On-line Local Mean Decomposition and its application to ECG signal denoising

Abstract

Local Mean Decomposition (LMD) has long been proven as an effective method for the analysis of non-linear and non-stationary time series. In this work, an on-line version of LMD, called extended Sliding Local Mean Decomposition (eSLMD), is proposed. The property of eSLMD is examined through numerical simulations, and the performance is evaluated through the ECG noise removal with the test signal obtained from MIT-BIH arrhythmia ECG database. The results show that the proposed eSLMD has better decomposition performance than conventional LMD, and is potentially well suited for on-line and real-time biomedical applications.