DocumentCode
3748994
Title
A LightWAVE client for semi-automated annotation of Heart Beats from ECG Time Series
Author
Luca Citi;Claudia Olariu;Riccardo Barbieri
Author_Institution
School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
fYear
2015
Firstpage
605
Lastpage
608
Abstract
LightWAVE is an open-source web-based software for viewing ECGs and other physiologic waveforms and associated annotations (such as heart-beat markers). At present, most users run the raw ECG through an automated QRS detector and later use LightWAVE to review and correct the detected heart beats. Although this 2-stage procedure may work well with clean signals, it is inefficient and time consuming when the recordings are contaminated by noise, artefacts or recurring ectopic events. To overcome this limitation, we customized the LightWAVE client to allow automated and semi-automated annotation of heart beats from ECG time series. In semi-automatic mode, the algorithm automatically identifies most QRS complexes and stops - asking for manual intervention - whenever the confidence of a detection falls below a given threshold. Additionally, the software now shows the series of inter-beat intervals, which is an invaluable tool to easily spot R-wave misdetections and genuine arrhythmias. The new client introduces further additional features compared to the standard version, for example the possibility of importing raw signals from local CSV files and of exporting the current plot in SVG format. Overall, our customized client extends the functionality of LightWAVE and brings it closer to one of its design goals, i.e. to provide a comfortable and efficient method of annotating physiologic data.
Keywords
"Electrocardiography","Heart beat","Physiology","Detection algorithms","Software","Software algorithms","Servers"
Publisher
ieee
Conference_Titel
Computing in Cardiology Conference (CinC), 2015
ISSN
2325-8861
Print_ISBN
978-1-5090-0685-4
Electronic_ISBN
2325-887X
Type
conf
DOI
10.1109/CIC.2015.7410983
Filename
7410983
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