## 5 Conclusions  
This study presented a novel approach for automated epilepsy detection using VFCDM in combination with CNN. The results demonstrated that VFCDM effectively extracted non-stationary and non-linear variations in EEG signals, enabling the discrimination of various epileptic states. The application of this method, in combination with LOSO CV, demonstrated significant performance, ranging from 90% to 99%. Considerations for real-world clinical implementation, including the development of user-friendly tools and validation in clinical settings, are vital for the practical application of this methodology. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Author**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{\begin{tabular}{c} **Cross-** \\ **Validation** \\ \end{tabular} } & \multicolumn{4}{c}{**Performance (\%)**} \\ \cline{5-8}  & & & & **Acc** & **Pre** & **Rec** & **F1** \\ \hline [PERSON] et al. 2010 [38] & Yule-Walker & SVM \({}^{\ddagger}\) & - & 93.3 & - & - & - \\ \hline [PERSON] et al. 2009 [39] & \begin{tabular}{c} Non-linear \\ measures \\ \end{tabular} & GMM \({}^{\ddagger}\) & - & 96.1 & - & - & - \\ \hline [PERSON] et al. 2013 [40] & \begin{tabular}{c} DWT Frequency \\ Bands \\ \end{tabular} & SVM \({}^{\ddagger}\) & - & 96.0 & - & - & - \\ \hline [PERSON] et al. 2017 [47] & \begin{tabular}{c} Flexible \\ WT/Fractal \\ Dimension \\ \end{tabular} & SVM & 10-Fold & 99.2 & - & - & - \\ \hline [PERSON] et al. 2018 [13] & 1D EEG Features & CNN & 10-Fold & 88.7 & - & - & - \\ \hline [PERSON] et al. 2023 [14] & Spectrogram/Delta & CNN & 10-Fold & 97.0 & 97.14-97.18 \({}^{\ast}\) & 96.00-97.99 \({}^{\ast}\) & 96.41–97.52 \({}^{\ast}\) \\ \hline [PERSON] et al. 2021 [15] & CWT & CNN & Split 10/90 & 98.46 & - & - & - \\ \hline [PERSON] et al. 2019 [43] & TD & RNN/LSTM & 3/5/10-Fold & 100 & - & - & - \\ \hline [PERSON] et al. 2023 [44] & DWT & SONN & Split 30/70 & 99.2 & 98 & 100 & 98.99 \\ \hline [PERSON] et al. 2022 [45] & TD & Epileptic-Net & 10-Fold & 99.95–99.98 \({}^{\ast}\) & - & - & - \\ \hline [PERSON] et al. 2017 [42] & CWT Scalogram & GPCA/SDAE & Split 80/20 & 100 & 100 & 100 & 100 \\ \hline [PERSON] et al. 2018 [12] & TD & P-1D-CNN & 10-Fold & 97.4–100 \({}^{\ast}\) & - & - & - \\ \hline [PERSON] et al. 2011 [49] & \
\begin{tabular}{c} Genetic \\ Programming \\ \end{tabular} & KNN & Split 60/40 & 93.5 & - & - & - \\ \hline [PERSON] et al. 2017 [46] & EWT & RF & 10-Fold & 99.4 & - & - & - \\ \hline [PERSON] et al. 2018 [41] & DWT Entropy & SVM & Split 50/50 & 78–100 \({}^{\ast}\) & - & - & - \\ \hline [PERSON] et al. 2023 [48] & Recurrence Plots & SVM & - & 98.21 & 99.61 & - & - \\ \hline [PERSON] et al. 2020 [50] &
\begin{tabular}{c} Entropy, \\ non-linear, and \\ spectra \\ \end{tabular} & SCANN & 10-Fold & 98.5 & - & - & - \\ \hline
**Proposed Approach** & **VFCDM** & **CNN** & **LOSO CV** & **90–99 \({}^{\ast}\)** & **96–99 \({}^{\ast}\)** & **94–99 \({}^{\ast}\)** & **93–99 \({}^{\ast}\)** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of various studies related to the automatic detection of epileptic classes using the Bonn University database of EEG signals. Author Contributions:Y.R.V., formal analysis, investigation, methodology, resources, software, validation, visualization, writing--original draft preparation; R.M., methodology, writing--original draft preparation, validation, visualization; H.F.P.-Q., conceptualization, supervision, formal analysis, investigation, methodology, validation, visualization, writing--review and editing. All authors have read and agreed to the published version of the manuscript. This research received no external funding. As the data were pre-existing and publicly accessible, ethical review and approval were not necessary for this study.