## 5 Discussion  
As stated in Section 2, there are numerous pieces of research on automatic CAP detection and classification; some are oriented toward implementing different feature extraction techniques in combination with classification models, and others are oriented toward implementing deep learning models without manually extracted features. It is noteworthy that the latter have gained popularity in recent years. The diversity of methodologies and classification approaches indicated in Table 1 has two opposite effects. It allows for exploring new methodologies to solve the CAP detection and classification problem while hindering their direct comparison. Nevertheless, the proposed methodology for a distributional representation of CAP A-phases is a valuable approach since it reduces the complexity of the problem by transforming the sleep EEG data into shorter symbolic strings. Moreover, the proposed methodology \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**D2V** & **Clf.** & & & & \\
**Output** & **Input** & **Accuracy** & **F1 Score** & **Recall** & **Precision** \\
25 & 50 & 0.7509 \(\pm\) 0.0126 & 0.7579 \(\pm\) 0.0125 & 0.7509 \(\pm\) 0.0126 & 0.7673 \(\pm\) 0.0131 \\
100 & 50 & 0.7519 \(\pm\) 0.0170 & **0.7587 \(\pm\) 0.0157** & 0.7519 \(\pm\) 0.0170 & **0.7868 \(\pm\) 0.0141** \\
300 & 50 & **0.7522 \(\pm\) 0.0116** & 0.7586 \(\pm\) 0.0116 & **0.7522 \(\pm\) 0.0116** & 0.7673 \(\pm\) 0.0122 \\
500 & 50 & 0.7458 \(\pm\) 0.0186 & 0.7541 \(\pm\) 0.0181 & 0.7458 \(\pm\) 0.0186 & 0.7656 \(\pm\) 0.0183 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Classification results with stratified 10-fold cross-validation with PCA (50 features). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**D2V** & **Clf.** & & & & \\
**Output** & **Input** & **Accuracy** & **F1 Score** & **Recall** & **Precision** \\
25 & 100 & 0.7535 \(\pm\) 0.0146 & **0.7609 \(\pm\) 0.0137** & 0.7535 \(\pm\) 0.0146 & **0.7709 \(\pm\) 0.0130** \\
100 & 100 & **0.7548 \(\pm\) 0.0185** & 0.7609 \(\pm\) 0.0175 & **0.7548 \(\pm\) 0.0185** & 0.7669 \(\pm\) 0.0166 \\
300 & 100 & 0.7522 \(\pm\) 0.0103 & 0.7587 \(\pm\) 0.0108 & 0.7522 \(\pm\) 0.0103 & 0.7675 \(\pm\) 0.0121 \\
500 & 100 & 0.7490 \(\pm\) 0.0184 & 0.7568 \(\pm\) 0.0179 & 0.7490 \(\pm\) 0.0184 & 0.7676 \(\pm\) 0.0179 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Classification results with stratified 10-fold cross-validation with PCA (100 features). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**D2V** & **Clf.** & & & & \\
**Output** & **Input** & **Accuracy** & **F1 Score** & **Recall** & **Precision** \\
50 & 100 & 0.7535 \(\pm\) 0.0146 & **0.7609 \(\pm\) 0.0137** & 0.7535 \(\pm\) 0.0146 & **0.7709 \(\pm\) 0.0130** \\
100 & 100 & **0.7548 \(\pm\) 0.0185** & 0.7609 \(\pm\) 0.0175 & **0.7548 \(\pm\) 0.0185** & 0.7690 \(\pm\) 0.0166 \\
300 & 100 & 0.7522 \(\pm\) 0.0103 & 0.7587 \(\pm\) 0.0108 & 0.7522 \(\pm\) 0.0103 & 0.7675 \(\pm\) 0.0121 \\
500 & 100 & 0.7490 \(\pm\) 0.0184 & 0.7568 \(\pm\) 0.0179 & 0.7490 \(\pm\) 0.0184 & 0.7676 \(\pm\) 0.0179 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Best classification results using hold-out validation, obtained with RBF SVC—balanced. allows for the use of powerful NLP tools that are well-known for their versatility and outstanding results. This proposal has certain particularities that make it unique. It translates the EEG signals into sequences of symbols in a different way to the work of [PERSON] et al. (2012) [20] and [PERSON] et al. (2020) [22]. Instead, 1d-SAX transformation was implemented. The reason for making this decision was to reduce the problem's complexity by representing the time series data as shorter sequences of symbols. Then, a method for vectorization was implemented, similar to [PERSON] et al. (2018) [21], yet this process was carried out with a Doc2 Vec model instead of using Wave2 Vec or Word2 Vec, which have comparable methodologies.