## 4 Evaluation and Results
### Feature Computation  
The analysis performed above opens perspectives for the extraction of a larger set of features, including not only the IMF\({}_{6}\) (analyzed in the previous section), but rather all the IMFs involving dynamics in the FHR frequency bands established in the literature (see Section 2.1). According to the spectral information involved in the IMFs, as explained in Section 3.2.3, we considered the first ten IMFs for the analysis. Besides, in addition to the indicator of total spectral energy (\(E\)) (Equation (11)), we computed the energy of the main component (\(E^{c}\)), and frequency of the main component (\(f^{c}\)). These indicators are described in Equation (12), where \(f^{c}\) corresponds to the frequency value at which the spectral energy exhibits the maximal level for each sample \(n\). Then, we made use of seven statistical coefficients that have been commonly used in CTG analysis, which were computed for each indicator. This set of coefficients corresponds to the arithmetic mean (\(\mu\)), median (M), standard deviation (\(\sigma\)), mean absolute deviation (mad), root mean square (RMS), sample entropy (SampEn) [68], and approximate entropy (ApEn) [69]. These last two coefficients allow estimating the signal complexity, which might be related to the interaction of the fetal sympathetic and parasympathetic ANS response [70]. Besides, according to the literature [38; 71; 72], entropy-based features have shown better performance in classification compared with the conventional CTG signal analysis. For the computation of the entropy-based features, following [69; 73; 74], we employed an embedding dimension \(m=2\) and a tolerance \(r=0.2\times\sigma\), where \(\sigma\) was the standard deviation. \[E^{c}[n]=SAR[f,n],f=f^{c}. \tag{12}\]  
In addition to the features described above (denoted as the _modal-spectral_ features), and following the results obtained in [61], we propose to include another set of time-domain features (denoted as the _conventional_ features), which have shown relatively satisfactory results in CTG classification. For this operation, we make use of the same seven statistical coefficients described above, which are computed from the IMFs in the time-domain, raw FHR signal, PBL, and detrended FHR signal. It is important to note that for the IMFs in the time-domain and detrended FHR signal analysis, central tendency measures are not informative; therefore, the arithmetic mean and median coefficients are not considered for such indicators. Figure 8: Box plots of representative features. The borders of the box are the 25 th and 75 th percentiles of the data. The median of the data is represented by the red line, and red crosses are the outliers. As explained at the beginning of Section 3, the main idea is to analyze the FHR signal during decelerations (DD) and resting periods (DR), independently from the complete signal (CS), thereby investigating whether the CTG classification can improve compared with the traditional analysis based on the CS. However, considering that the deceleration episodes can be 15 s in (60 samples) length, the SampEn and ApEn coefficients are not appropriate for the analysis of DD because their computation is applicable only for segments longer than 100 samples [69], or 200 samples [75] length. As a result, 210 modal-spectral and 69 conventional features are computed from CS and DR, independently, whereas the features computed from DD include 150 modal-spectral and 63 conventional features. Taking into account that the fetal response can change in the course of labor and that the FHR signal quality can decrease toward the delivery, it is necessary to consider an informative signal segment, as near as possible to the delivery where features can be computed (also called as _epoch_). According to the literature [5], there is not a standard definition of an optimal epoch, whose selection depends mainly on the type of the analysis performed, such as time-invariant, time-variant, short term, or long term.