## 6 DL Models with Spectral-Domain Input  
Spectral-domain features play a critical role in capturing frequency-specific patterns that characterize epileptic EEG signals [59, 60, 61, 62, 63, 64, 65, 66, 67]. These features enable models to detect and interpret abnormal neural oscillations commonly associated with seizure activity. EEG recordings are often decomposed into standard frequency bands--Delta (0.5-4 Hz), Theta (4-7 Hz), Alpha (8-12 Hz), Sigma (12-16 Hz), and Beta (13-30 Hz)--to isolate the relevant rhythms that may signal the onset of a seizure [68, 69], as illustrated in Figure 3. Subdividing these bands can enhance discrimination accuracy. For instance, [PERSON] et al. [70] proposed a refined beta sub-band to improve classification resolution, while [PERSON] [71] demonstrated that low-frequency bands, particularly those below 7 Hz, achieved a seizure classification accuracy of 90%. \begin{table}
\begin{tabular}{l l l l l} \hline
**Ref** & **Temporal Feature Representation** & **Model Type** & **Dataset** & **Performance** \\ \hline [PERSON] et al. [36] & Wavelet-decomposed CSP Time Features & CNN & EEG & \begin{tabular}{l} 90.2\% sensitivity, \\ 0.096/h FPR \\ \end{tabular} \\ \hline [PERSON] et al. [31] & Scale-Dependent Lyapunov Exponents (SDLE) & SVM, Random Forest & EEG & \begin{tabular}{l} 92.2\% sensitivity, \\ 0.12/h FPR \\ \end{tabular} \\ \hline [PERSON] [32] & Correlation Dimension (CD) & Simplified Classifier & EEG & Accuracy 100\% \\ \hline [PERSON] et al. [33] & CCA-based features & MLPNN & EEG & Accuracy 92.583\% \\ \hline [PERSON] et al. [34] & IQR, Kruskal-Wallis & Supervised ML & EEG & - \\ \hline [PERSON] et al. [35] & mRMR, mutual info & ML classifiers & \begin{tabular}{l} Multimodal \\ EEG \\ \end{tabular} & Accuracy 95\% \\ \hline [PERSON] et al. [37] & Raw EEG (Temporal Modeling) & 3D-CNN & EEG & Accuracy \textgreater{} 90\% \\ \hline [PERSON] et al. [38] & Temporal Autoencoding & Sparse Autoencoder & EEG & Error rate 22.93\% \\ \hline [PERSON] et al. [39] & Temporal Dependency & CNN-LSTM, Self-attention & EEG & Accuracy 92.07\% \\ \hline [PERSON] et al. [56] & Time-domain EEG & PLS Regression & EEG & Sensitivity \textgreater{} 99\% \\ \hline [PERSON] et al. [54] & Temporal HFOs & Kruskal-Wallis test & EEG & - \\ \hline [PERSON] et al. [55] & IED classification & DL Temporal Models & EEG & Accuracy 95.1\% \\ \hline [PERSON] et al. [57], & Peak-to-peak, Variance, IQR, Energy & SVM, DT, KNN, RF & Bonn, Senthil & \begin{tabular}{l} Alert 75 min before \\ seizure \\ \end{tabular} \\ \hline [PERSON] et al. [49], & Temporal-Spatial Features & GCN, STGAT & \begin{tabular}{l} Multichannel \\ EEG \\ \end{tabular} & Sensitivity 98.5\% \\ \hline [PERSON] et al. [51], & Time-domain Connectivity & Directed Graph Models & EEG & Accuracy 98.15\% \\ \hline [PERSON] et al. [42] & Real-time EEG Monitoring & Cloud/Edge DL & EEG & Accuracy 93.4\% \\ \hline [PERSON] et al. [43] & Real-time EEG & Federated, TSK-Fuzzy & EEG & Accuracy 91.43\% \\ \hline [PERSON] et al. [44] & Neonatal EEG Temporal & LMA-EEGNet & \
\begin{tabular}{l} EEG \\ (Neonatal) \\ \end{tabular} & Accuracy 95.71\% \\ \hline [PERSON] et al. [45], & Mean amplitude, std, median, kurtosis, skewness of 0-40 Hz WPD & CEJT Transfer &
\begin{tabular}{l} Cross-subject \\ EEG \\ \end{tabular} & Accuracy 86.31\% \\ \hline [PERSON] et al. [47] & Neural Mass Time-Domain & Neural Models & EEG & 1 hr before seizure \\ \hline [PERSON] et al. [48] & PSO, Correlation & Optimized ML Models & EEG & Accuracy 99.32\% \\ \hline [PERSON] et al. [40] & Temporal Transformers & EpilepsyNet & EEG & Accuracy 85\% \\ \hline [PERSON] et al. [41] & EEG Variability & EfficientNet-B0 + SVM & EEG & Accuracy 96.12\% \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._To convert EEG signals into a format suitable for spectral analysis, many studies have applied STFT, which yields time-frequency spectrograms. These spectrograms are commonly used as inputs to CNNs, which can capture localized spectral features across temporal windows. [PERSON] et al. (2019); [PERSON] et al.