## 5 ML Model with Time-Domain Input  
Initial studies emphasized handcrafted temporal features such as peak-to-peak amplitude, duration, and spike frequency for seizure prediction [22; 23; 24; 25]. These descriptors were often dataset-specific and lacked robustness across individuals. [PERSON] et al. [26] demonstrated the utility of Approximate Entropy (ApEn) with Elman and Probabilistic Neural Networks, achieving perfect classification accuracy on selected datasets. [PERSON] et al. [27] extracted energy-based metrics (SEN, IQR, MAD) and achieved 84.2% accuracy using statistical classifiers on CHB-MIT. [PERSON] et al. [28] introduced Intrinsic Time-Scale Decomposition (ITD) to compute the sample entropy and fractal dimension, reaching 99% sensitivity and 99.5% specificity. Enhancements to entropy were proposed by [PERSON] et al. [29], who developed Weighted Permutation Entropy (WPE), incorporating both temporal order and amplitude. [PERSON] et al. [30] used a 6 s EEG window to extract statistical features like skewness, kurtosis, and RMS, reporting 90.2% sensitivity with a low false prediction rate. [PERSON] et al. [31] employed Scale-Dependent Lyapunov Exponents (SDLEs) with SVM and Random Forest classifiers, achieving 92.2% sensitivity and predicting seizures with up to a 0.12/h false positive rate. [PERSON] [32] used a simplified classifier based on Correlation Dimension (CD), maintaining high accuracy with minimal computational load. [PERSON] et al. [33] utilized Canonical Correlation Analysis (CCA)-based features in a multilayer perceptron neural network (MLPNN), achieving 92.6% accuracy. Figure 2: Overview of ML and DL pipelines for EEG-based seizure prediction. Temporal and spectral features are extracted from EEG signals. ML models rely on handcrafted features, while DL models automatically learn representations through convolution and pooling layers. Spectral features are visualized as time-frequency maps (color-coded by power, where blue to red scale indicates lower to higher power). DL representations are shown as feature maps with color gradients (from blue to red), representing the progression from low to high feature activation. The classification outputs include normal, pre-ictal, and ictal EEG states. Other works applied statistical distributions and selection methods for robustness. [PERSON] et al. [34] evaluated Kruskal-Wallis and IQR features, while [PERSON] et al. [35] incorporated mutual information and minimum redundancy--maximum relevance (mRMR) techniques in multimodal EEG contexts, achieving 95% accuracy. With increasing computational power, raw EEG inputs have been used directly in DL models. [PERSON] et al. [36] applied wavelet-decomposed Common Spatial Pattern (CSP) features to CNNs, achieving 90.2% sensitivity and low false prediction rates. [PERSON] et al. [37] used 3D CNNs to learn spatial-temporal EEG structures, surpassing 90% accuracy, while [PERSON] et al. [38] implemented temporal autoencoders to reduce EEG into compressed embeddings with a 22.93% error rate. [PERSON] et al. [39] reported 92.07% accuracy using a combination of CNN, LSTM, and self-attention. Transformer-based models such as EpilepsyNet [40] integrated temporal attention while reducing computational complexity (85% accuracy). [PERSON] et al. [41] combined EfficientNet-B0 with an SVM to tackle inter-subject variability and achieved 96.12% accuracy, demonstrating the power of DL-based feature fusion. Real-time EEG prediction frameworks are gaining traction. [PERSON] et al. [42] applied cloud- and edge-based DL pipelines to achieve 93.4% accuracy. [PERSON] et al. [43] maintained 91.4% accuracy in real-time contexts using federated learning and privacy-aware fuzzy models. [PERSON] et al. [44] optimized LMA-EEGNet for neonatal seizure detection, delivering 95.7% accuracy. Several studies have focused on generalized modeling across subjects and sessions. [PERSON] et al. [45] and [PERSON] et al.