Extraction uterine contraction signal from abdominal uterine electromyogram (EMG) signal is considered as the most promising method to replace the traditional tocodynamometer (TOCO) for detecting uterine contractions activity. The traditional root mean square (RMS) algorithm has only some limited values in canceling the impulsive noise. In our study, an improved algorithm for uterine EMG envelope extraction was proposed to overcome the problem. Firstly, in our experiment, zero-crossing detection method was used to separate the burst of uterine electrical activity from the raw uterine EMG signal. After processing the separated signals by employing two filtering windows which have different width, we used the traditional RMS algorithm to extract uterus EMG envelope. To assess the performance of the algorithm, the improved algorithm was compared with two existing intensity of uterine electromyogram (IEMG) extraction algorithms. The results showed that the improved algorithm was better than the traditional ones in eliminating impulsive noise present in the uterine EMG signal. The measurement sensitivity and positive predictive value (PPV) of the improved algorithm were 0.952 and 0.922, respectively, which were not only significantly higher than the corresponding values (0.859 and 0.847) of the first comparison algorithm, but also higher than the values (0.928 and 0.877) of the second comparison algorithm. Thus the new method is reliable and effective.
Identification of real-time uterine contraction status is very significant to labor analgesia, but the traditional uterine contraction analysis algorithms and systems cannot meet the requirement. According to the situations mentioned above, this paper designs a set of algorithms for the real-time analysis of uterine contraction status. The algorithms include uterine contraction signal preprocessing, uterine contraction baseline extraction based on histogram and linear iteration and an algorithm for the real-time analysis of uterine contraction status based on finite state machines theory. It uses the last uterine status and a series of state transfer conditions to identify the current uterine contraction status, as well as a buffer mechanism to avoid false status transitions. To evaluate the performance of the algorithm, we compare it with an existing uterine contraction analysis algorithm used in the electronic fetal monitor. The experiments show that our algorithm can analyze the uterine contraction status while monitoring the uterine contraction signal in a real-time. Its sensitivity reaches 0.939 9 and its positive predictive value is 0.869 3, suggesting that the algorithm has high accuracy and meets the need of clinical monitoring.