Vol.33, No.1, 2022, pp.415-428, doi:10.32604/iasc.2022.022860
OPEN ACCESS
ARTICLE
Optimized Compressive Sensing Based ECG Signal Compression and Reconstruction
  • Ishani Mishra1,*, Sanjay Jain2
1 Department of ECE, New Horizon College of Engineering, Bengaluru, 560103, India
2 Department of ECE, CMR Institute of Technology, Bangalore, 560037, India
* Corresponding Author: Ishani Mishra. Email:
Received 20 August 2021; Accepted 11 October 2021; Issue published 05 January 2022
Abstract
In wireless body sensor network (WBSN), the set of electrocardiograms (ECG) data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient. However, due to the size of the ECG data, the performance of the signal compression and reconstruction is degraded. For efficient wireless transmission of ECG data, compressive sensing (CS) frame work plays significant role recently in WBSN. So, this work focuses to present CS for ECG signal compression and reconstruction. Although CS minimizes mean square error (MSE), compression rate and reconstruction probability of the CS is further to be improved. In this paper, we provide an efficient compressive sensing framework which strives to improve the reconstruction process, by adjusting the sensing matrix during the compression phase using the rain optimization algorithm (ROA). With the optimal sensing matrix, the compressed signal is reconstructed using Step Size optimized Sparsity Adaptive Matching Pursuit algorithm (SAMP). The results of this work demonstrate that the optimised CS framework achieves a higher compression rate and probability of reconstruction than the standard CS framework.
Keywords
Wireless body sensor network (WBSN); optimized compressive sensing; step size optimized sparsity adaptive matching pursuit algorithm (SAMP); rain optimization algorithm (ROA)
Cite This Article
I. Mishra and S. Jain, "Optimized compressive sensing based ecg signal compression and reconstruction," Intelligent Automation & Soft Computing, vol. 33, no.1, pp. 415–428, 2022.
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