Evidence map›Paper›PMID 38472722›Full record

ArticleJournal of cardiovascular translational research2024

Efficient Edge-AI Models for Robust ECG Abnormality Detection on Resource-Constrained Hardware.

Zhaojing Huang, Luis Fernando Herbozo Contreras, Wing Hang Leung, Leping Yu, Nhan Duy Truong, Armin Nikpour, Omid Kavehei

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cardiovascular translational research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Best Paper of the Year 2024.Journal of cardiovascular translational research · 2025
    Article
  2. Review
  3. 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

7 authors.

Zhaojing Huang *School of Biomedical Engineering, The University of Sydney, NSW 2008, Sydney, Australia. zhaojing.huang@sydney.edu.au.ORCID 0009-0004-2796-6734
Luis Fernando Herbozo Contreras *School of Biomedical Engineering, The University of Sydney, NSW 2008, Sydney, Australia.
Wing Hang LeungSchool of Biomedical Engineering, The University of Sydney, NSW 2008, Sydney, Australia.
Leping YuSchool of Biomedical Engineering, The University of Sydney, NSW 2008, Sydney, Australia.
Nhan Duy TruongSchool of Biomedical Engineering, The University of Sydney, NSW 2008, Sydney, Australia.
Armin NikpourDepartment of Neurology, Royal Prince Alfred Hospital, and Central Clinical School, The University of Sydney, NSW 2006, Sydney, Australia.
Omid KaveheiSchool of Biomedical Engineering, The University of Sydney, NSW 2008, Sydney, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study introduces two models, ConvLSTM2D-liquid time-constant network (CLTC) and ConvLSTM2D-closed-form continuous-time neural network (CCfC), designed for abnormality identification using electrocardiogram (ECG) data. Trained on the Telehealth Network of Minas Gerais (TNMG) subset dataset, both models were evaluated for their performance, generalizability capacity, and resilience. They demonstrated comparable results in terms of F1 scores and AUROC values. The CCfC model achieved slightly higher accuracy, while the CLTC model showed better handling of empty channels. Remarkably, the models were successfully deployed on a resource-constrained microcontroller, proving their suitability for edge device applications. Generalization capabilities were confirmed through the evaluation on the China Physiological Signal Challenge 2018 (CPSC) dataset. The models' efficient resource utilization, occupying 70.6% of memory and 9.4% of flash memory, makes them promising candidates for real-world healthcare applications. Overall, this research advances abnormality identification in ECG data, contributing to the progress of AI in healthcare.

Indexed as

ElectrocardiographyPredictive Value of TestsSignal Processing, Computer-AssistedAction PotentialsArrhythmias, CardiacDatabases, FactualDiagnosis, Computer-AssistedEquipment DesignHeart RateHumansNeural Networks, ComputerReproducibility of ResultsTime FactorsAbnormality identificationECG dataEdge devicesGeneralizationPerformance evaluationRobustnessSimple network

Identifiers

PMID38472722
PMCPMC11371854

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.