Evidence map›Paper›PMID 36008334›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2022

[ST segment morphological classification based on support vector machine multi feature fusion].

Haiman Du, Ting Bian, Peng Xiong, Jianli Yang, Jieshuo Zhang, Xiuling Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2022. 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. [Research onSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024
    Article
  2. [A review on intelligent auxiliary diagnosis methods based on electrocardiograms for myocardial infarction].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023
    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

6 authors.

Haiman DuKey Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China.
Ting BianKey Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China.
Peng XiongKey Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China.
Jianli YangKey Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China.
Jieshuo ZhangKey Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China.
Xiuling LiuKey Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

ST segment morphology is closely related to cardiovascular disease. It is used not only for characterizing different diseases, but also for predicting the severity of the disease. However, the short duration, low energy, variable morphology and interference from various noises make ST segment morphology classification a difficult task. In this paper, we address the problems of single feature extraction and low classification accuracy of ST segment morphology classification, and use the gradient of ST surface to improve the accuracy of ST segment morphology multi-classification. In this paper, we identify five ST segment morphologies: normal, upward-sloping elevation, arch-back elevation, horizontal depression, and arch-back depression. Firstly, we select an ST segment candidate segment according to the QRS wave group location and medical statistical law. Secondly, we extract ST segment area, mean value, difference with reference baseline, slope, and mean squared error features. In addition, the ST segment is converted into a surface, the gradient features of the ST surface are extracted, and the morphological features are formed into a feature vector. Finally, the support vector machine is used to classify the ST segment, and then the ST segment morphology is multi-classified. The MIT-Beth Israel Hospital Database (MITDB) and the European ST-T database (EDB) were used as data sources to validate the algorithm in this paper, and the results showed that the algorithm in this paper achieved an average recognition rate of 97.79% and 95.60%, respectively, in the process of ST segment recognition. Based on the results of this paper, it is expected that this method can be introduced in the clinical setting in the future to provide morphological guidance for the diagnosis of cardiovascular diseases in the clinic and improve the diagnostic efficiency.

Indexed as

ElectrocardiographySupport Vector MachineAlgorithmsArrhythmias, CardiacDatabases, FactualHumansElectrocardiogram signalFeature fusionGradientST segment morphologySupport vector machine

Identifiers

PMID36008334
PMCPMC10957352

What OpenQuestion holds

Textmetadata
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.