Impact of compressed sensing on clinically relevant metrics for ambulatory ECG monitoring. Issue 4 (1st February 2015)

• Record Type: Journal Article
• Authors: Craven, D.; McGinley, B.; Kilmartin, L.; Glavin, M.; Jones, E.
• Is Part Of: Electronics letters. Volume 51:Issue 4(2015)
• Page Start: 323
• Page End: 325
• Publication Date: 2015-02-01
• Subjects: electrocardiography -- compressed sensing -- signal reconstruction -- medical signal processing -- medical diagnostic computing -- feature extraction -- patient monitoring -- cardiovascular system -- biomedical electronics; ECG monitoring compressed sensing impact -- ECG monitoring CS impact -- electrocardiogram monitoring CS impact -- ambulatory ECG monitoring -- ambulatory electrocardiogram monitoring -- clinically relevant ECG monitoring metrics -- clinically relevant electrocardiogram monitoring metrics -- over‐complete dictionaries -- lossy compression -- ECG signals -- electrocardiogram signals -- dictionary learning -- DL application -- automatic dictionary creation -- dictionary creation methods -- patient specific dictionary creation method analysis -- patient agnostic dictionary creation method analysis -- reconstructed ECG signal analysis -- reconstructed electrocardiogram signal analysis -- QRS feature extraction‐based clinical metrics -- heart rate variability‐based clinical metrics -- HRV‐based clinical metrics -- compression ratio; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2014.4188
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : Recent research has examined the combination of compressed sensing with over‐complete dictionaries for the lossy compression of electrocardiogram (ECG) signals. The application of dictionary learning to automatically create the dictionary is described. A novel analysis of the reconstructed signals using a range of clinical metrics based around QRS feature extraction and heart rate variability is employed. Two methods for dictionary creation are proposed: patient specific and patient agnostic. A detailed comparison of each approach is described. Considering ambulatory ECG monitoring as an application, each methodology is analysed for a wide range of compression ratios.