## 6 DL Models with Spectral-Domain Input
(2020) implemented this approach using 30 s EEG segments with 50% overlap, normalizing the frequency axis to prevent bias from low-frequency dominance. Their CNN-based model achieved 81.4% sensitivity and a false prediction rate (FPR) of 0.06 per hour. In a similar context, [PERSON] et al. (2019) designed a 3D CNN by stacking STFT-transformed EEG data into a three-dimensional tensor, where each axis corresponded to time, frequency, and EEG channels. By integrating dilated convolutions, they enhanced the model's temporal perception and reported 85.8% sensitivity and 80.5% accuracy on the CHB-MIT dataset. [PERSON] et al. (2019) expanded on conventional spectrogram use by combining them with spectral descriptors, such as K-means clustering and local ternary patterns. Their approach, evaluated on the Bonn dataset, achieved the perfect classification of healthy versus epileptic EEG signals. [PERSON] et al. (2019) employed STFT for initial feature extraction, followed by a hybrid CNN-SVM classification model, achieving 92.7% sensitivity and 90.8% specificity. In a related effort, [PERSON] et al. (2019) proposed the Mean Amplitude Spectrum (MAS) as an alternative spectral descriptor, generating MAS maps for each EEG channel and applying them to a CNN-SVM framework, which reached 86.25% accuracy on CHB-MIT recordings. Incorporating self-attention and residual mechanisms has shown further promise in spectral learning. [PERSON] et al. (2019) developed a dual self-attentive residual CNN trained on STFT-based spectrograms and reported 92.07% accuracy, 89.33% sensitivity, and 93.02% specificity across 13 patients in the CHB-MIT dataset. [PERSON] et al. (2019) proposed a multi-view CNN architecture that combined both temporal and spectral features, achieving 93% sensitivity and 71% specificity. [PERSON] and [PERSON] (2019) introduced a CNN-LSTM hybrid  
Figure 3: Deep learning models for seizure prediction using spectral-domain EEG features. trained on spectrogram images, which achieved 94.5% accuracy and an F1-score of 0.9376, with an FPR of 0.055 per hour, across various prediction intervals. Further innovations have leveraged spectral connectivity features. [PERSON] et al. [75] used the Directed Transfer Function (DTF) to measure frequency-domain causal flow between iEEG channels, transforming these relationships into channel-frequency maps processed by CNNs. The model achieved 90.8% sensitivity with an FPR of 0.08 per hour. [PERSON] et al. [76] applied Empirical Ensemble Mode Decomposition (EEMD) in conjunction with neural networks to isolate relevant frequency components, reporting 86.7% sensitivity and 89.5% specificity using 23 CHB-MIT subjects. [PERSON] et al. [77] introduced a hybrid 3D-2D CNN (HyCNN) that preserved spectral depth while optimizing spatial representation, achieving 98.43% accuracy, 98.58% sensitivity, and 96.86% specificity. Recent advancements have explored the potential of Transformer-based architectures for modeling long-range spectral-temporal dependencies [39; 40; 72; 74; 77; 78; 79; 80; 81; 82; 83]. [PERSON] et al. [81] proposed EpilepsyNet, a Transformer model that used Pearson Correlation Coefficients (PCCs) as input features, achieving an accuracy of 85%. [PERSON] et al. [80] built upon this model by combining Transformer layers with LSTM and GRU modules, which yielded a sensitivity of 98.24% on the CHB-MIT dataset. These models underscore the superiority of attention-based mechanisms in modeling complex dependencies beyond the scope of CNNs alone. [PERSON] et al. [84] further enhanced this direction by integrating STFT inputs into a Transformer-guided CNN (TGCNN), which achieved 91.5% sensitivity, 93.5% AUC, and an FPR of 0.145 per hour. [PERSON] et al. [79] incorporated a Stability Index (SI) metric alongside STFT features and trained a CNN classifier, achieving accuracies between 90.1% and 94.5%, depending on whether the preictal interval was 30 or 60 min. Lightweight models leveraging spectral features have also shown great potential for real-time and mobile healthcare environments.