## 4 Evaluation and Results
### Features Evaluation and Discussion
This evaluation strategy allows identifying the optimal feature sets and the classification performance achieved for each signal segment, whose results are presented in Table 5.  
Results reveal that the analysis of CS achieved 81.7% quality, which is higher than DD and DR, independently. Nevertheless, the best classification performance was 83.2%, whose value was obtained by the contribution of features extracted from the CS and DR, whose performance was higher than the previous value obtained by only CS. Particularly, when considering CS in combination with DD, the classification performance increased from 81.7% to 82.5% of quality. Then, when including features extracted from DR, the classification performance was 83.2%. As can be observed in the last row of Table 5 (CS_DD_DR), the classification performance did not increase compared with the results obtained from CS_DR. Likewise, the optimal feature sets were the same for both analyses. This phenomenon indicates that features extracted from DD do not contribute to improving the CTG classification compared to the combination of features extracted from the CS and DR. It is important to note that considering DD in combination with DR (DD_DR), the classification performance achieved 76.2%, which is still lower compared with CS. In consequence, features extracted from the CS are still required; i.e., the analysis of the complete signal should not be replaced by the analysis of only decelerations and resting periods, but rather include them for the analysis. The best classification performance was achieved by the features sets CS\({}_{4}\), CS\({}_{11}\), CS\({}_{12}\), and DR\({}_{5}\), where two of them correspond to modal-spectral, whereas the other two sets correspond to conventional features. As presented in Tables 2 and 4, the select sets were extracted from the indicators IMF\({}_{4}\)-\(E^{c}\), FHR signal, PBL, and IMF\({}_{6}\)-\(E\), respectively. The results coincide with a recent approach [19], which employs another database, but similar class formation criteria and evaluation. That research shows that, considering features extracted from the entire FHR segment in combination with contraction-dependent features, might improve the detection of fetal distress, whose classification performance, based on the geometric mean, improved from 70% to 79%. Our approach based on CEEMDAN and TV-AR modeling shows that considering the complete segment and resting periods independently, the CTG classification performance improved from 81.7% to 83.2%. From a physiological perspective, these results are highly interesting, because, as can be observed in Figure 5 (right column), the IMF\({}_{4}\) and IMF\({}_{6}\) involve spectral information inside the LF band (0.03-0.15 Hz), which is associated with the fetal sympathetic ANS activity. Therefore, these results show that both CS and DR may involve significant information related to the sympathetic ANS activity, whose independent analysis can help to assess the fetal condition, and thereby improve the CTG's classification performance. Note that the LF band mentioned above is used only as a reference in order to associate the significant IMF dynamics with a possible physiological phenomenon. It is an important characteristic of our approach because the analysis is not performed only on an established frequency range; rather, it is based on tracking FHR dynamics that can involve information of interest in those frequency bands.