Evidence map›Paper›PMID 36660441›Full record

ArticleData in brief2023

Extraction of notable points from ECG data: A description of a dataset related to 30-s seated and 30-s stand up.

Rui Pedro Duarte, Francisco Alexandre Marinho, Eduarda Sofia Bastos, Rui João Pinto, Pedro Miguel Silva, Alice Fermino, Hanna Vitalyvna Denysyuk, António Jorge Gouveia, Norberto Jorge Gonçalves, Paulo Jorge Coelho and 5 more

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Data in brief, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Rui Pedro DuarteEscola de Ciências e Tecnologia, Universidade de Trás-Os-Montes e Alto Douro, Quinta de Prados, Vila Real 5001-801, Portugal.
Francisco Alexandre MarinhoEscola de Ciências e Tecnologia, Universidade de Trás-Os-Montes e Alto Douro, Quinta de Prados, Vila Real 5001-801, Portugal.
Eduarda Sofia BastosEscola de Ciências e Tecnologia, Universidade de Trás-Os-Montes e Alto Douro, Quinta de Prados, Vila Real 5001-801, Portugal.
Rui João PintoEscola de Ciências e Tecnologia, Universidade de Trás-Os-Montes e Alto Douro, Quinta de Prados, Vila Real 5001-801, Portugal.
Pedro Miguel SilvaEscola de Ciências e Tecnologia, Universidade de Trás-Os-Montes e Alto Douro, Quinta de Prados, Vila Real 5001-801, Portugal.
Alice FerminoComputer Science Department, Universidade da Beira Interior, Covilhã 6200-001, Portugal.
Hanna Vitalyvna DenysyukInstituto de Telecomunicações, Universidade da Beira Interior, Covilhã 6200-001, Portugal.
António Jorge GouveiaEscola de Ciências e Tecnologia, Universidade de Trás-Os-Montes e Alto Douro, Quinta de Prados, Vila Real 5001-801, Portugal.
Norberto Jorge GonçalvesEscola de Ciências e Tecnologia, Universidade de Trás-Os-Montes e Alto Douro, Quinta de Prados, Vila Real 5001-801, Portugal.
Paulo Jorge CoelhoSchool of Technology and Management, Polytechnic of Leiria, Leiria 2411-901, Portugal.
Eftim ZdravevskiFaculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia.
Petre LameskiFaculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia.
Toni TripunovskiInstitute of Pathophysiology and Nuclear Medicine, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia.
Nuno M GarciaInstituto de Telecomunicações, Universidade da Beira Interior, Covilhã 6200-001, Portugal.
Ivan Miguel PiresInstituto de Telecomunicações, Universidade da Beira Interior, Covilhã 6200-001, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is increasingly possible to acquire Electrocardiographic data with featured low-cost devices. The proposed dataset will help map different signals for various diseases related to Electrocardiography data. The dataset presented in this paper is related to the acquisition of electrocardiography data during the standing up and seated positions. The data was collected from 219 individuals (112 men, 106 women, and one other) in different environments, but they are in the Covilhã municipality. The dataset includes the 219 recordings and corresponds to the sensors' recordings of a 30 s sitting and a 30 s standing test, which checks to approximately 1 min for each one. This dataset includes 3.7 h (approximately) of recordings for further analysis with data processing techniques and machine learning methods. It will be helpful for the complementary creation of a robust method for identifying the characteristics of individuals related to Electrocardiography signals.

Indexed as

Daily activitiesDiseasesElectrocardiography signalsSeatedStand up

Identifiers

PMID36660441
PMCPMC9843242

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.