## 4 Discussion  
The accurate and timely detection of epileptic seizures remains a critical challenge in clinical neurology. While scalp EEG has long been the gold standard for seizure monitoring,  
Figure 10: The (**a**) training and validation metrics and (**b**) normalized confusion matrix of CNN in classifying healthy vs. interictal vs. ictal states. Figure 9: Radar plot representation of the CNN performance metrics for classifying healthy vs. interictal vs. ictal states. manual interpretation of EEG data is both time-consuming and prone to human error. Automated seizure detection systems that can provide real-time alerts have the potential to significantly improve patient care by enabling timely interventions and reducing the risk of seizure-related complications. They also have the potential to reduce the burden on healthcare professionals and enhance the overall management of epilepsy. This study investigated the application of VFCDM and CNN approaches in epilepsy detection using EEG signals. The focus was on exploring the possibility of developing an automated system for differentiating healthy, interictal, and ictal states. The VFCDM time-frequency spectrum that accounts for non-linear and non-stationary variations of EEG signals was used to develop the system. The results of the proposed method showcased high accuracy and the ability to manage both simple and complex classification scenarios. In this study, the Bonn dataset was specifically selected due to its robustness and extensive use in epilepsy research. Its publicly available nature promotes reproducibility and facilitates comparisons with previous studies. The dataset includes subsets representing healthy, interictal, and ictal states. It provides intricate neural activity patterns, making it well suited for multi-class classification tasks. To ensure the integrity and high quality of the data, rigorous preprocessing steps, including band-pass filtering and artifact removal, were implemented. The partitioning of the dataset into training and validation sets was carried out with the utmost care and consideration, using stratification techniques to ensure unbiased training and evaluation of the models. These deliberate choices profoundly influenced the results of the study, yielding valuable insights into the dynamics of epilepsy and significantly impacting the efficacy of learning-based methods. EEG signals exhibit distinct characteristics that differentiate between healthy and epilepsy states. In healthy individuals, EEG signals show low amplitude and stable neural activity, reflecting a consistent and balanced level of neuronal excitability. In contrast, interictal states, which are the EEG patterns observed between seizures, show increased amplitude and pronounced fluctuations, suggesting greater neuronal instability, often associated with epilepsy or pre-seizure conditions. Ictal states, the EEG patterns observed during seizures, exhibit an intermediate range of amplitude values but are characterized by intense amplitude fluctuations. These distinct EEG signal characteristics form the basis for effective seizure detection and classification. The analysis of the TFS of EEG signals using VFCDM further emphasized these distinctions. Healthy states demonstrated moderate neural activity with a mean frequency range of around 14.47 Hz. These states also exhibited stable dynamics. Interictal states exhibited lower neural activity with a mean frequency range of around 10 Hz. A significant shift toward higher frequencies was also observed in these states. Ictal states yielded substantially higher mean frequency ranges and intense neural activity. This demonstrates that there is a wide range of frequency activity during seizures. These TFS characteristics align with the amplitude and fluctuation patterns observed in EEG signals. The CNN model exhibited significant classification performance. The model consistently demonstrated high performance for all three cases considered in the study.