## 5 Conclusions and Outlook  
In this paper, a multi-network feature-fusion framework integrating five complex network transformation methods (the VG, the HVG, the LPVG, MFDM, and the QG) has been proposed for epileptic EEG classification. The VG and the HVG capture local temporal patterns, the QG quantifies long-range transition properties, and MFDM enhances sensitivity to amplitude distributions. This fusion overcomes the limitations of single-algorithm approaches. The experimental results demonstrate that the proposed framework achieves an accuracy of 97.8% and a specificity of 97.5% in binary classification tasks, with a balanced accuracy of 97.8% and an F1 score of 0.982. These results highlight the model's ability to effectively distinguish between healthy and epileptic EEG signals. The Matthews correlation coefficient and Cohen's kappa coefficient, both at 0.954, further confirm the model's superior predictive quality compared to random guessing. In the more complex five-class classification task, the framework achieves an accuracy of 82.4% and a specificity of 95.6%. The balanced accuracy of 82.4% and an F1 score of 0.823 indicate that the model maintains a good balance between precision and recall across multiple classes. The [PERSON] correlation coefficient and [PERSON]'s Kappa coefficient, both at 0.780, demonstrate the model's effectiveness in handling multi-class classification. The proposed framework exhibits robust performance in both binary and five-class classification tasks, demonstrating high accuracy and specificity and thereby validating its effectiveness. The framework eliminates the need for complex signal preprocessing or deep learning infrastructure, making it well-suited for resource-constrained medical environments while providing real-time diagnostic support for clinicians. Additionally, its efficient implementation reduces computational demands. However, there are also some limitations. For example, the amount of data in this paper is not large, and the data are only verified on classic datasets. Future studies will expand the experiment by incorporating multi-center clinical data (such as the CHB-MIT dataset). Since the method of this paper requires converting the time series into a network before calculating the feature classification, it is more applicable to time series with a length of less than \(10,000\). The framework of this paper can be used to obtain results conveniently and quickly, but if the amount of data is too large, the calculation may be slower. Therefore, for large-scale EEG data, we can use deep learning models for classification in the future. For example, converting time series data into images for the training of convolutional neural networks (CNNs) is a promising way to improve classification performance. Other methods for converting time series into network graphs can also be explored, and then graph neural network (GNN)-based methods can be used to effectively mine topological features from the network representation of time series data for classification. In addition, it is also possible to consider integrating time-frequency imaging techniques, such as continuous wavelet transform, which can further improve classification performance by capturing more discriminative features, which are essential for accurate diagnosis of epilepsy. Although the proposed framework does not require complex signal preprocessing or deep learning infrastructure, making it suitable for resource-limited settings, its future adaptability may include real-time epilepsy detection in clinical settings. Further validation using real-world data, especially integrating the framework with wearable EEG devices, will enhance its practicality and solidify its role as a reliable tool for providing real-time diagnostic support to clinicians. In summary, while the proposed framework exhibits strong performance and practicality, several opportunities remain to expand its applicability.