Title :
Compressed Sensing for Bioelectric Signals: A Review
Author :
Craven, Darren ; McGinley, Brian ; Kilmartin, Liam ; Glavin, Martin ; Jones, Edward
Author_Institution :
Coll. of Eng. & Inf., Nat. Univ. of Ireland, Galway, Ireland
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.
Keywords :
bioelectric potentials; compressed sensing; electrocardiography; electroencephalography; mean square error methods; medical signal processing; signal denoising; ECG; EEG; bioelectric signal compression applications; biosignals; compressed sensing; compressing electrocardiogram; compressive sampling; electroencephalography; embedded hardware; percent root-mean-squared difference; performance metrics; power consumption; signal-to-noise ratio; state-of-the-art compression algorithms; Compressed sensing; Dictionaries; Electrocardiography; Electroencephalography; Matching pursuit algorithms; Sparse matrices; Bioelectric signal compression; body area networks (BAN); compressed sensing (CS); electrocardiogram (ECG); electroencephalography (EEG);
Journal_Title :
Biomedical and Health Informatics, IEEE Journal of
DOI :
10.1109/JBHI.2014.2327194
