Analysis of uterine contractions using electromyography signals is gaining importance due to its capability to measure the dynamics of uterus. Uterine electromyography (uEMG) provides information on the nature of uterine contractions non-invasively. In this study, the fluctuations in uEMG signals associated with Term pregnancies are analyzed. For this, Term uEMG signals corresponding to second (T1) and third (T2) trimesters are considered. The signals are subjected to Adaptive Fractal Analysis (AFA), wherein a global trend is obtained by using overlapping windows of three orders namely, 25%, 50% and 75%. The signals are detrended and the fluctuation function is estimated. Two Hurst exponent features computed at short range (Hs) and long range (Hl) are extracted and statistically analyzed. Results show that AFA is able to characterize variations in the fluctuations of Term delivery signals. The feature values are observed to vary significantly during different weeks of gestation. It is found that features of T2 signals are higher than that of T1 signals for all the considered overlaps, indicating that T2 signals possess smoother characteristics than T1 signals. Further, coefficient of variation is observed to be low, indicating that these features are able to handle the inter-subject variations in Term signals. Therefore, it appears that the proposed approach could aid in investigation of progressive changes in uterine contractions during Term pregnancies.

由于其能够测量子宫动态，使用肌电图信号分析子宫收缩变得越来越重要。子宫肌电图 (uEMG) 以非侵入性方式提供有关子宫收缩性质的信息。在这项研究中，分析了与足月妊娠相关的 uEMG 信号的波动。为此，考虑对应于第二 (T1) 和第三 (T2) 三个月的术语 uEMG 信号。对信号进行自适应分形分析 (AFA)，其中通过使用三个阶数（即 25%、50% 和 75%）的重叠窗口获得全局趋势。去趋势信号并估计波动函数。在短距离 ( Hs ) 和长距离 ( Hl )计算的两个 Hurst 指数特征) 进行提取和统计分析。结果表明，AFA 能够表征 Term 传递信号波动的变化。观察到特征值在妊娠的不同周内显着变化。发现对于所有考虑的重叠，T2信号的特征都高于T1信号的特征，表明T2信号比T1信号具有更平滑的特征。此外，观察到变异系数很低，表明这些特征能够处理 Term 信号中的主体间变化。因此，所提议的方法似乎有助于调查足月妊娠期间子宫收缩的渐进性变化。