Compressed sensing for bioelectric signals: A review

Darren Craven; Brian McGinley; Liam Kilmartin; Martin Glavin; Edward Jones

doi:10.1109/JBHI.2014.2327194

Compressed sensing for bioelectric signals: A review

Darren Craven
, Brian McGinley
, Liam Kilmartin
, Martin Glavin
, Edward Jones

Electrical & Electronic Engineering

University of Galway

Research output: Contribution to a Journal (Peer & Non Peer) › Article › peer-review

169 Citations (Scopus)

Abstract

This paper provides a comprehensive review of compressed sensing or compressive sampling (CS) in bioelectric signal compression applications. The aim is to provide a detailed analysis of the current trends in CS, focusing on the advantages and disadvantages in compressing different biosignals and its suitability for deployment in embedded hardware. Performance metrics such as percent root-mean-squared difference (PRD), signal-to-noise ratio (SNR), and power consumption are used to objectively quantify the capabilities of CS. Furthermore, CS is compared to state-of-the-art compression algorithms in compressing electrocardiogram (ECG) and electroencephalography (EEG) as examples of typical biosignals. The main technical challenges associated with CS are discussed along with the predicted future trends.

Original language	English
Article number	6822522
Pages (from-to)	529-540
Number of pages	12
Journal	IEEE Journal of Biomedical and Health Informatics
Volume	19
Issue number	2
DOIs	https://doi.org/10.1109/JBHI.2014.2327194
Publication status	Published - 1 Mar 2015

Keywords

Bioelectric signal compression
body area networks (BAN)
compressed sensing (CS)
electrocardiogram (ECG)
electroencephalography (EEG)

Authors (Note for portal: view the doc link for the full list of authors)

Authors
Creaven, D; McGinley, B; Kilmartin, L; Glavin, M; Jones, E
Craven, D,McGinley, B,Kilmartin, L,Glavin, M,Jones, E

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10.1109/JBHI.2014.2327194

Cite this

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abstract = "This paper provides a comprehensive review of compressed sensing or compressive sampling (CS) in bioelectric signal compression applications. The aim is to provide a detailed analysis of the current trends in CS, focusing on the advantages and disadvantages in compressing different biosignals and its suitability for deployment in embedded hardware. Performance metrics such as percent root-mean-squared difference (PRD), signal-to-noise ratio (SNR), and power consumption are used to objectively quantify the capabilities of CS. Furthermore, CS is compared to state-of-the-art compression algorithms in compressing electrocardiogram (ECG) and electroencephalography (EEG) as examples of typical biosignals. The main technical challenges associated with CS are discussed along with the predicted future trends.",

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Compressed sensing for bioelectric signals: A review

Abstract

Keywords

Authors (Note for portal: view the doc link for the full list of authors)

Access to Document

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