Deep learning for suppressing EMG and motion artifacts in armband ECG R-peak detection.

Jaechan Lim, Shirin Hajeb, Youngsun Kong, Ki H Chon

Abstract read

In one paragraph

Article in Biomedical engineering letters, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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.

Research progress in exercise-induced fatigue monitoring and injury early warning based on flexible sensing textiles and deep learning.Frontiers in bioengineering and biotechnology · 2026
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

4 authors.

Jaechan LimDepartment of Biomedical Engineering, University of Connecticut, 352 Mansfield Rd, Storrs, CT 06269 USA.ORCID 0000-0003-1626-9218

Shirin HajebDepartment of Biomedical Engineering, University of Connecticut, 352 Mansfield Rd, Storrs, CT 06269 USA.

Youngsun KongDepartment of Biomedical Engineering, University of Connecticut, 352 Mansfield Rd, Storrs, CT 06269 USA.

Ki H ChonDepartment of Biomedical Engineering, University of Connecticut, 352 Mansfield Rd, Storrs, CT 06269 USA.ORCID 0000-0002-4422-4837

Funding

Waterproof Leadless Armband For Continuous Wireless Monitoring For Atrial FibrillationR43HL135961 · NHLBI · MOBILE SENSE TECHNOLOGIES, INC. · PI CHO, CHAE HO · 2017 to 2017

$225k

NHLBI NIH HHS R43 HL135961

6 · The paper itself

Abstract

Continuous cardiac monitoring through wearable electrocardiogram (ECG) devices faces significant challenges from electromyogram (EMG) contamination and motion artifacts during daily activities. This paper introduces a novel deep learning framework employing a convolutional neural network accentuating encoder-decoder (CNNAED) architecture for robust R-peak identification in artifact-corrupted armband ECG signals. The study enrolled 10 healthy participants who underwent simultaneous 24 h monitoring using both experimental armband systems and clinical-grade Holter monitors. Time-frequency spectrograms derived from 10 s ECG segments provided input features for the CNNAED architecture. The network training objective focused on selective R-peak enhancement through amplitude accentuation while suppressing interfering signal components. Model validation employed subject-independent testing protocols emphasizing daytime recordings with increased artifact prevalence. Statistical analysis adopted paired

Indexed as

Artifact suppressionCardiac monitoringDeep learningEncoder–decoder networksSignal processingWearable technology

Identifiers

PMID41584807

DOI10.1007/s13534-025-00507-2

PMCPMC12824059

What OpenQuestion holds

Textmetadata

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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.