Wavelet leader multifractal analysis of heart rate variability in atrial fibrillation

Kais Gadhoumi; Duc Do; Fabio Badilini; Michele M Pelter; Xiao Hu

doi:10.1016/j.jelectrocard.2018.08.030

Wavelet leader multifractal analysis of heart rate variability in atrial fibrillation

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

Authors

Kais Gadhoumi¹, Duc Do², Fabio Badilini³, Michele M Pelter⁴, Xiao Hu⁵

Affiliations

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.

MeSH terms

Atrial Fibrillation / diagnosis*
Atrial Fibrillation / physiopathology*
Databases, Factual
Diagnosis, Computer-Assisted
Electrocardiography / methods*
Fractals*
Heart Rate / physiology
Humans

Abstract

MeSH terms

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