Evidence map›Paper›PMID 37879933›Full record

ReviewSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2023

[A review on intelligent auxiliary diagnosis methods based on electrocardiograms for myocardial infarction].

Chuang Han, Wenge Que, Zhizhong Wang, Songwei Wang, Yanting Li, Li Shi

Abstract readReviewEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Review in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2023. 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.

  1. [Research on arrhythmia classification algorithm based on adaptive multi-feature fusion network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025
    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

6 authors.

Chuang HanSchool of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, P. R. China.
Wenge QueDepartment of Automation, Tsinghua university, Beijing 100000, P. R. China.
Zhizhong WangSchool of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450000, P. R. China.
Songwei WangSchool of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450000, P. R. China.
Yanting LiSchool of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, P. R. China.
Li ShiDepartment of Automation, Tsinghua university, Beijing 100000, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Myocardial infarction (MI) has the characteristics of high mortality rate, strong suddenness and invisibility. There are problems such as the delayed diagnosis, misdiagnosis and missed diagnosis in clinical practice. Electrocardiogram (ECG) examination is the simplest and fastest way to diagnose MI. The research on MI intelligent auxiliary diagnosis based on ECG is of great significance. On the basis of the pathophysiological mechanism of MI and characteristic changes in ECG, feature point extraction and morphology recognition of ECG, along with intelligent auxiliary diagnosis method of MI based on machine learning and deep learning are all summarized. The models, datasets, the number of ECG, the number of leads, input modes, evaluation methods and effects of different methods are compared. Finally, future research directions and development trends are pointed out, including data enhancement of MI, feature points and dynamic features extraction of ECG, the generalization and clinical interpretability of models, which are expected to provide references for researchers in related fields of MI intelligent auxiliary diagnosis.

Indexed as

ElectrocardiographyMyocardial InfarctionHumansRecognition, PsychologyElectrocardiogramFeature extractionIntelligent auxiliary diagnosisMachine learningMyocardial infarction

Identifiers

PMID37879933
PMCPMC10600411

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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.