BACKGROUND. Managing women in pregnancy or labour is becoming a serious challenge because of delayed conception age and higher morbidity. The main negative factor is increasing numbers of overweight and obese women. Fatty tissue significantly biases the detection of uterine contractions by tocography, which is routinely used in obstetrical wards. Thus, the FDA approved an alternative method called electrohysterography (EHG) and recommended it for women with an over-normal BMI. However, almost all published methods of labour prediction based on EHG signals use a “black-box model” approach, i.e., increasingly numerically complex signal features and classification algorithms that are chosen a priori, without any physiological rationale behind them. This makes using these algorithms difficult in obstetrical practice. AIM. The aim of the study was to show that a simple classifier based on a single and physiologically interpretable parameter can predict uterine contractions during labour with an accuracy comparable to advanced classifiers. METHODS. An obstetrical interpretable EHG parameter was introduced called the uterine activity index. To avoid the influence of confounding factors associated with preterm labour and imbalanced signal sets, this classifier was evaluated using the private, retrospective database of EHG signals registered for 45 women in the third trimester of a pregnancy, and 31 women in the second stage of labour with a normal BMI. The classifier, based on the logistic regression model, was tested using the bootstrap method. RESULTS. The bootstrapping mean (95% confidence interval) of the AUC ROC estimated for the 200 bootstrap samples was 0.96 (0.91–0.99). This accuracy was slightly better for EHG signals in comparison to predictions based on classical tocography. CONCLUSIONS. The obtained results confirm that a simple physiologically explained classifier can be considered in commercial applications of electrohysterography. However, its clinical significance should be evaluated through properly designed randomised clinical trials.

This study aimed to show what role biomedical engineering can play in sexual health. A new concept of sexological ontology, an essential tool for building evidence-based models of sexual health, is proposed. This ontology should be based on properly validated mathematical models of sexual reactions identified using reliable measurements of physiological signals. This paper presents a review of the recommended measurement methods. Moreover, a general human sexual reaction model based on dynamic systems built at different levels of time × space × detail is presented, and the actual used modeling approaches are reviewed, referring to the introduced model. Lastly, examples of devices and computer programs designed for sexual therapy are described, indicating the need for legal regulation of their manufacturing, similar to that for other medical devices.