Third-order SVD based denoising of multi-channel ECG

Abstract

Abstract Denoising of time series is still a challenge, especially when the spectral components of wanted signal and noise overlap. However, denoising is an inverse problem and its solution is of often ambiguous. Standard methods often use predefined functions, e.g., sine waves, to decompose a time series into wanted and unwanted parts. More recent methods calculate the basis for the representation of a time series by the time series itself. Such a method is singular spectrum analysis (SSA). SSA uses Hankel matrix embedding of a time series and singular value decomposition to determine wanted and unwanted components, e.g., noise. The wanted components are then used to compute the wanted part of the time series. Here we present a method that provides an extension of SSA for analyzing and - in particular - denoising multi-channel time series, i.e., multi-channel SSA-based denoising of multi-channel time series (MSSAD). The performance of the new method, which is based on Hankel embedding and tensor decomposition, is demonstrated on a 12-lead ECG.