Evidence map›Paper›PMID 36832667›Full record

ArticleEntropy (Basel, Switzerland)2023

Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy.

David Mayor, Tony Steffert, George Datseris, Andrea Firth, Deepak Panday, Harikala Kandel, Duncan Banks

Open access · goldAbstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
3.3field-weighted citation impact, top 7% of its field
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

5 citing papers in PubMed, 16 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Symmetry in Signals: A New Insight.Entropy (Basel, Switzerland) · 2024
    Article
  5. Review
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

7 authors at 4 institutions in 2 countries.

David MayorSchool of Health and Social Work, University of Hertfordshire, Hatfield AL10 9AB, UK.ORCID 0000-0002-2199-2091
Tony SteffertMindSpire, Napier House, 14-16 Mount Ephraim Rd., Tunbridge Wells TN1 1EE, UK.
George DatserisDepartment of Mathematics and Statistics, University of Exeter, North Park Road, Exeter EX4 4QF, UK.ORCID 0000-0002-6427-2385
Andrea FirthUniversity Campus Football Business, Wembley HA9 0WS, UK.ORCID 0000-0003-1206-504X
Deepak PandaySchool of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK.
Harikala KandelDepartment of Computer Science and Information Systems, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK.
Duncan BanksSchool of Life, Health and Chemical Sciences, STEM, Walton Hall, The Open University, Milton Keynes MK7 6AA, UK.ORCID 0000-0002-9448-8902
The Open University · GBUniversity of Hertfordshire · GBBirkbeck, University of London · GBUniversity of Exeter · GB

Funding

Innovate UK 63382
6 · The paper itself

Abstract

backgroundAs technology becomes more sophisticated, more accessible methods of interpretating Big Data become essential. We have continued to develop

methodsTo demonstrate the functionality of the software, data were collected from 44 healthy adults for a study investigating the effects on vagal tone of breathing paced at five different rates, as well as self-paced and un-paced. Five-minute 15-s recordings were used. Results were also compared with those from shorter segments of the data. Electrocardiogram (ECG), electrodermal activity (EDA) and Respiration (RSP) data were recorded. Particular attention was paid to COVID risk mitigation, and to parameter tuning for the CEPS measures. For comparison, data were processed using Kubios HRV, RR-APET and DynamicalSystems.jl software. We also compared findings for ECG RR interval (RRi) data resampled at 4 Hz (4R) or 10 Hz (10R), and non-resampled (noR). In total, we used around 190-220 measures from CEPS at various scales, depending on the analysis undertaken, with our investigation focused on three families of measures: 22 fractal dimension (FD) measures, 40 heart rate asymmetries or measures derived from Poincaré plots (HRA), and 8 measures based on permutation entropy (PE).

resultsFDs for the RRi data differentiated strongly between breathing rates, whether data were resampled or not, increasing between 5 and 7 breaths per minute (BrPM). Largest effect sizes for RRi (4R and noR) differentiation between breathing rates were found for the PE-based measures. Measures that both differentiated well between breathing rates

conclusionThe updated CEPS software enables visualisation and analysis of multichannel physiological data using a variety of established and recently introduced complexity entropy measures. Although equal resampling is theoretically important for FD estimation, it appears that FD measures may also be usefully applied to non-resampled data.

Indexed as

complexityfractal dimensionheart rate asymmetryheart rate variability (HRV)paced breathingparameter tuningpermutation entropyresonant breathingsoftware

Identifiers

PMID36832667
PMCPMC9955651
OpenAlexW4319315927

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.