show that the Bayesian model is the most applicable to filtering of multiple nonstationary frequencies with large noise. In the case the best way for filtering out the noise in electrocardiogram suggested that the background rolling noise should be eliminated by FFT after removing the high frequency component by Bayesian or trend model.