Premature birth is the leading cause of a neonatal death, so, it is extremely important to distinguish the pregnancy at risk of preterm threatening labour. The electrohysterography seems very promising as a method which enables noninvasive recording of readable bioelectrical signal of uterine muscle. The developed instrumentation enabled simultaneous recording of bioelectrical signals by means of electrodes attached to abdominal wall and mechanical activity of uterine muscle using fetal monitor. Material comprised 27 patients in physiological pregnancy (27 ÷ 40 week), and 21 patients (23 ÷ 36 week) with the symptoms of threatening premature labour. The obtained results showed that quantitative parameters of detected uterine contractions: amplitude and contraction area, obtained both for mechanical and electrical activity, were statistically significant (p < 0.05) to distinguish the patients at risk of premature labour. However, their reliability is low because they strongly depend on individual patient features. We consider the parameters characteristic for electrohysterogram exclusively e.g. contraction power and its median frequency as more useful (p < 0.05). Noninvasive electrohysterography ensures higher sensitivity and specificity for recognition of uterine contraction activity in comparison to classical mechanical method.
Monitoring of uterine contractile activity enables to control the progress of labour. Automated detection of contractions is to be an integral part of the signal analysis implemented in computer aided fetal surveillance system. Evaluation of efficiency of three algorithms for automated detection of uterine contractions in the signal of uterine mechanical activity is presented. These algorithms are based generally on analysis of the frequency distribution of signal values. The reference data in form of beginning and end of contraction episodes were obtained from human expert. Obtained results showed high efficiency of the algorithms tested where the best one ensured the sensitivity and positive predictive value equal to 92.2 and 97.2, respectively.
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Monitoring of uterine contractile activity enables to control the progress of labor. Automated detection of contractions is an integral part of the signal analysis implemented in computer- aided fetal surveillance system. Comparison of four algorithms for automated detection of uterine contractions in the signal of uterine mechanical activity is presented. Three algorithms are based generally on analysis of the frequency distribution of signal values. The fourth method relies on analyzing the rate of changes of the uterine activity signal. The reference data in form of beginning and end of contraction episodes were provided by human experts. Obtained results show that all algorithms were capable to detect above 91% reference contractions, and less than 7% of recognized patterns were false. Two algorithms can be distinguished as providing a higher performance expressed by the sensitivity of 95% and the positive predictive value of 97%. Such results could be obtained by optimization of contraction validation criteria.
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