Identification of Myoelectric Signals of Pregnant Rat Uterus: New Method to Detect Myometrial Contraction

Aim: To develop an electromyography method for pregnant rat uterus in vivo and to separate myometrial signals from the gastrointestinal tract signals.

Methods: Pregnant Sprague-Dawley rats (n=8) were anaesthetized and their stomach, small intestine, and large intestine were removed from the abdomen. A pair of thread electrodes was inserted into the uterus, while a pair of disk electrodes was placed subcutaneously above the myometrium. Additionally, a strain gauge sensor was fixed on the surface of the myometrium and cecum for the parallel detection of mechanical contractions in rats (n=18) with intact gastrointestinal tract. The filtered electric signals were amplified and recorded by an online computer system and analyzed by fast Fourier transformation. The frequency of the electric activity was characterized by cycle per minute (cpm), the magnitude of the activity was described as power spectrum density maximum (PsDmax).

Results: The frequency of the pregnant uterine activity was 1-3 cpm, which falls within the same range as that of cecum. Measuring by both electrodes, oxytocin (1 µg/kg) increased and terbutaline (50 µg/kg) decreased the PsDmax by 25%-50% (P<0.001) and 25%-40% (P<0.01), respectively. We found a strong positive correlation between the alterations of PsDmax values and the strain gauge sensor-detected mechanical contractions (area under curve). The GI specific compounds (neostigmine, atropine) mainly affected the cecal activity, while myometrium specific drugs (oxytocin, terbutaline) influenced the myometrial signals only. Conclusion Our method proved to be able to detect the myoelectric activity that reflects the mechanical contraction. The overlapping myometrial and cecal signals are not separable, but they can be distinguished based on the much higher activity and different pharmacological reactivity of the pregnant uterus. Thus, the early signs of contractions can be detected and labor may be predicted in a fast and sensitive way.