Evidence map›Paper›PMID 42136822›Full record

ArticleBiomedical signal processing and control2025

Reconstructing 12-lead ECG from reduced lead sets using an encoder-decoder convolutional neural network.

Dorsa EPMoghaddam, Anton Banta, Allison Post, Mehdi Razavi, Behnaam Aazhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical signal processing and control, 2025. 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. A novel method for 12-lead ECG reconstruction.Conference record. Asilomar Conference on Signals, Systems & Computers
    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.

Dorsa EPMoghaddamDepartment of Electrical and Computer Engineering, Rice University, United States of America.ORCID 0000-0001-5183-7204
Anton BantaDepartment of Electrical and Computer Engineering, Rice University, United States of America.
Allison PostElectrophysiology Clinical Research and Innovations, Texas Heart Institute, United States of America.
Mehdi RazaviDepartment of Cardiology, Texas Heart Institute, United States of America.
Behnaam AazhangDepartment of Electrical and Computer Engineering, Rice University, United States of America.

Funding

Leadless wirelessly powered pacemaker for multi chamber pacing using miniaturized pacing and sensing nodeR01HL144683 · NHLBI · TEXAS HEART INSTITUTE · PI BABAKHANI, AYDIN, RAZAVI, MEHDI · 2019 to 2022
$2.4M
NHLBI NIH HHS R01 HL144683
6 · The paper itself

Abstract

The standard 12-lead electrocardiogram (ECG) is the gold-standard clinical tool for assessing the heart's electrical activity. The primary goal of this study is to reduce the number of recording sites needed to capture the same amount of information as a 12-lead ECG. This approach will simplify monitoring procedures, improve patient comfort, and broaden ECG accessibility across various healthcare settings. In this work, a patient-specific framework is investigated to map a multivariate input to a multivariate output, namely reconstructing the standard 12-lead ECG from any subset of three independent standard ECG leads (i.e.

Indexed as

Cardiovascular diseasesConvolutional neural network (CNN)ECG reconstructionElectrocardiogram (ECG)Encoder–decoderMutual informationStandard 12-lead system

Identifiers

PMID42136822
PMCPMC13171163

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.