## 1 Introduction
A frequency content criterion was a central parameter. Later, [PERSON] et al. [7] used wavelet packets for the same above-mentioned classes as in [20], using as the main criteria its frequentist content. Wavelet packet nodes were allocated to the defined EHG contraction classes. A best basis was obtained and shown to outperform the discrete wavelet transform. Multilayer perceptron and support vector machine were used as classification methods, producing a detection rate of over 85%. [PERSON] et al. [25] used a nonlinear correlation coefficient between two consecutive horizontally bipolar EHG channels as a contraction delineator. The reported detection rates were 62.5% (full detections), 37.5% (partial detections), and 100% (total detections). In another work, [PERSON] et al. [26] compared the IUP estimation using the RMS, the first moment of the spectrogram, and the Teager operator. It was found that the first moment of the spectrogram was the best estimation method, but also the most computationally demanding. The RMS and the Teager operator turned out to be the most computationally efficient, despite not being as accurate as the spectrogram. [PERSON] et al. [27] used uterine magnetomyographic recordings to delineate contractions through the application of the Hilbert transform along with an affinity propagation clustering technique. [PERSON] et al. [3] pinpointed the challenges about the uterine contractions detection in the TOCO, which can be related to the same procedure done on the EHG. These authors used four algorithms to determine relevant basal tones and detection levels, which were able to detect above 91% of all the referenced contractions, where less than 7% of the detected contractions were false positives. In one of the referred algorithms, contractions exceeding three minutes were submitted to reanalysis. [PERSON] et al. [13] introduced enhancements on the dynamic cumulative sum (DCS) method, using a data fusion procedure, thus improving contraction detection and segmentation. [PERSON] et al. [28] manually extracted 7136 uterine contractions from the EHG and the same number of non-contractile events, from which a convolutional neural network (CNN) model was obtained. [PERSON] et al. [29] used the EHGs from 20 pregnant women to report that the RMS and selected parameters of wavelet decompositions were significantly higher for uterine contraction events both in labor and non-labor cases. For the presented work, we assumed that all contractions show an energy burst above the uterine basal signal, resulting in an elevated myometrial activity [17]. It has been demonstrated [30] that at least three different contraction types are present in the EHG, which may be related to different uterine electrophysiologic events, such as delivery triggers. Since the EHG is a non-invasive technique, it can be used for long-term pregnancy monitoring, as part of ambulatory and home-based systems associated with warning labels and automatic clinical reports. The aim of this work is to present and compare five different energy-abased EHG contraction detection methods that can be used in applications that require the input of automatically identified and delineated uterine contractions.