A robust algorithm for heart rate variability time series artefact correction using novel beat classification

Purpose: Heart rate variability is a commonly used measurement to evaluate functioning of autonomic nervous system, psychophysiological stress, and exercise intensity and recovery. HRV measurements contain artefacts such as extra, missed or misaligned beat detections, which can produce significant d...

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Bibliographic Details
Published in:Journal of medical engineering & technology 2019-04, Vol.43 (3), p.173-181
Main Authors: Lipponen, Jukka A., Tarvainen, Mika P.
Format: Article
Language:English
Subjects:
Algorithms
artefact correction
beat classification
Cardiac Complexes, Premature - classification
Cardiac Complexes, Premature - diagnosis
Databases, Factual
ectopic beat
Electrocardiography
Heart Rate - physiology
Heart rate variability
HRV
Humans
Reproducibility of Results
Sensitivity and Specificity
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Summary:Purpose: Heart rate variability is a commonly used measurement to evaluate functioning of autonomic nervous system, psychophysiological stress, and exercise intensity and recovery. HRV measurements contain artefacts such as extra, missed or misaligned beat detections, which can produce significant distortion on HRV parameters. In this paper, a robust automatic method for artefact detection from HRV time series is proposed. Methods: The proposed detection method is based on time-varying thresholds estimated from distribution of successive RR-interval differences combined with a novel beat classification scheme. The method is validated using simulated extra, missed and misaligned beat detections as well as real artefacts such as atrial and ventricular ectopic beats. Results: The sensitivity of the algorithm to detect simulated missed/extra beats was 100%. The sensitivity to detect real atrial and ventricular ectopic beats was 96.96%, the corresponding specificity being 99.94%. The mean error in HRV parameters after correction was
ISSN:0309-1902
1464-522X
DOI:10.1080/03091902.2019.1640306