Publication Type:

Conference Paper

Source:

Second International Conference on Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference on, IEEE, Amity University, Noida (2015)

ISBN:

9781479959907

Accession Number:

15090274

URL:

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7095319&tag=1

Keywords:

Blocked identity matrix, Compressed sensing, Compressed Sensing (CS), compressed sensing ECG reconstruction, Computational complexity, Electrocardiography, electrocardiography (ECG), Gaussian distribution, identity matrices, Matrix algebra, matrix domain, medical signal processing, mutual incoherence, Null space, null space bases, peak root mean square deviation, PRD, PRD values, Random variables, Sensors, Signal reconstruction, Sparse matrices, time algorithmic performance

Abstract:

In the problem of compressed sensing (CS) successful reconstruction can be achieved by maintaining a low mutual coherence between the columns in the vector space. In this work, a way to increase the mutual incoherence is introduced. This is achieved by replacing certain matrix domain of the sparse random matrix, which is used as the measurement matrix with null space bases. For convenience, this can be replaced even by identity matrices. The result shows that there is a substantial improvement in Peak Root mean Square deviation (PRD). Many different alternatives have been tried out and relative PRD were plotted. Thresholding is generally adapted in CS in order to reduce the PRD values. It was found that without using thresholding technique, it is possible to obtain reduction in PRD values. The time algorithmic performance was also analyzed and found to be better.

Cite this Research Publication

S. Abhishek, Veni, S., and Narayanankutty, K. A., “A trick to improve PRD during compressed sensing ECG reconstruction”, in Second International Conference on Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference on, Amity University, Noida, 2015.