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J Electrocardiol. 2018 Nov - Dec;51(6S):S83-S87. doi: 10.1016/j.jelectrocard.2018.08.030. Epub 2018 Aug 23.

Wavelet leader multifractal analysis of heart rate variability in atrial fibrillation.

Author information

1
Department of Physiological Nursing, University of California, San Francisco, CA, USA. Electronic address: kais.gadhoumi@ucsf.edu.
2
David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
3
Center for Physiologic Research, University of California, San Francisco, CA, USA.
4
Department of Physiological Nursing, University of California, San Francisco, CA, USA.
5
Department of Physiological Nursing, University of California, San Francisco, CA, USA; Institute for Computational Health Sciences, University of California, San Francisco, CA, USA; Department of Neurological Surgery, University of California, San Francisco, CA, USA; Department of Neurosurgery, University of California, Los Angeles, CA, USA.

Abstract

BACKGROUND:

Accurate and timely detection of atrial fibrillation (AF) episodes is important in primarily and secondary prevention of ischemic stroke and heart-related problems. In this work, heart rate regularity of ECG inter-beat intervals was investigated in episodes of AF and other rhythms using a wavelet leader based multifractal analysis. Our aim was to improve the detectability of AF episodes.

METHODS:

Inter-beat intervals from 25 ECG recordings available in the MIT-BIH atrial fibrillation database were analysed. Four types of annotated rhythms (atrial fibrillation, atrial flutter, AV junctional rhythm, and other rhythms) were available. A wavelet leader based multifractal analysis was applied to 5 min non-overlapping windows of each recording to estimate the multifractal spectrum in each window. The width of the multifractal spectrum was analysed for its discrimination power between rhythm episodes.

RESULTS:

In 10 of 25 recordings, the width of multifractal spectrum was significantly lower in episodes of AF than in other rhythms indicating increased regularity during AF. High classification accuracy (95%) of AF episodes was achieved using a combination of features derived from the multifractal analysis and statistical central moment features.

CONCLUSIONS:

An increase in the regularity of inter-beat intervals was observed during AF episodes by means of multifractal analysis. Multifractal features may be used to improve AF detection accuracy.

KEYWORDS:

Atrial fibrillation; Inter-beat (RR) intervals; Multifractal analysis; Wavelet leaders

PMID:
30177367
PMCID:
PMC6263832
[Available on 2019-11-01]
DOI:
10.1016/j.jelectrocard.2018.08.030

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