Evidence map›Paper›PMID 36781924›Full record

ArticleScientific reports2023

Left ventricular hypertrophy detection using electrocardiographic signal.

Cheng-Wei Liu, Fu-Hsing Wu, Yu-Lun Hu, Ren-Hao Pan, Chuen-Horng Lin, Yung-Fu Chen, Guo-Shiang Tseng, Yung-Kuan Chan, Ching-Lin Wang

Abstract read
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In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
–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

21 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  4. Review
  5. Article
  6. Deep learning to predict left ventricular hypertrophy from the electrocardiogram.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2026
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  16. An ECG-Based Model for Left Ventricular Hypertrophy Detection: A Machine Learning Approach.IEEE open journal of engineering in medicine and biology · 2025
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  17. Review
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  20. What Else Can AI See in a Digital ECG?Journal of personalized medicine · 2023
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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

9 authors.

Cheng-Wei Liu *Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital Songshan Branch, National Defense Medical Center, Taipei, Taiwan.
Fu-Hsing Wu *Bachelor Degree Program of Artificial Intelligence, National Taichung University of Science and Technology, Taichung, Taiwan.
Yu-Lun HuDepartment of Management Information Systems, National Chung-Hsing University, Taichung, Taiwan.
Ren-Hao PanLa Vida Tec. Co. Ltd., Taichung, Taiwan.
Chuen-Horng LinDepartment of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung, Taiwan.
Yung-Fu ChenDepartment of Dental Technology and Materials Science, Central Taiwan University of Science and Technology, Taichung, Taiwan.
Guo-Shiang TsengDivision of Cardiology, Department of Internal Medicine, Taoyuan Armed Force General Hospital Hsinchu Branch, Hsinchu, Taiwan.
Yung-Kuan ChanDepartment of Management Information Systems, National Chung-Hsing University, Taichung, Taiwan. ykchan@nchu.edu.tw.
Ching-Lin WangDepartment of Information Management, National Chin-Yi University of Technology, Taichung, Taiwan. clwang@ncut.edu.tw.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Left ventricular hypertrophy (LVH) indicates subclinical organ damage, associating with the incidence of cardiovascular diseases. From the medical perspective, electrocardiogram (ECG) is a low-cost, non-invasive, and easily reproducible tool that is often used as a preliminary diagnosis for the detection of heart disease. Nowadays, there are many criteria for assessing LVH by ECG. These criteria usually include that voltage combination of RS peaks in multi-lead ECG must be greater than one or more thresholds for diagnosis. We developed a system for detecting LVH using ECG signals by two steps: firstly, the R-peak and S-valley amplitudes of the 12-lead ECG were extracted to automatically obtain a total of 24 features and ECG beats of each case (LVH or non-LVH) were segmented; secondly, a back propagation neural network (BPN) was trained using a dataset with these features. Echocardiography (ECHO) was used as the gold standard for diagnosing LVH. The number of LVH cases (of a Taiwanese population) identified was 173. As each ECG sequence generally included 8 to 13 cycles (heartbeats) due to differences in heart rate, etc., we identified 1466 ECG cycles of LVH patients after beat segmentation. Results showed that our BPN model for detecting LVH reached the testing accuracy, precision, sensitivity, and specificity of 0.961, 0.958, 0.966 and 0.956, respectively. Detection performances of our BPN model, on the whole, outperform 7 methods using ECG criteria and many ECG-based artificial intelligence (AI) models reported previously for detecting LVH.

Indexed as

Cardiovascular DiseasesHypertrophy, Left VentricularArtificial IntelligenceEchocardiographyElectrocardiographyHumans

Identifiers

PMID36781924
PMCPMC9924839

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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.