Premature birth is one of the major problems in obstetrics. Early detection of preterm birth is an important key to prevent and reduce its consequences. As a result, it has been a subject of interest for many researchers. The current work is placed as part of the development of a non-invasive clinical aid tool. We implement a portable and easy-to-use device for pregnant women be able to automatically detect signs associated with uterine contractions. The device is able to detect EHG segments associated with uterine contractions during pregnancy. EHG signals are acquired, in real time, from 12 bipolar electrodes placed on the abdomen of pregnant women. The data are then analyzed and processed with a powerful processor. The technique used for detection is based on the Dynamic Cumulative Sum (DCS) method which has already demonstrated its strong potential in event detection when applied to non-stationary signals. The DCS method is followed by a data fusion method to merge the segments of all bipolar channels. A technique based on Fisher's test has been implemented and applied between two consecutive segments in order to reduce the over-segmentation problem. This strategy was proposed in previous studies and was proved to be accurate and efficient. The results show that a prototype based on the Raspberry Pi board and the Arduino Mega board is very promising for setting up a final prototype.

Over-segmentation in non-stationary signals remains a challenge in several automatic segmentation methods. In this context, most of automatic segmentation methods of contractions in multichannel uterine Electromyogram signals (Electrohysterogram signals, EHGs) encounter the problem of detecting other events which are not corresponding to contractions labeled by expert. In addition, the great advantage of using wavelet manifests by its ability to separate the fine details in a signal. Many studies applied wavelets for sake of detecting and analyzing abrupt changes in non-stationary signals. This study is another step of our project focused on the automatic contraction segmentation of EHGs. Indeed, we will focus on the application of dynamic cumulative sum method (DCS) with over-segmentation elimination techniques on bipolar EHGs and details after wavelet decomposition of those signals. Detected events are then compared to contractions identified by expert using Margin validation test with and without application of over-segmentation techniques. Regarding the obtained sensitivity and other events rate of methods we find that DCS with over-segmentation elimination techniques application on bipolar EHGs gives the highest sensitivity 100% and lowest other event rate 39.77%.

Until recently, many segmentation research trials on uterine EMG have been recorded for the sake of not only automatic detection of contractions but also curtailment of other events presents in the electrohysterogram (EHG). In this study, we use an online segmentation method, which has proven its efficiency and known by Dynamic Cumulative Sum (DCS). The method is first applied on real bipolar EHGs signals then on their details obtained by wavelet transform. The detected rupture instants are driven through an enhanced technique of faulty rupture instants elimination, dynamic selection of multichannel bipolar EHG signals and its details after wavelet transform and event tracking algorithm. Therefore, enhanced method sensitivity and “other events” rate of bipolar EHGs with and without wavelet are computed using Margin validation test in order to classify among events as contractions or not referring to contractions identified by expert. Indeed, enhanced technique of event tracking, proposed in this study, seems to be more efficient comparing to previous techniques. Further studies should be achieved for the sake of fully identifying the uterine contractions from other events and then decreasing the “other events” rate.

Until recently, many studies have been achieved for the sake of automatically segmentation of the Electrohysterogram (EHG) in order to identify the efficient uterine contractions but the most of them encountered the presence of other events such as motion artifacts and other kind of contractions despite of the use of efficient filtering methods. In this study, we apply an online method which is developed previously and known by Dynamic Cumulative Sum (DCS) on monopolar EHG signals acquired through a 4x4 electrodes matrix with and without Canonical Correlation Analysis and Empirical Mode Decomposition (CCA-EMD) denoising method, then on monopolar EHG after wavelet decomposition. The detected segments are driven through an automatic concatenation technique of detected event time from all channels in order to reduce the unwanted segments, the obtained segments then undergo to implemented Margin validation test in order to classify among them. Sensitivity of detected contractions and other detected events rate referring to identified contractions by expert have been calculated in order to track the efficiency of the fully automated multichannel segmentation method. Additional EHG filtering techniques like CCA-EMD method seems to be better but effective time cost. Further studies should be achieved in order to decreasing the other events rate for the sake of fully identifying the uterine contractions.

Automatic segmentation of contractions in multichannel uterine EMG signals is the main issue of interest in many recent studies. Most of them are faced with the problem of the detecting other events than contractions. Wavelets have the great advantage of being able to separate the fine details in a signal. Many studies applied wavelets for sake of detecting and analyzing abrupt changes in non-stationary signals. This study is another step of our project focused on the automatic contractions segmentation of uterine EMG signals. Indeed, we will focus on the application of dynamic cumulative sum method (DCS) with over-segmentation elimination techniques on details after wavelet decomposition of bipolar uterine EMG records. DCS will be applied in mono- and multi-dimensional. Detected events are then compared to contractions identified by expert using Margin validation test. Regarding the obtained sensitivity and other events rate of methods we find that DCS with multidimensional application give the highest sensitivity 100% and lowest other events rate 49.6%.

Until recently, many studies have been achieved for the sake of automatically segmentation of the electrohysterogram (EHG) in order to identify the efficient uterine contractions but the most of them encountered the presence of other events such as motion artifacts and other kind of contractions despite of the use of efficient filtering methods. In this study, we apply an online method which is developed previously and known by Dynamic Cumulative Sum (DCS) on monopolar EHG signals acquired through a 4×4 electrodes matrix with and without CCA-EMD denoising method. The detected segments are driven through an automatic concatenation technique of detected event time from all channels in order to reduce the unwanted segments, the obtained segments then undergo to implemented Margin validation test in order to classify among them. Sensitivity of detected contractions and other detected events rate referring to identified contractions by expert have been calculated in order to track the efficiency of the fully automated multichannel segmentation method. Additional EHG filtering techniques like CCA-EMD method seems to be better but effective time cost. Further studies should be achieved in order to decreasing the other events rate for the sake of fully identifying the uterine contractions.

In a recent past, several techniques have been developed to analyze the events contained in the electrohysterogram signals (EHG). But, the most of them focused on online methods. In this study, we use online methods like Fractal Dimension, Wavelet transform technique, Dynamic Cumulative Sum (DCS). Methods were applied on synthetic signals and real labeled EHG signals database acquiring using $4 x 4$ electrodes matrix. For this purpose, three parameters affecting the Fractal dimension method, the size of analyzing window, thresholding value and the window overlapping, were first tuned in order to identify the recommended values for ruptures detection of the EHG signals by comparing results to its label. According to the obtained results, these three methods seem to be encouraging methods that could be used for automatic ruptures segmentation.

In a recent past, several techniques have been developed to analyze the effect of electrode grid alignment with the muscle fibers. But, they focused on pattern matching of Motor Unit (MU) electrical signatures and not on the interference signal. In this study, we use a method which is widely applied in connectivity and directionality analysis of stochastic and complex signals, namely, the nonlinear correlation coefficient (h <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) on HD-sEMG signal matrices. The approach is first applied on simulated data from recent generation model through an (8×8) simulated electrode matrix, then on real signals using the same grid specifications on the Biceps Brachii. Both simulated and real data were evaluated with three angles of grid alignment with respect to muscle fibers. For this purpose, five parameters were extracted from obtained h <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> correlation matrices and tested. According to the obtained results, a relationship between h <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values and the electrode matrix alignment seems to exist. However, further efforts are needed to design parameters more sensitive to grid misalignment with respect to muscle fibers.

Automatic segmentation of contractions in multichannel uterine EMG signals is the main issue of interest in many recent studies. Most of them are faced with the problem of the detecting other events than contractions. Wavelets have the great advantage of being able to separate the fine details in a signal. Many studies applied wavelets for sake of detecting and analyzing abrupt changes in non-stationary signals. This study is another step of our project focused on the automatic contractions segmentation of uterine EMG signals. Indeed, we will focus on the application of dynamic cumulative sum method (DCS) with over-segmentation elimination techniques on details after wavelet decomposition of bipolar uterine EMG records. DCS will be applied in mono- and multi-dimensional. Detected events are then compared to contractions identified by expert using Margin validation test. Regarding the obtained sensitivity and other events rate of methods we find that DCS with multidimensional application give the highest sensitivity 100% and lowest other events rate 49.6%.