Effect of obesity on preterm delivery prediction by transabdominal recording of uterine electromyography

Lučovnik, Miha; Chambliss, Linda R.; Blumrick, Richard; Balducci, James; Geršak, Ksenija; Garfield, Robert E.

doi:10.1016/j.tjog.2015.05.005

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…The inadequacy of methods used to accurately diagnose preterm birth remains one of the biggest unresolved obstetric problems (Lucovnik at al., 2016). Although the estimation of preterm birth is a very difficult task, it is important to detect it as early as possible (Yochum at al., 2016).…”

Section: Introductionmentioning

confidence: 99%

Premature Birth Detection from EHG signals

Sağlam¹,

Şentürk²,

Yücedağ³

2021

European Journal of Science and Technology

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Section: Introductionmentioning

confidence: 99%

Premature Birth Detection from EHG signals

Sağlam¹,

Şentürk²,

Yücedağ³

2021

European Journal of Science and Technology

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“…Besides, it is easily obtained and can be used as early as the 20th week of gestation. Although the BMI has been reported as having an imperceptible impact on the EHG, one of its main advantages [10,11], another study [12] reports a fat-induced low-pass filtering effect. The EHG is a signal with increased complexity that often requires the application of advanced signal processing analysis techniques.…”

Section: Introductionmentioning

confidence: 99%

Automatic Contraction Detection Using Uterine Electromyography

et al. 2020

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Electrohysterography (EHG) is a promising technique for pregnancy monitoring and preterm risk evaluation. It allows for uterine contraction monitoring as early as the 20th gestational week, and it is a non-invasive technique based on recording the electric signal of the uterine muscle activity from electrodes located in the abdominal surface. In this work, EHG-based contraction detection methodologies are applied using signal envelope features. Automatic contraction detection is an important step for the development of unsupervised pregnancy monitoring systems based on EHG. The exploratory methodologies include wavelet energy, Teager energy, root mean square (RMS), squared RMS, and Hilbert envelope. In this work, two main features were evaluated: contraction detection and its related delineation accuracy. The squared RMS produced the best contraction (97.15 ± 4.66%) and delineation (89.43 ± 8.10%) accuracy and the lowest false positive rate (0.63%). Despite the wavelet energy method having a contraction accuracy (92.28%) below the first-rated method, its standard deviation was the second best (6.66%). The average false positive rate ranged between 0.63% and 4.74%—a remarkably low value.

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