Vinken Maartje P G C, Rabotti Chiara, Mischi Massimo, Oei S Guid
Department of Obstetrics and Gynecology, Máxima Medical Centre, Veldhoven, The Netherlands.
Obstet Gynecol Surv. 2009 Aug;64(8):529-41. doi: 10.1097/OGX.0b013e3181a8c6b1.
The diagnosis of labor and effective prevention of preterm delivery are still among the most significant problems faced by obstetricians. Currently, there is no technique or method for objectively monitoring the uterus and assessing whether the organ has entered a state of increased activity that may indicate labor. Several studies have investigated a new, noninvasive technique to monitor uterine contractions: the electrohysterogram (EHG). Analysis of frequency-related parameters of the EHG may allow physiological uterine activity to be distinguished from uterine contractions that will lead to preterm delivery. However, although a variety of parameters and methodologies have been employed, they have not been objectively compared. The objective of this review, which was based on a systematic literature search using the Cochrane, PubMed, and EMBASE databases up to February 2008, was to determine whether frequency-related parameters of the EHG signal can reliably differentiate preterm contractions that will lead to preterm delivery from those that will not (in patients who will ultimately deliver at term) and to identify the most accurate parameter. Of all the different EHG parameters, both human and animal studies indicate that the power spectral density peak frequency may be the most predictive of true labor. The best parameter for predicting delivery is, therefore, related to the EHG spectral content shift, as calculated by Fourier transform, time-frequency, or Wavelet analysis. The incidence and extent to which shifts in uterine electrical spectral components occur, as the measurement-to-delivery interval decreases, imply that these changes might be used to predict preterm delivery. There is also promising data suggesting that a combination of the measured parameters, used as inputs to artificial neural network algorithms, may be more useful than individual ones for critically assessing uterine activity.
Obstetricians & Gynecologists, Family Physicians.
After completion of this article, the reader will be able to recall the physiology of uterine contractions leading to labor, summarize the limitations of tocodynamometry, and outline four different electrohysterogram parameters.
分娩的诊断以及早产的有效预防仍是产科医生面临的最重大问题。目前，尚无客观监测子宫并评估其是否已进入可能预示分娩的活动增强状态的技术或方法。多项研究已对一种监测子宫收缩的新的非侵入性技术进行了调查：子宫电图（EHG）。对EHG频率相关参数的分析可能有助于将生理性子宫活动与将导致早产的子宫收缩区分开来。然而，尽管已采用了多种参数和方法，但它们尚未得到客观比较。本综述基于截至2008年2月使用Cochrane、PubMed和EMBASE数据库进行的系统文献检索，目的是确定EHG信号的频率相关参数能否可靠地区分将导致早产的早产宫缩与不会导致早产的宫缩（对于最终足月分娩的患者），并确定最准确的参数。在所有不同的EHG参数中，人体和动物研究均表明，功率谱密度峰值频率可能最能预测真正的分娩。因此，预测分娩的最佳参数与通过傅里叶变换、时频或小波分析计算的EHG频谱内容偏移有关。随着测量至分娩间隔的缩短，子宫电频谱成分发生偏移的发生率和程度表明，这些变化可能用于预测早产。也有前景良好的数据表明，将测量参数组合用作人工神经网络算法的输入，可能比单个参数在关键评估子宫活动方面更有用。
妇产科医生、家庭医生。
阅读本文后，读者将能够回忆起导致分娩的子宫收缩的生理学知识，总结宫缩图的局限性，并概述四种不同的子宫电图参数。
