Physiology and electrical activity of uterine contractions

Garfield, Robert E.; Maner, William L.

doi:10.1016/j.semcdb.2007.05.004

Cited by 189 publications

(159 citation statements)

References 44 publications

Supporting

Mentioning

147

Contrasting

Unclassified

Order By: Relevance

“…The process of parturition involves a relatively long conditioning (preparatory) phase, followed by a short active labor [29]. During the preparatory phase, excitability of the myometrial cells increases due to the changes in transduction mechanisms and synthesis of several new proteins, including ion channels and receptors for uterotonins.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…In most pregnancies, the connections between gap junctions are sparse, although gradually increasing, until the last few days before labour. A specific pacemaker site has not been conclusively identified, although, due to obvious physiological reasons, there may be a generalised propagation direction, from the top to the bottom of the uterus [22].…”

Section: Electrohysterographymentioning

confidence: 99%

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

et al. 2015

View full text Add to dashboard Cite

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%.

show abstract

“…Let a fasciculus be embedded in the extracellular matrix of connective tissue. Our developments of a biomechanical model will be based on the following assumptions which are consistent with several lines of experimental evidence [3,4]. i) Smooth muscle cells in the fasciculus are connected by tight junctions to form a homogenous electromechanical continuum.…”

Section: Model Formulationmentioning

confidence: 88%