Adaptive Fourier Decomposition Approach to ECG Denoising

Abstract

In this work, a novel signal decomposition method named Adaptive Fourier Decomposition (AFD) is investigated, which can decompose signals to some mono-components that only contain positive phase derivatives based on their energy distributions. With such nice characteristics, the AFD is applied removing noise from ECG signals. More specifically, a judgment is defined based on the estimated signal-to-noise ratio of a noisy signal to stop the recursive AFD process, with which a novel AFD-based denoising algorithm is proposed for ECG signals. In validation, artificial and real ECG signals from the MIT-BIH Arrhythmia Database with additive Gaussian white noise, muscle and electrode motion artifacts are used. Moreover, four other denoising methods based on the Fourier transform, the wavelet transform, the empirical mode decomposition and the ensemble empirical mode decomposition are used to compare with the AFD-based denoising method. The simulation results indicate that the proposed AFD-based method performs mostly the best. In addition, from the simulation study, two rules of the AFD are concluded which can be used to choose and adjust the decomposition level of the AFD for denoising. In summary, this report shows that the AFD is a promising tool for ECG signal denoising.