Evidence map›Paper›PMID 30503301›Full record

Trial reportComputers in biology and medicine2019

Systematic differences of non-invasive dominant frequency estimation compared to invasive dominant frequency estimation in atrial fibrillation.

Frederique J Vanheusden, Gavin S Chu, Xin Li, João Salinet, Tiago P Almeida, Nawshin Dastagir, Peter J Stafford, G André Ng, Fernando S Schlindwein

Abstract readClinical TrialComparative Study
In one paragraph

Trial report in Computers in biology and medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

9 authors.

Frederique J VanheusdenDepartment of Engineering, School of Science & Technology, Clifton Campus, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, United Kingdom; Department of Engineering, University of Leicester, University Road, Leicester, LE1 7RH, United Kingdom. Electronic address: frederique.vanheusden@ntu.ac.uk.
Gavin S ChuDepartment of Cardiovascular Sciences, University of Leicester, Clinical Sciences Wing, Glenfield General Hospital, Groby Road, Leicester, LE3 9QP, United Kingdom; University Hospitals of Leicester NHS Trust, Glenfield General Hospital, Groby Road, Leicester, LE3 9QP, United Kingdom. Electronic address: gc171@leicester.ac.uk.
Xin LiDepartment of Engineering, University of Leicester, University Road, Leicester, LE1 7RH, United Kingdom; Department of Cardiovascular Sciences, University of Leicester, Clinical Sciences Wing, Glenfield General Hospital, Groby Road, Leicester, LE3 9QP, United Kingdom. Electronic address: xl251@leicester.ac.uk.
João SalinetBiomedical Engineering, Centre for Engineering, Modelling and Applied Social Sciences, Federal University of ABC, Alameda da Universidade, Anchieta, São Bernardo do Campo, SP, 09606-045, Brazil. Electronic address: joao.salinet@ufabc.edu.br.
Tiago P AlmeidaInstituto Tecnológico da Aeronáutica (ITA), Praça Marechal Eduardo Gomes 50, Vila das Acacias, São José dos Campos, SP, 12228-900, Brazil. Electronic address: tiagopaggi@gmail.com.
Nawshin DastagirDepartment of Engineering, University of Leicester, University Road, Leicester, LE1 7RH, United Kingdom. Electronic address: nawshinzerin@hotmail.com.
Peter J StaffordDepartment of Cardiovascular Sciences, University of Leicester, Clinical Sciences Wing, Glenfield General Hospital, Groby Road, Leicester, LE3 9QP, United Kingdom; University Hospitals of Leicester NHS Trust, Glenfield General Hospital, Groby Road, Leicester, LE3 9QP, United Kingdom. Electronic address: peter.stafford@uhl-tr.nhs.uk.
G André NgDepartment of Cardiovascular Sciences, University of Leicester, Clinical Sciences Wing, Glenfield General Hospital, Groby Road, Leicester, LE3 9QP, United Kingdom; University Hospitals of Leicester NHS Trust, Glenfield General Hospital, Groby Road, Leicester, LE3 9QP, United Kingdom; National Institute for Health Research Leicester Cardiovascular Biomedical Research Centre, Leicester, United Kingdom. Electronic address: gan1@leicester.ac.uk.
Fernando S SchlindweinDepartment of Engineering, University of Leicester, University Road, Leicester, LE1 7RH, United Kingdom; Department of Cardiovascular Sciences, University of Leicester, Clinical Sciences Wing, Glenfield General Hospital, Groby Road, Leicester, LE3 9QP, United Kingdom; National Institute for Health Research Leicester Cardiovascular Biomedical Research Centre, Leicester, United Kingdom. Electronic address: fss1@leicester.ac.uk.

Funding

British Heart Foundation PG/18/33/33780British Heart Foundation RG/17/3/32774Department of HealthMedical Research Council MC_PC_15056
6 · The paper itself

Abstract

Non-invasive analysis of atrial fibrillation (AF) using body surface mapping (BSM) has gained significant interest, with attempts at interpreting atrial spectro-temporal parameters from body surface signals. As these body surface signals could be affected by properties of the torso volume conductor, this interpretation is not always straightforward. This paper highlights the volume conductor effects and influences of the algorithm parameters for identifying the dominant frequency (DF) from cardiac signals collected simultaneously on the torso and atrial surface. Bi-atrial virtual electrograms (VEGMs) and BSMs were recorded simultaneously for 5 min from 10 patients undergoing ablation for persistent AF. Frequency analysis was performed on 4 s segments. DF was defined as the frequency with highest power between 4 and 10 Hz with and without applying organization index (OI) thresholds. The volume conductor effect was assessed by analyzing the highest DF (HDF) difference of each VEGM HDF against its BSM counterpart. Significant differences in HDF values between intra-cardiac and torso signals could be observed, independent of OI threshold. This difference increases with increasing endocardial HDF (BSM-VEGM median difference from -0.13 Hz for VEGM HDF at 6.25 ± 0.25 Hz to -4.24 Hz at 9.75 ± 0.25 Hz), thereby confirming the theory of the volume conductor effect in real-life situations. Applying an OI threshold strongly affected the BSM HDF area size and location and atrial HDF area location. These results suggest that volume conductor and measurement algorithm effects must be considered for appropriate clinical interpretation.

Indexed as

AlgorithmsBody Surface Potential MappingElectrophysiologic Techniques, CardiacAdultAgedAtrial FibrillationHeart AtriaHeart Conduction SystemHumansMaleMiddle AgedAtrial fibrillationBiomedical signal processingBody surface mappingCardiologyDominant frequencyElectrocardiographyNon-contact mappingVolume conductor

Identifiers

PMID30503301
PMCPMC6334202

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Registered trials

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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.