ArticleProceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference)2020
Estimation of Consecutively Missed Samples in Fetal Heart Rate Recordings.
Article in Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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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.
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Who cites it
6 citing papers in PubMed.
- Fetal Heart Rate Preprocessing Techniques: A Scoping Review.Bioengineering (Basel, Switzerland) · 2024Article
- Multimodal Deep Learning for Predicting Adverse Birth Outcomes Based on Early Labour Data.Bioengineering (Basel, Switzerland) · 2023Article
- Cardiotocography analysis by empirical dynamic modeling and Gaussian processes.Frontiers in bioengineering and biotechnology · 2022Article
- Unsupervised Detection of Anomalies in Fetal Heart Rate Tracings using Phase Space Reconstruction.Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference) · 2021Article
- Fetal heart rate development during labour.Biomedical engineering online · 2021Article
- Estimation of Consecutively Missed Samples in Fetal Heart Rate Recordings.Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
During labor, fetal heart rate (FHR) is monitored externally using Doppler ultrasound. This is done continuously, but for various reasons (e.g., fetal or maternal movements) the system does not record any samples for varying periods of time. In many settings, it would be quite beneficial to estimate the missing samples. In this paper, we propose a (deep) Gaussian process-based approach for estimation of consecutively missing samples in FHR recordings. The method relies on similarities in the state space and on exploiting the concept of attractor manifolds. The proposed approach was tested on a short segment of real FHR recordings. The experimental results indicate that the proposed approach is able to provide more reliable results in comparison to several interpolation methods that are commonly applied for processing of FHR signals.
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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.