## 4 Evaluation and Results
### Features Evaluation and Discussion  
Once the feature sets are defined, we proceed to evaluate their classification performance. For this operation, we employ the strategy proposed in [61]. This evaluation strategy is based on stratified 5-folds cross-validation and uses a SVM as a classifier. First, the data in the feature dimension is randomly split into training and testing set but keeping the proportion of the normal and acidotic classes, where four folds are used as training, and one as testing data. Then, for the training set, principal component analysis (PCA) is applied, and the testing set data is transformed accordingly. This method provides attributes in the feature dimension that are less correlated with each other but that keep as much significant information as possible [44]. After that, considering the imbalanced number of observations between classes, the adaptive synthetic sampling technique (ADASYN) is used. This technique can adaptively generate synthetic data based on both the minority class and the nearest majority class data. It is applied to the training set to generate synthetic data for the minority class by using a factor of 19 and 5 \(k\)-neighbors. For a detailed explanation of the ADASYN method, please refer to [76]. Finally, the data resulting from such operation is used as the input data for the classifier, and the trained model is used to evaluate the testing set. As a metric for classification performance, we use the geometric mean (see Equation (13)), where Se and Sp correspond to the statistical metrics of sensitivity and specificity, respectively. These measures have been commonly used in CTG classification as appropriate indicators of quality (\(QI\)) for imbalanced data [44; 77]. In this work, the complete feature evaluation procedure is repeated 300 times; thereby, the performance metric \(QI\) is based on the average of the results obtained from the 300 iterations. \[QI=\sqrt{Se\cdot Sp}. \tag{13}\]  
The strategy explained above was applied separately for the CS, DD, and DR. Likewise, we evaluated the classification performance by combining the feature sets extracted from these groups. \begin{table}
\begin{tabular}{c l l} \hline \hline
**Feature Set** & **Significant Features** & **Type** \\ \hline \(DR_{1}\) & IMF\({}_{1}\)-\(E\) & \(\mu\), M, RMS & \\ \(DR_{2}\) & IMF\({}_{1}\)-\(E^{c}\) & \(\mu\), M, RMS & \\ \(DR_{3}\) & IMF\({}_{2}\)-\(E\) & \(\mu\), mad & \\ \(DR_{4}\) & IMF\({}_{2}\)-\(E^{c}\) & ApEn & \\ \(DR_{5}\) & IMF\({}_{6}\)-\(E\) & \(\sigma\), ApEn, SampEn, RMS & modal-spectral \\ \(DR_{6}\) & IMF\({}_{8}\)-\(E\) & ApEn, SampEn & \\ \(DR_{7}\) & IMF\({}_{10}\)-\(E^{c}\) & SampEn & \\ \(DR_{8}\) & IMF\({}_{7}\)-\(f^{c}\) & \(\mu\), \(\sigma\), mad & \\ \hline \(DR_{9}\) & FHR signal & \(\sigma\), mad, ApEn, SampEn & \\ \(DR_{10}\) & PBL & \(\sigma\), mad & conventional \\ \(DR_{11}\) & detrended FHR & ApEn, SampEn & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Feature sets and corresponding significant features extracted during resting periods (DR). \begin{table}
\begin{tabular}{c l l l} \hline \hline
**Feature Set** & **Significant Features** & **Type** \\ \hline \(DD_{1}\) & IMF\({}_{1}\)-\(E\) & \(\mu\), \(\sigma\), mad, RMS & \\ \(DD_{2}\) & IMF\({}_{4}\)-\(E\) & \(\sigma\) & \\ \(DD_{3}\) & IMF\({}_{4}\)-\(E^{c}\) & \(\sigma\), RMS & \\ \(DD_{4}\) & IMF\({}_{5}\)-\(E^{c}\) & M & modal-spectral \\ \(DD_{5}\) & IMF\({}_{6}\)-\(E\) & \(\mu\), M, \(\sigma\), mad, RMS & \\ \(DD_{6}\) & IMF\({}_{6}\)-\(E^{c}\) & \(\mu\), M, \(\sigma\), mad, RMS & \\ \(DD_{7}\) & IMF\({}_{6}\)-\(f^{c}\) & M & \\ \hline \(DD_{8}\) & FHR signal & \(\mu\), M, RMS & \\ \(DD_{9}\) & PBL & \(\mu\) & conventional \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Feature sets and corresponding significant features extracted during decelerations (DD).