Uterine Contractions in Normal Labor Developed by a Positive Feed-back and Oscillation

Maeda, Kazuo

doi:10.4172/2157-7420.1000130

Cited by 9 publications

(8 citation statements)

References 8 publications

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“…Akin to these physiological systems, fetus dynamics also comprise biological oscillations e.g., labor contraction of uterus has been modeled as a 0.008 Hz oscillatory feedback system (Maeda, 2013 ). Along these lines, the present study also hypothesized that “vaginal vs. cesarean” dynamics might be differentiated by complex features of spectral oscillations of UC-FHR couplings.…”

Section: Discussionmentioning

confidence: 99%

A Strategy for Classification of “Vaginal vs. Cesarean Section” Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

et al. 2019

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We propose objective and robust measures for the purpose of classification of “vaginal vs. cesarean section” delivery by investigating temporal dynamics and complex interactions between fetal heart rate (FHR) and maternal uterine contraction (UC) recordings from cardiotocographic (CTG) traces. Multivariate extension of empirical mode decomposition (EMD) yields intrinsic scales embedded in UC-FHR recordings while also retaining inter-channel (UC-FHR) coupling at multiple scales. The mode alignment property of EMD results in the matched signal decomposition, in terms of frequency content, which paves the way for the selection of robust and objective time-frequency features for the problem at hand. Specifically, instantaneous amplitude and instantaneous frequency of multivariate intrinsic mode functions are utilized to construct a class of features which capture nonlinear and nonstationary interactions from UC-FHR recordings. The proposed features are fed to a variety of modern machine learning classifiers (decision tree, support vector machine, AdaBoost) to delineate vaginal and cesarean dynamics. We evaluate the performance of different classifiers on a real world dataset by investigating the following classifying measures: sensitivity, specificity, area under the ROC curve (AUC) and mean squared error (MSE). It is observed that under the application of all proposed 40 features AdaBoost classifier provides the best accuracy of 91.8% sensitivity, 95.5% specificity, 98% AUC, and 5% MSE. To conclude, the utilization of all proposed time-frequency features as input to machine learning classifiers can benefit clinical obstetric practitioners through a robust and automatic approach for the classification of fetus dynamics.

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Section: Discussionmentioning

confidence: 99%

A Strategy for Classification of “Vaginal vs. Cesarean Section” Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

et al. 2019

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“…In particular, as parturition approaches, the uterus becomes more excitable through an activation phase which involves molecular changes that lead to a decrease in time between subsequent contractions [8]. In humans, this interval decreases from approximately 30 min to approximately 2 min as labour progresses [5,8], whereas in rats, contractions occur at 2.3 min intervals at gestational day 19 and at 46 s intervals in active labour [6].…”

Section: Introductionmentioning

confidence: 99%

Alterations in gap junction connexin43/connexin45 ratio mediate a transition from quiescence to excitation in a mathematical model of the myometrium

Sheldon

Mashayamombe

Shi

et al. 2014

J. R. Soc. Interface.

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The smooth muscle cells of the uterus contract in unison during delivery. These cells achieve coordinated activity via electrical connections called gap junctions which consist of aggregated connexin proteins such as connexin43 and connexin45. The density of gap junctions governs the excitability of the myometrium (among other factors). An increase in gap junction density occurs immediately prior to parturition. We extend a mathematical model of the myometrium by incorporating the voltage-dependence of gap junctions that has been demonstrated in the experimental literature. Two functional subtypes exist, corresponding to systems with predominantly connexin43 and predominantly connexin45, respectively. Our simulation results indicate that the gap junction protein connexin45 acts as a negative modulator of uterine excitability, and hence, activity. A network with a higher proportion of connexin45 relative to connexin43 is unable to excite every cell. Connexin45 has much more rapid gating kinetics than connexin43 which we show limits the maximum duration of a local burst of activity. We propose that this effect regulates the degree of synchronous excitation attained during a contraction. Our results support the hypothesis that as labour approaches, connexin45 is downregulated to allow action potentials to spread more readily through the myometrium.

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“…However, the route of conduction of the information of uterine enlargement to the hypothalamus was unknown for 60 years. Recently, 4 research papers reported the presence of nerves between the uterus and brain in rats and mice [2][3][4][5], which will play very important role in the formation of labor contractions of uterus [6].…”

Section: Introductionmentioning

confidence: 99%

The Cause of Preeclampsia

Maeda¹

2014

J Health Med Informat

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Uterine Contractions in Normal Labor Developed by a Positive Feed-back and Oscillation

Cited by 9 publications

References 8 publications

A Strategy for Classification of “Vaginal vs. Cesarean Section” Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

A Strategy for Classification of “Vaginal vs. Cesarean Section” Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

Alterations in gap junction connexin43/connexin45 ratio mediate a transition from quiescence to excitation in a mathematical model of the myometrium

The Cause of Preeclampsia

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