Evidence map›Paper›PMID 35923089›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2022

Toward ECG-based analysis of hypertrophic cardiomyopathy: a novel ECG segmentation method for handling abnormalities.

Kasra Nezamabadi, Jacob Mayfield, Pengyuan Li, Gabriela V Greenland, Sebastian Rodriguez, Bahadir Simsek, Parvin Mousavi, Hagit Shatkay, M Roselle Abraham

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2022. 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
0.3field-weighted citation impact, top 40% of its field
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, 2 citations in OpenAlex.

  1. 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

9 authors at 3 institutions in 2 countries.

Kasra NezamabadiComputational Biomedicine Lab, Computer and Information Sciences, University of Delaware, Newark, Delaware, USA.ORCID 0000-0002-9844-7729
Jacob MayfieldDivision of Cardiology, Hypertrophic Cardiomyopathy Center of Excellence, University of California San Francisco, San Francisco, California, USA.
Pengyuan LiComputational Biomedicine Lab, Computer and Information Sciences, University of Delaware, Newark, Delaware, USA.
Gabriela V GreenlandDivision of Cardiology, Hypertrophic Cardiomyopathy Center of Excellence, University of California San Francisco, San Francisco, California, USA.
Sebastian RodriguezDivision of Cardiology, Hypertrophic Cardiomyopathy Center of Excellence, University of California San Francisco, San Francisco, California, USA.
Bahadir SimsekDivision of Cardiology, Hypertrophic Cardiomyopathy Center of Excellence, University of California San Francisco, San Francisco, California, USA.
Parvin MousaviSchool of Computing, Queen's University, Kingston, Ontario, Canada.
Hagit ShatkayComputational Biomedicine Lab, Computer and Information Sciences, University of Delaware, Newark, Delaware, USA.
M Roselle AbrahamHypertrophic Cardiomyopathy Center of Excellence, Division of Cardiology, University of California San Francisco, San Francisco, USA.
University of California, San Francisco · USUniversity of Delaware · USQueen's University · CA

Funding

John Taylor BabbittJohn Taylor Babbitt (JTB) foundationNSFNSF IIS EAGER #1650851UCSF Division of Cardiology
6 · The paper itself

Abstract

objectiveAbnormalities in impulse propagation and cardiac repolarization are frequent in hypertrophic cardiomyopathy (HCM), leading to abnormalities in 12-lead electrocardiograms (ECGs). Computational ECG analysis can identify electrophysiological and structural remodeling and predict arrhythmias. This requires accurate ECG segmentation. It is unknown whether current segmentation methods developed using datasets containing annotations for mostly normal heartbeats perform well in HCM. Here, we present a segmentation method to effectively identify ECG waves across 12-lead HCM ECGs.

methodsWe develop (1) a web-based tool that permits manual annotations of P, P', QRS, R', S', T, T', U, J, epsilon waves, QRS complex slurring, and atrial fibrillation by 3 experts and (2) an easy-to-implement segmentation method that effectively identifies ECG waves in normal and abnormal heartbeats. Our method was tested on 131 12-lead HCM ECGs and 2 public ECG sets to evaluate its performance in non-HCM ECGs.

resultsOver the HCM dataset, our method obtained a sensitivity of 99.2% and 98.1% and a positive predictive value of 92% and 95.3% when detecting QRS complex and T-offset, respectively, significantly outperforming a state-of-the-art segmentation method previously employed for HCM analysis. Over public ECG sets, it significantly outperformed 3 state-of-the-art methods when detecting P-onset and peak, T-offset, and QRS-onset and peak regarding the positive predictive value and segmentation error. It performed at a level similar to other methods in other tasks.

conclusionOur method accurately identified ECG waves in the HCM dataset, outperforming a state-of-the-art method, and demonstrated similar good performance as other methods in normal/non-HCM ECG sets.

Indexed as

Cardiomyopathy, HypertrophicElectrocardiographyHumansPredictive Value of Testsabnormalitiesdelineationelectrocardiogram (ECG)hypertrophic cardiomyopathysegmentation

Identifiers

PMID35923089
PMCPMC9552290
OpenAlexW4289731377

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.