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.