## 4 Discussion
It accurately classified both simple and complex combinations of states while maintaining a balance between true positives and negatives. The highest performance (99%) was achieved when classifying healthy (Z) and seizure states. The model adapted well to complex differences within these state combinations, thus effectively managing variations in Acc and loss metrics. Previous studies have reported various methods for automated epilepsy detection using the Bonn dataset. Numerous studies have addressed epilepsy detection, employing diverse methodologies and classifiers. Table 4 provides a comparison of our proposed work with the state-of-the-art methods. [PERSON] et al. (2010) [PERSON] et al. (2010) employed the Yule-Walker method and an SVM, yielding an Acc of 93.3%. Similarly, [PERSON] et al. (2009) [PERSON] et al. (2009) ([PERSON] et al., 2010) computed non-linear measures, such as correlation and fractal dimension, and the Hurst and Lyapunov exponent. These features were fed into the GMM classifier and achieved an accuracy of 96.1%. These findings emphasize the potential of advanced methods and classifiers for precise epilepsy diagnosis. Researchers such as [PERSON] et al. (2013) [40] have focused on signal-processing methods. They employed DWT frequency band features and SVM for epilepsy detection, maintaining a high accuracy of 96.0%. Additionally, [PERSON] et al. (2018) [41] used DWT entropy features with an SVM classifier, highlighting the significance of exploring various signal-processing techniques for improved classification. The emergence of deep learning approaches in the field of EEG analysis is evident in the studies by [PERSON] et al. (2018) [13] and [PERSON] et al. (2023) [14]. While [PERSON] et al. [13] achieved an accuracy of 88.7% using 1D EEG features and a CNN, [PERSON] et al. [14] adopted spectrogram analysis with a CNN to attain an accuracy of 97.0%. These studies showcase the potential of deep learning models in EEG analysis and the advantages they offer in terms of accuracy. Further, the introduction of innovative feature extraction methods has significantly influenced epilepsy detection. [PERSON] et al. (2021) [15] employed CWT with a CNN classifier, achieving an accuracy of 98.46%. Their research highlights the potential of wavelet-based features when coupled with CNN models. Additionally, [PERSON] et al. (2017) [42] applied CWT scalogram analysis with GPCA and SDAE, achieving absolute accuracy and precision. These studies illustrate the ability of advanced feature extraction techniques. Some studies have also used neural network architectures. [PERSON] et al. (2019) [43] introduced TD features and RNN/LSTM for epilepsy detection, achieving an accuracy of 100%. Meanwhile, [PERSON] et al. (2023) [44] used DWT with SONN, resulting in an accuracy of 99.2%. These studies emphasized the potential of neural networks in EEG analysis. Recent advancements in deep learning are reflected in studies such as that of [PERSON] et al. (2022) [45], who explored TD features with the Epileptic-Net model, achieving an accuracy ranging from 99.95% to 99.98%. Further, [PERSON] et al. (2017) [46] examined EWT features with RF, reaching an accuracy of 99.4%. This highlights the need for a multimodal approach to enhance diagnostic accuracy. Additionally, [PERSON] et al. (2017) [47] employed flexible WT and fractal dimension with SVM, achieving an accuracy of 99.2%. Their research shows the potential of combining multiple techniques for enhanced results. Further, [PERSON] et al. (2023) [48] introduced recurrence plots with an SVM classifier, achieving an accuracy of 98.21% and a precision rate of 99.61%. These studies showcase the potential of unique approaches in feature extraction and classification. Building upon the foundations laid by previous works, the proposed approach in this study, which used VFCDM and CNN for automated epilepsy detection, demonstrated promising performance, with accuracy ranging from 90% to 99% across various classification scenarios.