ECG signal compressed sensing using the wavelet tree model

2015 ◽  
Author(s):  
Zhicheng Li ◽  
Yang Deng ◽  
Hong Huang ◽  
Satyajayant Misra
Keyword(s):  
Compressed Sensing ◽  
Ecg Signal ◽  
Tree Model

scholarly journals A Case Study in Low-Complexity ECG Signal Encoding: How Compressing is Compressed Sensing?

2015 ◽  
Vol 22 (10) ◽  
pp. 1743-1747 ◽  
Author(s):  
Valerio Cambareri ◽  
Mauro Mangia ◽  
Fabio Pareschi ◽  
Riccardo Rovatti ◽  
Gianluca Setti
Keyword(s):  
Compressed Sensing ◽  
Low Complexity ◽  
Ecg Signal ◽  
Signal Encoding

Compressed Sensing based on Wavelet Tree Model in Wireless Sensor Networks

2013 ◽  
Vol 8 (9) ◽  
pp. 995-1003
Author(s):  
Weijun Su ◽  
Honghong Wang ◽  
Chongchong Yu ◽  
Mingxing Li
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Compressed Sensing ◽  
Wireless Sensor ◽  
Tree Model

scholarly journals Tree-Based Backtracking Orthogonal Matching Pursuit for Sparse Signal Reconstruction

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yigang Cen ◽  
Fangfei Wang ◽  
Ruizhen Zhao ◽  
Lihong Cui ◽  
Lihui Cen ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Optimization Problem ◽  
Selection Process ◽  
Matching Pursuit ◽  
Signal Reconstruction ◽  
Search Space ◽  
Orthogonal Matching Pursuit ◽  
Superior Performance ◽  
Sparse Signal ◽  
Tree Model

Compressed sensing (CS) is a theory which exploits the sparsity characteristic of the original signal in signal sampling and coding. By solving an optimization problem, the original sparse signal can be reconstructed accurately. In this paper, a new Tree-based Backtracking Orthogonal Matching Pursuit (TBOMP) algorithm is presented with the idea of the tree model in wavelet domain. The algorithm can convert the wavelet tree structure to the corresponding relations of candidate atoms without any prior information of signal sparsity. Thus, the atom selection process will be more structural and the search space can be narrowed. Moreover, according to the backtracking process, the previous chosen atoms’ reliability can be detected and the unreliable atoms can be deleted at each iteration, which leads to an accurate reconstruction of the signal ultimately. Compared with other compressed sensing algorithms, simulation results show the proposed algorithm’s superior performance to that of several other OMP-type algorithms.

scholarly journals Modified Block Compressed Sensing for Extraction of Fetal Electrocardiogram from Mother Electrocardiogram Using Block Compressed Sensing Based Guided FOCUSS and FAST-Independent Component

2021 ◽  
Vol 50 (1) ◽  
pp. 123-137
Author(s):  
Muhammad Tayyib Awan ◽  
Muhammad Amir ◽  
Sarmad Maqsood ◽  
Musyyab Yousufi ◽  
Suheel Abdullah ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Compression Ratio ◽  
Ecg Signal ◽  
High Compression Ratio ◽  
Sensing Matrix ◽  
Compression Technique ◽  
High Compression ◽  
Fetal Ecg ◽  
Block Compressed Sensing ◽  
Modified Block

Fetal ECG extraction from abdominal ECG is critical task for telemonitoring of fetus which require lot of understanding to the subject. Conventional source separation methods are not efficient enough to separate FECG from huge multichannel ECG. Thus use of compression technique is needed to compress and reconstruct ECG signal without any significant losses in quality of signal. Compressed sensing shows promising results for such tasks. However, current compressed sensing theory is not so far that successful due to the non-sparsity and strong noise contamination present in ECG signal. The proposed work explores the concept of block compressed sensing to reconstruct non-sparse FECG signal using GFOCUSS algorithm. The main objective of this paper is not only to successfully reconstruct the ECG signal but to efficiently separate FECG from abdominal ECG. The proposed algorithm is explained in very extensive manner for all experiments. The key feature of proposed method is, that it doesn’t affect the interdependence relation between multichannel ECG. The useof walsh sensing matrix made it possible to achieve high compression ratio. Experimental results shows that even at very high compression ratio, successful FECG reconstruction from raw ECG is possible. These results are validated using PSNR, SINR, and MSE. This shows the framework, compared to other algorithms such as current blocking CS algorithms, rackness CS algorithm and wavelet algorithms, can greatly reduce code execution time during data compression stage and achieve better reconstruction in terms of MSE, PSNR and SINR.

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