## 4 Evaluation and Results
### Definition of Proposed Features  
This section presents a comprehensive analysis behind the extraction of the proposed features, whose explanation is performed using two representative cases extracted from the dataset described in Section 3.1. These cases are presented in Figure 7, which belong to the recording numbers 1189 (left column) and 2011 (right column) of the CTG database, as examples of normal and acidotic  
Figure 6: Identification of deceleration episodes. (**a**) Preprocessed FHR signal (blue) and floating-line (red); (**b**) progressive baseline (PBL) (magenta) and evident deceleration episodes (black); (**c**) preprocessed UC signal (blue) and signal apexes (black dots); (**d**) deceleration episodes after the second identification step (D01...D10) (black). conditions, respectively. Figure 7a,b shows the raw FHR signal of each recording. The second row shows the preprocessed FHR signal (blue), computed PBL (magenta), floating-line (red), and the identified deceleration episodes (black). The third and fourth row shows the detrended FHR signal and the IMF\({}_{6}\), which was arbitrarily selected for the explanation of the proposed spectral analysis. This IMF involves spectral dynamics in the frequency range between 0.03 and 0.15 Hz (see Figure 5), whose band, as explained in Section 2.1, is mainly related to the fetal sympathetic ANS activity. The fifth and sixth rows show the TV-AR spectrum computed from the IMF\({}_{6}\) and its corresponding total energy (\(E\)), respectively. The total spectral energy \(E\) is calculated in the complete band of interest (0-2 Hz) for each time instant \(n\), as described in Equation (11). For better visualization and comparison of these two examples, the spectral energy values were scaled between 0 and 100 with respect to the maximum value of energy of these indicators. Besides, in the third, fourth, and sixth rows, the deceleration episodes are highlighted in gray. It is important to mention that the data normalization explained above was performed only for a better explanation of this example. For the feature computation and evaluation, as presented in Section 4.2, the features were standardized using \(z\)-score based normalization. It is important to note that for the graphic explanation of the proposed spectral analysis, only one IMF was considered. Nevertheless, for the subsequent feature extraction operation (performed in Section 4.2), the entire FHR frequency range of interest was considered by analyzing the first ten IMFs, as explained in Section 3.2.3. \[E[n]=\sum_{f=0}^{2 Hz}(SAR[f,n]). \tag{11}\]  
Results shown in Figure 7i,j indicates that the representative examples exhibit important spectral dynamics strongly variant in time. As represented by the spectral energy \(E\) (last row), the spectral behavior differs between the normal and the acidotic case. Notably, for the normal case, the indicator \(E\) (see Figure 7k) presents pronounced variations in amplitude. In contrast, the example corresponding to an acidotic fetal condition shows completely different behavior. In this acidotic case, \(E\) is considerably lower and exhibits a less marked variation in amplitude compared with the previous case. This phenomenon can be directly related to the fetal health status because the fetal response, modulated by the sympathetic ANS activity, may decrease for an acidotic case [13]. In order to evaluate whether those graphic observations are related to the fetal health condition, we compute the indicator \(E\) for the complete dataset (defined in Section 3.1). Then, from each obtained indicator, we calculate two statistical coefficients: the average (\(\mu\)) and the standard deviation (\(\sigma\)). It is important to note that for this example, only two features were computed; nevertheless, as explained in Section 4.2, for the evaluation of our approach, a larger set of features was extracted.