Evidence map›Paper›PMID 36552926›Full record

ArticleDiagnostics (Basel, Switzerland)2022

Detection of Atrial Fibrillation Episodes based on 3D Algebraic Relationships between Cardiac Intervals.

Naseha Wafa Qammar, Vaiva Šiaučiūnaitė, Vytautas Zabiela, Alfonsas Vainoras, Minvydas Ragulskis

Abstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2022. 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
  2. Article
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.

Naseha Wafa QammarDepartment of Mathematical Modelling, Kaunas University of Technology, LT-51368 Kaunas, Lithuania.ORCID 0000-0001-9987-6325
Vaiva ŠiaučiūnaitėDepartment of Mathematical Modelling, Kaunas University of Technology, LT-51368 Kaunas, Lithuania.ORCID 0000-0003-2753-6035
Vytautas ZabielaCardiology Institute, The Lithuanian University of Health Sciences, Mickeviciaus g.9, LT-44307 Kaunas, Lithuania.
Alfonsas VainorasCardiology Institute, The Lithuanian University of Health Sciences, Mickeviciaus g.9, LT-44307 Kaunas, Lithuania.
Minvydas RagulskisDepartment of Mathematical Modelling, Kaunas University of Technology, LT-51368 Kaunas, Lithuania.ORCID 0000-0002-3348-9717

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, the notion of perfect matrices of Lagrange differences is employed to detect atrial fibrillation episodes based on three ECG parameters (JT interval, QRS interval, RR interval). The case study comprised 8 healthy individuals and 7 unhealthy individuals, and the mean and standard deviation of age was 65.84 ± 1.4 years, height was 1.75 ± 0.12 m, and weight was 79.4 ± 0.9 kg. Initially, it was demonstrated that the sensitivity of algebraic relationships between cardiac intervals increases when the dimension of the perfect matrices of Lagrange differences is extended from two to three. The baseline dataset was established using statistical algorithms for classification by means of the developed decision support system. The classification helps to determine whether the new incoming candidate has indications of atrial fibrillation or not. The application of probability distribution graphs and semi-gauge indicator techniques aided in visualizing the categorization of the new candidates. Though the study's data are limited, this work provides a strong foundation for (1) validating the sensitivity of the perfect matrices of Lagrange differences, (2) establishing a robust baseline dataset for supervised classification, and (3) classifying new incoming candidates within the classification framework. From a clinical standpoint, the developed approach assists in the early detection of atrial fibrillation in an individual.

Indexed as

atrial fibrillationdecision support systemperfect matrix of Lagrange differencesstatistical indicator

Identifiers

PMID36552926
PMCPMC9776502

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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.