Paper Info
Title
A comprehensive performance analysis of EEMD-BLMS and DWT-NN hybrid algorithms for ECG denoising.
Abstract
Electrocardiogram (ECG) is a widely used non-invasive method to study the rhythmic activity of the heart. These signals, however, are often obscured by artifacts/noises from various sources and minimization of these artifacts is of paramount importance for detecting anomalies. This paper presents a thorough analysis of the performance of two hybrid signal processing schemes ((i) Ensemble Empirical Mode Decomposition (EEMD) based method in conjunction with the Block Least Mean Square (BLMS) adaptive algorithm (EEMD-BLMS), and (ii) Discrete Wavelet Transform (DWT) combined with the Neural Network (NN), named the Wavelet NN (WNN)) for denoising the ECG signals. These methods are compared to the conventional EMD (C-EMD), C-EEMD, EEMD-LMS as well as the DWT thresholding (DWT-Th) based methods through extensive simulation studies on real as well as noise corrupted ECG signals. Results clearly show the superiority of the proposed methods. (C) 2015 Elsevier Ltd. All rights reserved.
Year
DOI
Venue
2016
10.1016/j.bspc.2015.11.012
Biomedical Signal Processing and Control
Keywords
Field
DocType
Electrocardiogram (ECG),Denoising,Ensemble empirical mode decomposition (EEMD),Block least mean square (BLMS),Discrete Wavelet Transform (DWT),Neural Networks (NN)
Least mean squares filter,Noise reduction,Signal processing,Pattern recognition,Algorithm,Discrete wavelet transform,Artificial intelligence,Adaptive algorithm,Thresholding,Mathematics,Wavelet,Hilbert–Huang transform
Journal
Volume
ISSN
Citations 
25
1746-8094
5
PageRank 
References 
Authors
0.44
14
3
Name
Order
Citations
PageRank
Kevin Kaergaard150.44
Søren Hjøllund Jensen250.44
Sadasivan Puthusserypady318127.49