Research on ECG signal reconstruction based on improved weighted nuclear norm minimization and approximate message passing algorithm

In order to improve the energy efficiency of wearable devices, it is necessary to compress and reconstruct the collected electrocardiogram data. The compressed data may be mixed with noise during the transmission process. The denoising-based approximate message passing (AMP) algorithm performs well...

Full description

Saved in:
Bibliographic Details
Published in:Frontiers in neuroinformatics Vol. 18; p. 1454244
Main Authors: Zhang, Bing, Zhu, Xishun, Khan, Fadia Ali, Jamal, Sajjad Shaukat, Mazroa, Alanoud Al, Nawaz, Rab
Format: Journal Article
Language:English
Published: Switzerland Frontiers Research Foundation 08.10.2024
Frontiers Media S.A
Subjects:
Algorithms
approximate message passing algorithm
compressed sensing
Convex analysis
Decomposition
Dictionaries
ECG
EKG
Electrocardiography
Neuroscience
non-local similarity
Optimization
Sparsity
Wavelet transforms
weighted nuclear norm minimization
ECG
weighted nuclear norm minimization
non-local similarity
approximate message passing algorithm
compressed sensing
ISSN:1662-5196, 1662-5196
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In order to improve the energy efficiency of wearable devices, it is necessary to compress and reconstruct the collected electrocardiogram data. The compressed data may be mixed with noise during the transmission process. The denoising-based approximate message passing (AMP) algorithm performs well in reconstructing noisy signals, so the denoising-based AMP algorithm is introduced into electrocardiogram signal reconstruction. The weighted nuclear norm minimization algorithm (WNNM) uses the low-rank characteristics of similar signal blocks for denoising, and averages the signal blocks after low-rank decomposition to obtain the final denoised signal. However, under the influence of noise, there may be errors in searching for similar blocks, resulting in dissimilar signal blocks being grouped together, affecting the denoising effect. Based on this, this paper improves the WNNM algorithm and proposes to use weighted averaging instead of direct averaging for the signal blocks after low-rank decomposition in the denoising process, and validating its effectiveness on electrocardiogram signals. Experimental results demonstrate that the IWNNM-AMP algorithm achieves the best reconstruction performance under different compression ratios and noise conditions, obtaining the lowest PRD and RMSE values. Compared with the WNNM-AMP algorithm, the PRD value is reduced by 0.17∼4.56, the P-SNR value is improved by 0.12∼2.70.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
Feng Liu, Nankai University, China
Ninad Mehendale, K J Somaiya College Of Engineering, India
Reviewed by: Soumyajit Mandal, Brookhaven National Laboratory (DOE), United States
Edited by: Andrés Úbeda, University of Alicante, Spain
ISSN:1662-5196
1662-5196
DOI:10.3389/fninf.2024.1454244