论文部分内容阅读
目的利用体表子宫肌电信号的分析实现宫缩和非宫缩状态的识别。方法利用Monica AN24母胎监护仪采集10名孕期和10名临产期孕妇的子宫肌电信号,然后提取了子宫肌电信号线性和非线性特征参数及其变化率,特征参数包括均方根、峰值频率、中值频率、平均频率、小波包分解系数方差和时间可逆性。使用统计学方法对提取的数据特征进行单因素方差分析,比较了临产组和孕期组中宫缩段和非宫缩段信号的差异。结果组内宫缩段信号与非宫缩段信号、组间宫缩段信号与非宫缩段信号的特征差异具有统计学意义(P<0.05)。结论子宫肌电信号的研究为子宫收缩的识别与监测提供了重要的参考价值。
Objective To use the analysis of body surface EMG to realize the identification of contractions and non-contractions. Methods Monica AN24 maternal fetal monitor collected 10 pregnant women and 10 pregnant women’s uterine EMG signal, and then extracted the linear and non-linear characteristic parameters of uterine electromyogram and its rate of change, the characteristic parameters including root mean square, peak frequency , Median frequency, average frequency, wavelet packet decomposition coefficient variance and time reversibility. One-way ANOVA was used to analyze the extracted data features using statistical methods, and the differences of contractile and non-contractile signals in labor group and pregnancy group were compared. Results The characteristics of uterine contractions and non-uterine contractions, signals of uterine contractions and signs of uterine contractions were statistically significant (P <0.05). Conclusion The study of uterine myoelectric signal provides an important reference value for the identification and monitoring of uterine contractions.