DocumentCode :
704656
Title :
A trick to improve PRD during compressed sensing ECG reconstruction
Author :
Abhishek, S. ; Veni, S. ; Narayanankutty, K.A.
Author_Institution :
Dept. of Electron. & Commun. Eng., Amrita Vishwa Vidyapeetham, Coimbatore, India
fYear :
2015
fDate :
19-20 Feb. 2015
Firstpage :
174
Lastpage :
179
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.
Keywords :
compressed sensing; computational complexity; electrocardiography; matrix algebra; medical signal processing; signal reconstruction; PRD values; compressed sensing ECG reconstruction; identity matrices; matrix domain; mutual incoherence; null space bases; peak root mean square deviation; time algorithmic performance; Compressed sensing; Electrocardiography; Gaussian distribution; Null space; Random variables; Sensors; Sparse matrices; Blocked identity matrix; Compressed Sensing (CS); PRD; electrocardiography (ECG);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference on
Conference_Location :
Noida
Print_ISBN :
978-1-4799-5990-7
Type :
conf
DOI :
10.1109/SPIN.2015.7095319
Filename :
7095319
Link To Document :
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