Paper Info
Title
Compressed sensing ECG using restricted Boltzmann machines.
Abstract
Recently, it has been shown that compressed sensing (CS) has the potential to lower energy consumption in wireless electrocardiogram (ECG) systems. By reducing the number of acquired measurements, the communication burden is decreased and energy is saved. In this paper, we aim at further reducing the number of necessary measurements to achieve faithful reconstruction by exploiting the representational power of restricted Boltzmann machines (RBMs) to model the probability distribution of the sparsity pattern of ECG signals. The motivation for using this approach is to capture the higher-order statistical dependencies between the coefficients of the ECG sparse representation, which in turn, leads to superior reconstruction accuracy and reduction in the number of measurements, as it is shown via experiments.
Year
DOI
Venue
2018
10.1016/j.bspc.2018.05.022
Biomedical Signal Processing and Control
Keywords
Field
DocType
Electrocardiogram (ECG),Wireless body area networks (WBAN),Compressed sensing (CS),Overcomplete dictionaries,Restricted Boltzmann machine (RBM)
Boltzmann machine,Wireless,Computer science,Sparse approximation,Algorithm,Probability distribution,Energy consumption,Compressed sensing
Journal
Volume
ISSN
Citations 
45
1746-8094
0
PageRank 
References 
Authors
0.34
16
2
Name
Order
Citations
PageRank
Luisa F. Polania11319.54
Rafael I. Plaza200.34