## 4 Discussion  
The squared RMS produced the best results in all the evaluated parameters. Accounting for the respective standard deviations, the RMS stands below the wavelet energy operator as far as the contraction accuracy is concerned, but outperformed the latter--and all the other estimators--in terms of the delineation accuracy and false negative rate. The false negative rate of the squared RMS stands out as being substantially lower compared to the remaining estimators. In the EHG framework, contraction detection and delineation are key subjects that may provide important information for long-term pregnancy monitoring and preterm risk evaluation algorithm development. Previous studies regarding preterm risk evaluation used the complete recorded EHG signal, thus overcoming the need for contraction detection [39]. In this case, it should be assumed that the included basal uterine activity does not impact the overall signal parameters, as was considered in a related work [40]. Nevertheless, for classification and clustering studies, it is expected to be required to work on separated contraction events. There has been an increased interest regarding the identification of the different contraction types in the EHG, for which a delineation operation should be performed. Elsewhere, the authors have published an EHG-based uterine contraction clustering analysis by means of the detected contractions using the methodologies presented herein [30]. The spectral properties of the Braxton Hicks [25] and two different types of Alvarez waves were obtained in that study [30]. Figure 8: Wavelet spectrum for the same contraction, using the five delineation methods. The curve overlap shows that, despite a 30% difference in contraction cut-offs, the energy contents are representative of the event. In various studies, the contraction detection was visually performed, which is a time-consuming task [9; 20]. Considering this, automatic contraction detection methodologies are sought as a step forward in the general framework of EHG processing--namely, in preterm risk evaluation, propagation studies, and contraction classification. The present report describes five different contraction detectors (delineation included) based on the EHG energy bursts--a feature that stands out due to the increased cell-to-cell communication mechanisms leading to extra contractile activity. The best result was obtained with the squared RMS estimation. Notwithstanding the relatively lower score of the Teager operator, this estimator was shown to be able to delineate long-duration EHG events, a feature that could be explored in future work. All the used estimator algorithms have low computational cost, making them a good choice for mobile platforms, having in mind home-based contraction monitoring. A closely related study [2] that used an expert visual scoring method and the IUP as a reference reports a sensitivity of 94.2% and a positive predictive value of 87.6%. These results cannot be directly compared to those reported herein, due to the different scoring methodologies and all the subjects in the referred study being in active labor, and thus not in the context of continuous pregnancy monitoring. In another related study [25], where a nonlinear correlation coefficient was used as a feature for contraction detection, the reported detection rates were 62.5 % (full detections), 37.5% (partial detections), and 100% (total detections). Comparatively, in the present report and selecting the best delineator/detector as the squared RMS, a contraction accuracy of 97.15 \(\pm\) 4.66% and a delineation accuracy of 89.43 \(\pm\) 8.10% are featured. The assessment of these comparative results should be made carefully, taking into account the different expert scoring methodologies. The present work is a contribution to research on automatic contraction detection methodologies. It uses the most evident feature in a uterine contraction event: an energy burst.