ArticlePloS one2020
Model based estimation of QT intervals in non-invasive fetal ECG signals.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Deep learning-based approach for accurate detection of fetal QRS complexes in abdominal ECG signals.Scientific reports · 2025Article
- Fetal ECG-based analysis reveals the impact of fetal movements and maternal respiration on maternal-fetal heart rate synchronization.PloS one · 2024Article
- Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches.Biomimetics (Basel, Switzerland) · 2023Review
- Independent component analysis algorithms for non-invasive fetal electrocardiography.PloS one · 2023Article
- Model-based estimation of QT intervals of mouse fetal electrocardiogram.Biomedical engineering online · 2022Article
- A novel algorithm based on ensemble empirical mode decomposition for non-invasive fetal ECG extraction.PloS one · 2021Article
- Measurement of the cardiac time intervals of the fetal ECG utilising a computerised algorithm: A retrospective observational study.JRSM cardiovascular diseaseArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The end timing of T waves in fetal electrocardiogram (fECG) is important for the evaluation of ST and QT intervals which are vital markers to assess cardiac repolarization patterns. Monitoring malignant fetal arrhythmias in utero is fundamental to care in congenital heart anomalies preventing perinatal death. Currently, reliable detection of end of T waves is possible only by using fetal scalp ECG (fsECG) and fetal magnetocardiography (fMCG). fMCG is expensive and less accessible and fsECG is an invasive technique available only during intrapartum period. Another safer and affordable alternative is the non-invasive fECG (nfECG) which can provide similar assessment provided by fsECG and fMECG but with less accuracy (not beat by beat). Detection of T waves using nfECG is challenging because of their low amplitudes and high noise. In this study, a novel model-based method that estimates the end of T waves in nfECG signals is proposed. The repolarization phase has been modeled as the discharging phase of a capacitor. To test the model, fECG signals were collected from 58 pregnant women (age: (34 ± 6) years old) bearing normal and abnormal fetuses with gestational age (GA) 20-41 weeks. QT and QTc intervals have been calculated to test the level of agreement between the model-based and reference values (fsECG and Doppler Ultrasound (DUS) signals) in normal subjects. The results of the test showed high agreement between model-based and reference values (difference < 5%), which implies that the proposed model could be an alternative method to detect the end of T waves in nfECG signals.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.