Propagation of spontaneous electrical activity in the ex vivo human uterus.

Contractions of the non-pregnant uterus play a key role in fertility. Yet, the electrophysiology underlying these contractions is poorly understood. In this paper, we investigate the presence of uterine electrical activity and characterize its propagation in unstimulated ex vivo human uteri. Multichannel electrohysterographic measurements were performed in five freshly resected human uteri starting immediately after hysterectomy. Using an electrode grid externally and an electrode array internally, measurements were performed up to 24 h after hysterectomy and compared with control. Up to 2 h after hysterectomy, we measured biopotentials in all included uteri. The median root mean squared (RMS) values of the external measurements ranged between 3.95 µV (interquartile range (IQR) 2.41-14.18 µV) and 39.4 µV (interquartile range (IQR) 10.84-105.64 µV) and were all significantly higher than control (median RMS of 1.69 µV, IQR 1.13-3.11 µV), consisting of chicken breast meat. The RMS values decreased significantly over time. After 24 h, the median RMS (1.27 µV, IQR 0.86-3.04 µV) was comparable with the control (1.69 µV, IQR 1.13-3.11 µV, p = 0.125). The internal measurements showed a comparable pattern over time, but overall lower amplitude. The measured biopotentials propagated over the uterine surface, following both a plane-wave as well as an erratic pattern. No clear pacemaker location nor a preferred propagation direction could be identified. These results show that ex vivo uteri can spontaneously generate propagating biopotentials and provide novel insight contributing to improving our understanding of the electrophysiology of the human non-pregnant uterus.

Dedicated Ultrasound Speckle Tracking for Quantitative Analysis of Uterine Motion Outside Pregnancy.

Fertility problems are nowadays being paralleled by important advances in assisted reproductive technologies. Yet the success rate of these technologies remains low. There is evidence that fertilization outcome is affected by uterine motion, but solutions for quantitative analysis of uterine motion are lacking. This work proposes a dedicated method for uterine-motion quantification by B-mode transvaginal ultrasound. Motion analysis is implemented by speckle tracking based on block matching after speckle-size regularization. Sum of absolute differences is the adopted matching metrics. Prior to the analysis, dedicated singular value decomposition (SVD) filtering is implemented to enhance the uterine motion over noise, clutter, and uncorrelated motion induced by neighboring organs and probe movements. SVD and block matching are first optimized by a dedicated ex vivo setup. Robustness to noise and speckle decorrelation is improved by median filtering of the tracking coordinates from surrounding blocks. Speckle tracking is further accelerated by a diamond search. The method feasibility was tested in vivo with a longitudinal study on nine women, aimed at discriminating between four selected phases of the menstrual cycle known to show different uterine behavior. Each woman was scanned in each phase for 4 min; four sites on the uterine fundus were tracked over time to extract strain and distance signals along the longitudinal and transversal directions of the uterus. Several features were extracted from these signals. Among these features, median frequency and contraction frequency showed significant differences between active and quiet phases. These promising results motivate toward an extended validation in the context of fertilization procedures.

Machine learning for classification of uterine activity outside pregnancy.

The objective of this study was to investigate the use of classification methods by a machine-learning approach for discriminating the uterine activity during the four phases of the menstrual cycle. Four different classifiers, including support vector machine (SVM), K-nearest neighbors (KNN), Gaussian mixture model (GMM) and naïve Bayes are here proposed. A set of amplitude- and frequency-features were extracted from signals measured by two different quantitative and noninvasive methods, such as electrohysterography and ultrasound speckle tracking. The proposed classifiers were trained using all possible feature combinations. The method was applied on a database (24 measurements) collected in different phases of the menstrual cycle, comprising uterine active and quiescent phases. The SVM classifier showed the best performance for discrimination between the different menstrual phases. The classification accuracy, sensitivity, and specificity were 90%, 79%, 93%, respectively. Similar methods can in the future contribute to the diagnosis of infertility or other common uterine diseases such as endometriosis.