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  • A preprocessing tool for removing artifact from cardiac RR interval recordings using three-dimensional spatial distribution mapping

    Author(s)
    Stapelberg, Nicolas JC
    Neumann, David L
    Shum, David HK
    Mcconnell, Harry
    Hamilton-Craig, Ian
    Griffith University Author(s)
    Neumann, David L.
    Hamilton-Craig, Ian
    Year published
    2016
    Metadata
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    Abstract
    Artifact is common in cardiac RR interval data that is recorded for heart rate variability (HRV) analysis. A novel algorithm for artifact detection and interpolation in RR interval data is described. It is based on spatial distribution mapping of RR interval magnitude and relationships to adjacent values in three dimensions. The characteristics of normal physiological RR intervals and artifact intervals were established using 24-h recordings from 20 technician-assessed human cardiac recordings. The algorithm was incorporated into a preprocessing tool and validated using 30 artificial RR (ARR) interval data files, to which ...
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    Artifact is common in cardiac RR interval data that is recorded for heart rate variability (HRV) analysis. A novel algorithm for artifact detection and interpolation in RR interval data is described. It is based on spatial distribution mapping of RR interval magnitude and relationships to adjacent values in three dimensions. The characteristics of normal physiological RR intervals and artifact intervals were established using 24-h recordings from 20 technician-assessed human cardiac recordings. The algorithm was incorporated into a preprocessing tool and validated using 30 artificial RR (ARR) interval data files, to which known quantities of artifact (0.5%, 1%, 2%, 3%, 5%, 7%, 10%) were added. The impact of preprocessing ARR files with 1% added artifact was also assessed using 10 time domain and frequency domain HRV metrics. The preprocessing tool was also used to preprocess 69 24-h human cardiac recordings. The tool was able to remove artifact from technician-assessed human cardiac recordings (sensitivity 0.84, SD = 0.09, specificity of 1.00, SD = 0.01) and artificial data files. The removal of artifact had a low impact on time domain and frequency domain HRV metrics (ranging from 0% to 2.5% change in values). This novel preprocessing tool can be used with human 24-h cardiac recordings to remove artifact while minimally affecting physiological data and therefore having a low impact on HRV measures of that data.
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    Journal Title
    Psychophysiology
    Volume
    53
    Issue
    4
    DOI
    https://doi.org/10.1111/psyp.12598
    Subject
    Biological sciences
    Biomedical and clinical sciences
    Psychology
    Other psychology not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/142566
    Collection
    • Journal articles

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