Evidence map›Paper›PMID 30177367›Full record

ArticleJournal of electrocardiology

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

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of electrocardiology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Kais GadhoumiDepartment of Physiological Nursing, University of California, San Francisco, CA, USA. Electronic address: kais.gadhoumi@ucsf.edu.
Duc DoDavid Geffen School of Medicine, University of California, Los Angeles, CA, USA.
Fabio BadiliniCenter for Physiologic Research, University of California, San Francisco, CA, USA.
Michele M PelterDepartment of Physiological Nursing, University of California, San Francisco, CA, USA.
Xiao HuDepartment 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.

Funding

Develop&validate SuperAlarm to detect patient deterioration with few false alarmsR01HL128679 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HU, XIAO · 2015 to 2018
$2.4M
A unified longitudinal functional data framework for the analysis of complex biomedical dataR01GM111378 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SENTURK, DAMLA, SUGAR, CATHERINE ANN · 2015 to 2018
$1.4M
Targeted Delivery of Multi-Parametic Telemetry Clinical Messages to Mitigate AlarR18HS022860 · AHRQ · RHODE ISLAND HOSPITAL · PI KOBAYASHI, LEO · 2014 to 2016
$746k
AHRQ HHS R18 HS022860NHLBI NIH HHS R01 HL128679NIGMS NIH HHS R01 GM111378
6 · The paper itself

Abstract

backgroundAccurate 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.

methodsInter-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.

resultsIn 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.

conclusionsAn 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.

Indexed as

FractalsAtrial FibrillationDatabases, FactualDiagnosis, Computer-AssistedElectrocardiographyHeart RateHumansAtrial fibrillationInter-beat (RR) intervalsMultifractal analysisWavelet leaders

Identifiers

PMID30177367
PMCPMC6263832

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.