Use of uterine electromyography to diagnose term and preterm labor

Lučovnik, Miha; Kuon, Ruben; Chambliss, Linda R.; Maner, William L.; Shi, Suqing; Shi, Leili; Balducci, James; Garfield, Robert E.

doi:10.1111/j.1600-0412.2010.01031.x

Cited by 94 publications

(74 citation statements)

References 51 publications

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“…However, a major limitation is that the differences in patients can easily affect these parameters. Patients may differ in the amount of fatty tissue they have, and the conductivity of the skin-electrode interface, which leads to differences in the attenuation of uterine signals [27], [6], [35]. Examples of amplitude-related parameters include root mean square, peak amplitude and median amplitude.…”

Section: Pretermmentioning

confidence: 99%

Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women

et al. 2015

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There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. There is a strong body of evidence emerging that suggests the analysis of uterine electrical signals, from the abdominal surface (Electrohysterography -EHG), could provide a viable way of diagnosing true labour and even predict preterm deliveries. This paper explores this idea further and presents a new dynamic self-organised network immune algorithm that classifies term and preterm records, using an open dataset containing 300 records (38 preterm and 262 term). Using the dataset, oversampling and cross validation techniques are evaluated against other similar studies. the proposed approach shows an improvement on existing studies with 89% sensitivity, 91% specificity, 90% positive predicted value, 90% negative predicted value, and an overall accuracy of 90%.

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Section: Pretermmentioning

confidence: 99%

Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women

et al. 2015

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“…Nevertheless, none of the currently available clinical methods can distinguish reliably between true and false term and preterm labor. On the other hand, evidence from many studies suggest that UEMG recordings can diagnose true labor more accurately than any other method used today [30]. As a result, UEMG provides a strong basis for objective prediction and diagnosis of preterm birth [36].…”

Section: Applications To Uterine Emg Signal Chracterizationmentioning

confidence: 99%

“…Eventually, the preparatory process becomes irreversible and leads to active labor. In labor, this bioelectrical activity becomes more synchronous resulting towards the complete dilation of the cervix and expulsion (delivery) of the fetus [28,30].…”

Section: Applications To Uterine Emg Signal Chracterizationmentioning

confidence: 99%

A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis

Ahmed

Chanwimalueang

Thayyil

et al. 2016

Entropy

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Abstract:The recently introduced multivariate multiscale entropy (MMSE) has been successfully used to quantify structural complexity in terms of nonlinear within-and cross-channel correlations as well as to reveal complex dynamical couplings and various degrees of synchronization over multiple scales in real-world multichannel data. However, the applicability of MMSE is limited by the coarse-graining process which defines scales, as it successively reduces the data length for each scale and thus yields inaccurate and undefined entropy estimates at higher scales and for short length data. To that cause, we propose the multivariate multiscale fuzzy entropy (MMFE) algorithm and demonstrate its superiority over the MMSE on both synthetic as well as real-world uterine electromyography (EMG) short duration signals. Based on MMFE features, an improvement in the classification accuracy of term-preterm deliveries was achieved, with a maximum area under the curve (AUC) value of 0.99.

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“…24-29 Abdominal EHG, while very promising, has yet to provide a reliable method of labor detection. 28,30,31 This study was undertaken to determine if EHG, as recorded from the cervical and vaginal surfaces offers a feasible means of detecting and recording uterine activity.…”

Section: Discussionmentioning

confidence: 99%

Vaginal electrohysterography: the design and preliminary evaluation of a novel device for uterine contraction monitoring in an ovine model*

Sunwoo

Hwang

Aina-Mumuney

2015

The Journal of Maternal-Fetal & Neonatal Medicine

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Objective Tocodynamometry is the most common method of labor evaluation but most clinicians would agree it has limited utility before 26 weeks gestation. The obesity epidemic has further reduced our ability to accurately detect uterine contractions using the tocodynamometer at any gestational age. We sought to design and test a novel contraction monitor that bypasses the maternal abdomen. Methods An optimized version of an intravaginal electrohysterographic ring device was tested in an ovine model. The device and its methodology as well as the tocodynamometer were validated against the current gold standard uterine activity monitor, the intrauterine pressure catheter in 6 sheep at varying gestational ages. Results Both the intravaginal ring device and the tocodynamometer correlated well with IUPC, r = 0.69 and 0.73 respectively (p<0.001). The number of contractions detected by each monitor remained similar even after accounting for confounders. Conclusions These results suggest that uterine activity can be monitored from the vaginal interface in an ovine model and offers an alternative clinical tool for the detection of contractions in situations in which tocodynamometry would be ineffective or intrauterine monitoring inappropriate.

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Use of uterine electromyography to diagnose term and preterm labor

Cited by 94 publications

References 51 publications

Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women

Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women

A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis

Vaginal electrohysterography: the design and preliminary evaluation of a novel device for uterine contraction monitoring in an ovine model*

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