TITLE:
Robust Low-Power Algorithm for Random Sensing Matrix for Wireless ECG Systems Based on Low Sampling-Rate Approach
AUTHORS:
Mohammadreza Balouchestani, Kaamran Raahemifar, Sridhar krishnan
KEYWORDS:
Sensing Matrix; Power Consumption; Normal and Abnormal ECG Signal; Compressed Sensing; Block Sparse Bayesian learning
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.4 No.3B,
October
17,
2013
ABSTRACT: The main drawback of current ECG
systems is the location-specific nature of the systems due to the use of fixed/wired
applications. That is why there is a critical need to improve the current ECG
systems to achieve extended patient’s mobility and to cover security handling. With
this in mind, Compressed Sensing (CS) procedure and the collaboration of
Sensing Matrix Selection (SMS) approach are used to provide a robust
ultra-low-power approach for normal and abnormal ECG signals. Our simulation
results based on two proposed algorithms illustrate 25% decrease in
sampling-rate and a good level of quality for the degree of incoherence between
the random measurement and sparsity matrices. The simulation results also
confirm that the Binary Toeplitz Matrix (BTM) provides the best compression
performance with the highest energy efficiency for random sensing matrix.