## 7 Discussion
### Impact of Feature Representation on Prediction Performance  
Temporal features such as signal amplitude, entropy measures, and waveform variability have historically underpinned seizure classification in ML pipelines using models like SVM and RF [31, 32]. These features are computationally efficient and often interpretable, making them suitable for real-time applications. However, their capacity to model the nonlinear dynamics and abrupt transitions characteristic of epileptic seizures is limited. While methods employing Approximate Entropy [26] and Scale-Dependent Lyapunov Exponents [31] have demonstrated encouraging results in distinguishing preictal from \begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref** & **Feature Representation** & **Model Type** & **Dataset** & **Performance Metrics** \\ \hline [PERSON] et al. [59, 64] & STFT (spectrogram) & CNN & CHB-MIT & 81.4\% Sensitivity, FPR 0.06/h \\ \hline [PERSON] et al. [67] & STFT (3D Tensor) & 3D CNN with Dilated Conv & CHB-MIT & 85.8\% Sensitivity, 80.5\% Accuracy \\ \hline [PERSON] et al. [66] & Spectrogram + Descriptors & MLP, K-means, LTP & Bonn & 100\% Accuracy \\ \hline [PERSON] et al. [72] & STFT & CNN-SVM & CHB-MIT & 92.7\% Sensitivity, 90.8\% Specificity \\ \hline [PERSON] et al. [65] & Mean Amplitude Spectrum (MAS) & CNN-SVM & CHB-MIT & 86.25\% Accuracy \\ \hline [PERSON] et al. [39] & STFT & Self-attentive Residual CNN & CHB-MIT & 92.07\% Accuracy, 89.33\% Sensitivity, 93.02\% Specificity \\ \hline [PERSON] et al. [73] & Temporal + Spectral & Multi-view CNN & CHB-MIT & 93\% Sensitivity, 71\% Specificity \\ \hline [PERSON] \& [PERSON] [74] & Spectrogram & CNN-LSTM & CHB-MIT & 94.5\% Accuracy, F1-score 0.9376, FPR \\ \hline [PERSON] et al. [75] & DTF (Spectral Flow) & CNN + Moving Avg & iEEG & 90.8\% Sensitivity, FPR 0.08/h \\ \hline [PERSON] et al. [76] & EEMD & Neural Networks & CHB-MIT & 86.7\% Sensitivity, 89.5\% Specificity \\ \hline [PERSON] et al. [77] & Spectral Depth & 3D+2D HyCNN & CHB-MIT & 98.43\% Accuracy, 98.58\% Sensitivity, 96.86\% Specificity \\ \hline [PERSON] et al. [40] & PCC & Transformer & EEG & 85\% Accuracy \\ \hline [PERSON] et al. [80] & Spectral Features & Transformer + LSTM/GRU & CHB-MIT & 98.24\% Sensitivity \\ \hline [PERSON] et al. [84] & STFT & TGCNN & CHB-MIT & 91.5\% Sensitivity, AUC 93.5\%, FPR 0.145/h \\ \hline [PERSON] et al. [79] & STFT + SI & CNN & CHB-MIT & Accuracy 90.1\%–94.5\% \\ \hline [PERSON] et al. [81] & Entropy Spectral Features & SVM, Decision Trees & EEG & High Accuracy, Low Data Requirements \\ \hline [PERSON] et al. [82] & Alpha/Beta Bands & SVM & EEG & High Accuracy, Real-Time Suitability \\ \hline [PERSON] \& [PERSON] [83] & FBFT & CNN & EEG & High Accuracy \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of performance of DL models with spectral-domain input. ictal phases, their generalization to diverse patient populations and recording conditions remains suboptimal. Spectral-domain features have emerged as a more expressive alternative, offering deeper insight into seizure-related frequency anomalies. Time-frequency decomposition techniques such as STFT, wavelet transform, and EEMD have been extensively adopted to convert EEG signals into rich spectral representations. These inputs enable CNNs to extract hierarchical features that encode both spatial and frequency-related seizure dynamics [59; 60; 76]. CNNs trained on STFT-based spectrograms, for example, have demonstrated superior performance, with some achieving over 92% sensitivity and 90% specificity in detecting ictal activity [72]. Hybrid and attention-based architectures have further improved spectral learning. The Transformer-based model EpilepsyNet [40], which integrates Pearson correlation coefficient features into self-attention mechanisms, achieved robust results across varying seizure contexts. Building on this, [PERSON] et al.