Abstract
A novel noise filtering algorithm based on ensemble empirical mode decomposition (EEMD) is proposed to remove artifacts in electrocardiogram (ECG) traces. Three noise patterns with different power—50 Hz, EMG, and base line wander – were embedded into simulated and real ECG signals. Traditional IIR filter, Wiener filter, empirical mode decomposition (EMD) and EEMD were used to compare filtering performance. Mean square error between clean and filtered ECGs was used as filtering performance indexes. Results showed that high noise reduction is the major advantage of the EEMD based filter, especially on arrhythmia ECGs.
Highlights
Empirical mode decomposition (EMD) is a novel recently developed algorithm [1]
The goal of this study is to investigate ensemble empirical mode decomposition (EEMD) based filtering performance and the corresponding phase delay of filtered signals in arrhythmia ECGs
intrinsic mode function (IMF) distribution is very similar to a filter bank
Summary
Empirical mode decomposition (EMD) is a novel recently developed algorithm [1]. EMD is based on a decomposition derived from the data and is useful for the analysis of nonlinear and nonstationary time series signals [2]. Sensors 2010, 10 without a predefined cut-off frequency [2] This interesting property of EMD has been widely applied in biomedical signal analysis, such as monitoring the effect of anesthetic drugs [3], rapid screening of obstructive sleep apnea [4], and respiratory sinus arrhythmia estimation from ECGs [5]. Blanco-Velasco developed an EMD-based algorithm to remove the baseline wander and high-frequency noise of ECGs [10]. Nimunkar and Tompkin added a pseudo-high-frequency noise to IMFs as an aid to remove power-line noise. They developed a complete ECG processing algorithm for R-peak detection and feature extraction, based on EMD approaches [11]. Low-frequency baseline wander can be removed by reconstruction without higher IMF levels [12]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
