Optimisation of electrode placement for new ambulatory ECG monitoring devices

Alan Kennedy, Dewar D. Finlay, Daniel Guldenring, Raymond Bond, Kieran Moran, James McLaughlin

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    In this study we aim to determine, from body surface potential map (BSPM) data, the optimal bipolar chest electrode placement for maximum R-wave amplitude. The study data consisted of 117-lead 352-node BSPM data recorded from 229 healthy subjects. The dataset was split into a training set of 172 subjects and a testing set of the remaining 57 subjects. Optimal electrode placement was determined using a lead selection method based on the difference in R-wave amplitude across all 352 nodes for each patient. R-wave values were then extracted and used to create a median BSPM of the training data. From this median BSPM the optimal electrode placement was defined as the location of the minimum and maximum R-wave values. On the testing dataset this new optimal bipolar chest lead (R-lead) was then compared to all of the leads of the Mason-Likar 12-lead ECG and previously described bipolar chest leads, CM5, CS5, CC5 and CB5. The R-lead showed significant improvement in median R-wave amplitude over the next best lead, CM5 (2562μV vs. 2420μV, Wilcoxon sign ranked test, p< 0.001). Given the improvement in signal strength, an improvement in automated R-wave detection and R-R interval analysis from single lead ECG monitors may be achieved.

    Original languageEnglish
    Title of host publicationComputing in Cardiology Conference, CinC 2016
    EditorsAlan Murray
    PublisherIEEE Computer Society
    Pages101-104
    Number of pages4
    ISBN (Electronic)9781509008964
    Publication statusPublished (in print/issue) - 1 Mar 2016
    Event43rd Computing in Cardiology Conference, CinC 2016 - Vancouver, Canada
    Duration: 11 Sept 201614 Sept 2016

    Publication series

    NameComputing in Cardiology
    Volume43
    ISSN (Print)2325-8861
    ISSN (Electronic)2325-887X

    Conference

    Conference43rd Computing in Cardiology Conference, CinC 2016
    Country/TerritoryCanada
    CityVancouver
    Period11/09/1614/09/16

    Bibliographical note

    Funding Information:
    This work has been supported by the Northern Ireland Connected Health Innovation Centre and the PATHway project funded by the European Commission under the Horizon 2020 Programme (Call H2020-PHC-2014, Grant no. 643491)

    Publisher Copyright:
    © 2016 CCAL.

    Copyright:
    Copyright 2017 Elsevier B.V., All rights reserved.

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